From 45a657c24f8d7b17bd83076b7a02cfd04e4928d0 Mon Sep 17 00:00:00 2001 From: PatReis Date: Sat, 2 Dec 2023 15:47:51 +0100 Subject: [PATCH] update CMPNN layers and update notebooks. --- docs/source/models.ipynb | 2 +- kgcnn/literature/CMPNN/_layers.py | 46 +- notebooks/workflow_molecule_regression.ipynb | 552 ++++------- notebooks/workflow_qm_regression.ipynb | 926 +------------------ 4 files changed, 260 insertions(+), 1266 deletions(-) diff --git a/docs/source/models.ipynb b/docs/source/models.ipynb index 5821224d..af86366e 100644 --- a/docs/source/models.ipynb +++ b/docs/source/models.ipynb @@ -564,7 +564,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.5" } }, "nbformat": 4, diff --git a/kgcnn/literature/CMPNN/_layers.py b/kgcnn/literature/CMPNN/_layers.py index 88b15d7e..c9acd2de 100644 --- a/kgcnn/literature/CMPNN/_layers.py +++ b/kgcnn/literature/CMPNN/_layers.py @@ -4,11 +4,38 @@ class PoolingNodesGRU(Layer): - def __init__(self, units, **kwargs): + def __init__(self, units, static_output_shape=None, + activation='tanh', recurrent_activation='sigmoid', + use_bias=True, kernel_initializer='glorot_uniform', + recurrent_initializer='orthogonal', + bias_initializer='zeros', kernel_regularizer=None, + recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, + recurrent_constraint=None, bias_constraint=None, dropout=0.0, + recurrent_dropout=0.0, reset_after=True, seed=None, + **kwargs): super(PoolingNodesGRU, self).__init__(**kwargs) self.units = units - self.cast_layer = CastDisjointToBatchedAttributes(return_mask=True) - self.gru = GRU(units=units) + self.cast_layer = CastDisjointToBatchedAttributes( + static_output_shape=static_output_shape, return_mask=True) + self.gru = GRU( + units=units, + activation=activation, + recurrent_activation=recurrent_activation, + use_bias=use_bias, + kernel_initializer=kernel_initializer, + recurrent_initializer=recurrent_initializer, + bias_initializer=bias_initializer, + kernel_regularizer=kernel_regularizer, + recurrent_regularizer=recurrent_regularizer, + bias_regularizer=bias_regularizer, + kernel_constraint=kernel_constraint, + recurrent_constraint=recurrent_constraint, + bias_constraint=bias_constraint, + dropout=dropout, + recurrent_dropout=recurrent_dropout, + reset_after=reset_after, + seed=seed + ) def call(self, inputs, **kwargs): n, mask = self.cast_layer(inputs) @@ -17,5 +44,16 @@ def call(self, inputs, **kwargs): def get_config(self): config = super(PoolingNodesGRU, self).get_config() - config.update({"units": self.units}) + config.update({"units": self.units, "static_output_shape": self.static_output_shape}) + conf_gru = self.gru_cell.get_config() + param_list = ["units", "activation", "recurrent_activation", + "use_bias", "kernel_initializer", + "recurrent_initializer", + "bias_initializer", "kernel_regularizer", + "recurrent_regularizer", "bias_regularizer", "kernel_constraint", + "recurrent_constraint", "bias_constraint", "dropout", + "recurrent_dropout", "reset_after"] + for x in param_list: + if x in conf_gru.keys(): + config.update({x: conf_gru[x]}) return config diff --git a/notebooks/workflow_molecule_regression.ipynb b/notebooks/workflow_molecule_regression.ipynb index 31e98690..41c7f296 100644 --- a/notebooks/workflow_molecule_regression.ipynb +++ b/notebooks/workflow_molecule_regression.ipynb @@ -13,17 +13,9 @@ "execution_count": 1, "id": "d925ab75", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using PyTorch backend.\n" - ] - } - ], + "outputs": [], "source": [ - "import keras_core as ks" + "import keras as ks" ] }, { @@ -148,7 +140,8 @@ "from kgcnn.graph.preprocessor import SetRange, SetEdgeIndicesReverse\n", "data.map_list(SetRange(max_distance=5.0, in_place=True));\n", "data.map_list(SetEdgeIndicesReverse(in_place=True));\n", - "data.map_list(method=\"count_nodes_and_edges\");" + "data.map_list(method=\"count_nodes_and_edges\");\n", + "data.map_list(**{\"method\": \"count_nodes_and_edges\", \"total_edges\": \"total_reverse\"});" ] }, { @@ -160,7 +153,7 @@ { "data": { "text/plain": [ - "dict_keys(['node_symbol', 'node_number', 'edge_indices', 'edge_number', 'graph_size', 'node_coordinates', 'graph_labels', 'node_attributes', 'edge_attributes', 'graph_attributes', 'range_indices', 'range_attributes', 'edge_indices_reverse', 'total_nodes', 'total_edges'])" + "dict_keys(['node_symbol', 'node_number', 'edge_indices', 'edge_number', 'graph_size', 'node_coordinates', 'graph_labels', 'node_attributes', 'edge_attributes', 'graph_attributes', 'range_indices', 'range_attributes', 'edge_indices_reverse', 'total_nodes', 'total_edges', 'total_reverse'])" ] }, "execution_count": 8, @@ -237,7 +230,8 @@ " {\"shape\": (None, 2), \"name\": \"edge_indices\", \"dtype\": \"int64\"},\n", " {\"shape\": (None, 1), \"name\": \"edge_indices_reverse\", \"dtype\": \"int64\"},\n", " {\"shape\": (), \"name\": \"total_nodes\", \"dtype\": \"int64\"},\n", - " {\"shape\": (), \"name\": \"total_edges\", \"dtype\": \"int64\"}\n", + " {\"shape\": (), \"name\": \"total_edges\", \"dtype\": \"int64\"},\n", + " {\"shape\": (), \"name\": \"total_reverse\", \"dtype\": \"int64\"}\n", " ],\n", " \"cast_disjoint_kwargs\": {},\n", " \"input_node_embedding\": {\"input_dim\": 95, \"output_dim\": 64},\n", @@ -304,9 +298,12 @@ "│ │ (None), (None), (None)] │ │ total_nodes[0][0], │\n", "│ │ │ │ total_edges[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ cast_batched_attributes_to_d… │ [(None, 41), (None), │ 0 │ node_attributes[0][0], │\n", + "│ (CastBatchedAttributesToDisj… │ (None), (None)] │ │ total_nodes[0][0] │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_attributes (InputLayer) │ (None, None, 11) │ 0 │ - │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gather_nodes_outgoing │ (None, 41) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ gather_nodes_outgoing │ (None, 41) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (GatherNodesOutgoing) │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_attributes_to_d… │ [(None, 11), (None), │ 0 │ edge_attributes[0][0], │\n", @@ -318,14 +315,16 @@ "│ edge_indices_reverse │ (None, None, 1) │ 0 │ - │\n", "│ (InputLayer) │ │ │ │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ total_reverse (InputLayer) │ (None) │ 0 │ - │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense (Dense) │ (None, 128) │ 6,784 │ concatenate[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_indices_to_disj… │ [(None, 2), (1, None), │ 0 │ edge_indices[0][0], │\n", "│ (CastBatchedIndicesToDisjoin… │ (None), (None), (None), │ │ edge_indices_reverse[0][0], │\n", - "│ │ (None), (None), (None)] │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ (None), (None), (None)] │ │ total_edges[0][0], │\n", + "│ │ │ │ total_reverse[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directed │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directed │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dense[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -342,7 +341,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout (Dropout) │ (None, 128) │ 0 │ activation[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -353,7 +352,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_1 (Dropout) │ (None, 128) │ 0 │ activation_1[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_1[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -364,7 +363,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_2 (Dropout) │ (None, 128) │ 0 │ activation_2[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_2[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -375,7 +374,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_3 (Dropout) │ (None, 128) │ 0 │ activation_3[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_3[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -386,12 +385,12 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_4 (Dropout) │ (None, 128) │ 0 │ activation_4[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ aggregate_local_edges_5 │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ aggregate_local_edges_5 │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (AggregateLocalEdges) │ │ │ dropout_4[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ concatenate_1 (Concatenate) │ (None, 169) │ 0 │ aggregate_local_edges_5[0][0], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_2 (Dense) │ (None, 128) │ 21,760 │ concatenate_1[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", @@ -401,8 +400,8 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ mlp (MLP) │ (None, 1) │ 10,368 │ pooling_nodes[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state │ (None, 1) │ 0 │ mlp[0][0] │\n", - "│ (CastDisjointToGraphState) │ │ │ │\n", + "│ cast_disjoint_to_batched_gra… │ (None, 1) │ 0 │ mlp[0][0] │\n", + "│ (CastDisjointToBatchedGraphS… │ │ │ │\n", "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n", "\n" ], @@ -423,9 +422,12 @@ "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", + "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_attributes (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gather_nodes_outgoing │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ gather_nodes_outgoing │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mGatherNodesOutgoing\u001b[0m) │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ edge_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", @@ -437,14 +439,16 @@ "│ edge_indices_reverse │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ total_reverse (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m6,784\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m), (\u001b[38;5;34m1\u001b[0m, \u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ │ edge_indices_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", + "│ │ │ │ total_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directed │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directed │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -461,7 +465,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -472,7 +476,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -483,7 +487,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -494,7 +498,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -505,12 +509,12 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ aggregate_local_edges_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ aggregate_local_edges_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mAggregateLocalEdges\u001b[0m) │ │ │ dropout_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ concatenate_1 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m169\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ aggregate_local_edges_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m21,760\u001b[0m │ concatenate_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", @@ -520,8 +524,8 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ mlp (\u001b[38;5;33mMLP\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m10,368\u001b[0m │ pooling_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mlp[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mCastDisjointToGraphState\u001b[0m) │ │ │ │\n", + "│ cast_disjoint_to_batched_gra… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mlp[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "│ (\u001b[38;5;33mCastDisjointToBatchedGraphS…\u001b[0m │ │ │ │\n", "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n" ] }, @@ -572,7 +576,7 @@ "output_type": "stream", "text": [ "None\n", - "Print Time for training: 0:06:17.250000\n", + "Print Time for training: 0:07:42.906250\n", "Running training on fold: 1\n" ] }, @@ -608,28 +612,33 @@ "│ │ (None), (None), (None)] │ │ total_nodes[0][0], │\n", "│ │ │ │ total_edges[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ cast_batched_attributes_to_d… │ [(None, 41), (None), │ 0 │ node_attributes[0][0], │\n", + "│ (CastBatchedAttributesToDisj… │ (None), (None)] │ │ total_nodes[0][0] │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_attributes (InputLayer) │ (None, None, 11) │ 0 │ - │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gather_nodes_outgoing_11 │ (None, 41) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ gather_nodes_outgoing_6 │ (None, 41) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (GatherNodesOutgoing) │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_attributes_to_d… │ [(None, 11), (None), │ 0 │ edge_attributes[0][0], │\n", "│ (CastBatchedAttributesToDisj… │ (None), (None)] │ │ total_edges[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ concatenate_2 (Concatenate) │ (None, 52) │ 0 │ gather_nodes_outgoing_11[0][0… │\n", + "│ concatenate_2 (Concatenate) │ (None, 52) │ 0 │ gather_nodes_outgoing_6[0][0], │\n", "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_indices_reverse │ (None, None, 1) │ 0 │ - │\n", "│ (InputLayer) │ │ │ │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ total_reverse (InputLayer) │ (None) │ 0 │ - │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_3 (Dense) │ (None, 128) │ 6,784 │ concatenate_2[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_indices_to_disj… │ [(None, 2), (1, None), │ 0 │ edge_indices[0][0], │\n", "│ (CastBatchedIndicesToDisjoin… │ (None), (None), (None), │ │ edge_indices_reverse[0][0], │\n", - "│ │ (None), (None), (None)] │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ (None), (None), (None)] │ │ total_edges[0][0], │\n", + "│ │ │ │ total_reverse[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dense_3[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -646,7 +655,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_5 (Dropout) │ (None, 128) │ 0 │ activation_5[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_5[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -657,7 +666,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_6 (Dropout) │ (None, 128) │ 0 │ activation_6[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_6[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -668,7 +677,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_7 (Dropout) │ (None, 128) │ 0 │ activation_7[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_7[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -679,7 +688,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_8 (Dropout) │ (None, 128) │ 0 │ activation_8[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_8[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -690,12 +699,12 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_9 (Dropout) │ (None, 128) │ 0 │ activation_9[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ aggregate_local_edges_11 │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ aggregate_local_edges_11 │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (AggregateLocalEdges) │ │ │ dropout_9[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ concatenate_3 (Concatenate) │ (None, 169) │ 0 │ aggregate_local_edges_11[0][0… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_5 (Dense) │ (None, 128) │ 21,760 │ concatenate_3[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", @@ -705,8 +714,8 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ mlp_1 (MLP) │ (None, 1) │ 10,368 │ pooling_nodes_1[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state… │ (None, 1) │ 0 │ mlp_1[0][0] │\n", - "│ (CastDisjointToGraphState) │ │ │ │\n", + "│ cast_disjoint_to_batched_gra… │ (None, 1) │ 0 │ mlp_1[0][0] │\n", + "│ (CastDisjointToBatchedGraphS… │ │ │ │\n", "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n", "\n" ], @@ -727,28 +736,33 @@ "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", + "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_attributes (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gather_nodes_outgoing_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ gather_nodes_outgoing_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mGatherNodesOutgoing\u001b[0m) │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ edge_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ concatenate_2 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ gather_nodes_outgoing_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", + "│ concatenate_2 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ gather_nodes_outgoing_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_indices_reverse │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ total_reverse (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m6,784\u001b[0m │ concatenate_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m), (\u001b[38;5;34m1\u001b[0m, \u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ │ edge_indices_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", + "│ │ │ │ total_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -765,7 +779,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -776,7 +790,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -787,7 +801,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -798,7 +812,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -809,12 +823,12 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ aggregate_local_edges_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ aggregate_local_edges_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mAggregateLocalEdges\u001b[0m) │ │ │ dropout_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ concatenate_3 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m169\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ aggregate_local_edges_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_5 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m21,760\u001b[0m │ concatenate_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", @@ -824,8 +838,8 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ mlp_1 (\u001b[38;5;33mMLP\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m10,368\u001b[0m │ pooling_nodes_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mlp_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mCastDisjointToGraphState\u001b[0m) │ │ │ │\n", + "│ cast_disjoint_to_batched_gra… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mlp_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "│ (\u001b[38;5;33mCastDisjointToBatchedGraphS…\u001b[0m │ │ │ │\n", "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n" ] }, @@ -876,7 +890,7 @@ "output_type": "stream", "text": [ "None\n", - "Print Time for training: 0:06:26.906250\n", + "Print Time for training: 0:06:26.343750\n", "Running training on fold: 2\n" ] }, @@ -912,28 +926,33 @@ "│ │ (None), (None), (None)] │ │ total_nodes[0][0], │\n", "│ │ │ │ total_edges[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ cast_batched_attributes_to_d… │ [(None, 41), (None), │ 0 │ node_attributes[0][0], │\n", + "│ (CastBatchedAttributesToDisj… │ (None), (None)] │ │ total_nodes[0][0] │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_attributes (InputLayer) │ (None, None, 11) │ 0 │ - │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gather_nodes_outgoing_22 │ (None, 41) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ gather_nodes_outgoing_12 │ (None, 41) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (GatherNodesOutgoing) │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_attributes_to_d… │ [(None, 11), (None), │ 0 │ edge_attributes[0][0], │\n", "│ (CastBatchedAttributesToDisj… │ (None), (None)] │ │ total_edges[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ concatenate_4 (Concatenate) │ (None, 52) │ 0 │ gather_nodes_outgoing_22[0][0… │\n", + "│ concatenate_4 (Concatenate) │ (None, 52) │ 0 │ gather_nodes_outgoing_12[0][0… │\n", "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_indices_reverse │ (None, None, 1) │ 0 │ - │\n", "│ (InputLayer) │ │ │ │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ total_reverse (InputLayer) │ (None) │ 0 │ - │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_6 (Dense) │ (None, 128) │ 6,784 │ concatenate_4[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_indices_to_disj… │ [(None, 2), (1, None), │ 0 │ edge_indices[0][0], │\n", "│ (CastBatchedIndicesToDisjoin… │ (None), (None), (None), │ │ edge_indices_reverse[0][0], │\n", - "│ │ (None), (None), (None)] │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ (None), (None), (None)] │ │ total_edges[0][0], │\n", + "│ │ │ │ total_reverse[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dense_6[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -950,7 +969,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_10 (Dropout) │ (None, 128) │ 0 │ activation_10[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_10[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -961,7 +980,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_11 (Dropout) │ (None, 128) │ 0 │ activation_11[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_11[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -972,7 +991,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_12 (Dropout) │ (None, 128) │ 0 │ activation_12[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_12[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -983,7 +1002,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_13 (Dropout) │ (None, 128) │ 0 │ activation_13[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_13[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -994,12 +1013,12 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_14 (Dropout) │ (None, 128) │ 0 │ activation_14[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ aggregate_local_edges_17 │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ aggregate_local_edges_17 │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (AggregateLocalEdges) │ │ │ dropout_14[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ concatenate_5 (Concatenate) │ (None, 169) │ 0 │ aggregate_local_edges_17[0][0… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_8 (Dense) │ (None, 128) │ 21,760 │ concatenate_5[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", @@ -1009,8 +1028,8 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ mlp_2 (MLP) │ (None, 1) │ 10,368 │ pooling_nodes_2[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state… │ (None, 1) │ 0 │ mlp_2[0][0] │\n", - "│ (CastDisjointToGraphState) │ │ │ │\n", + "│ cast_disjoint_to_batched_gra… │ (None, 1) │ 0 │ mlp_2[0][0] │\n", + "│ (CastDisjointToBatchedGraphS… │ │ │ │\n", "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n", "\n" ], @@ -1031,28 +1050,33 @@ "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", + "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_attributes (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gather_nodes_outgoing_22 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ gather_nodes_outgoing_12 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mGatherNodesOutgoing\u001b[0m) │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ edge_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ concatenate_4 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ gather_nodes_outgoing_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", + "│ concatenate_4 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ gather_nodes_outgoing_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_indices_reverse │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ total_reverse (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_6 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m6,784\u001b[0m │ concatenate_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m), (\u001b[38;5;34m1\u001b[0m, \u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ │ edge_indices_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", + "│ │ │ │ total_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dense_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1069,7 +1093,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_10 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1080,7 +1104,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_11 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1091,7 +1115,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_12 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1102,7 +1126,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_13 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1113,12 +1137,12 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_14 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ aggregate_local_edges_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ aggregate_local_edges_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mAggregateLocalEdges\u001b[0m) │ │ │ dropout_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ concatenate_5 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m169\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ aggregate_local_edges_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_8 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m21,760\u001b[0m │ concatenate_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", @@ -1128,8 +1152,8 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ mlp_2 (\u001b[38;5;33mMLP\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m10,368\u001b[0m │ pooling_nodes_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mlp_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mCastDisjointToGraphState\u001b[0m) │ │ │ │\n", + "│ cast_disjoint_to_batched_gra… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mlp_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "│ (\u001b[38;5;33mCastDisjointToBatchedGraphS…\u001b[0m │ │ │ │\n", "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n" ] }, @@ -1180,7 +1204,7 @@ "output_type": "stream", "text": [ "None\n", - "Print Time for training: 0:06:27.437500\n", + "Print Time for training: 0:07:21.625000\n", "Running training on fold: 3\n" ] }, @@ -1216,28 +1240,33 @@ "│ │ (None), (None), (None)] │ │ total_nodes[0][0], │\n", "│ │ │ │ total_edges[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ cast_batched_attributes_to_d… │ [(None, 41), (None), │ 0 │ node_attributes[0][0], │\n", + "│ (CastBatchedAttributesToDisj… │ (None), (None)] │ │ total_nodes[0][0] │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_attributes (InputLayer) │ (None, None, 11) │ 0 │ - │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gather_nodes_outgoing_33 │ (None, 41) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ gather_nodes_outgoing_18 │ (None, 41) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (GatherNodesOutgoing) │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_attributes_to_d… │ [(None, 11), (None), │ 0 │ edge_attributes[0][0], │\n", "│ (CastBatchedAttributesToDisj… │ (None), (None)] │ │ total_edges[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ concatenate_6 (Concatenate) │ (None, 52) │ 0 │ gather_nodes_outgoing_33[0][0… │\n", + "│ concatenate_6 (Concatenate) │ (None, 52) │ 0 │ gather_nodes_outgoing_18[0][0… │\n", "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_indices_reverse │ (None, None, 1) │ 0 │ - │\n", "│ (InputLayer) │ │ │ │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ total_reverse (InputLayer) │ (None) │ 0 │ - │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_9 (Dense) │ (None, 128) │ 6,784 │ concatenate_6[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_indices_to_disj… │ [(None, 2), (1, None), │ 0 │ edge_indices[0][0], │\n", "│ (CastBatchedIndicesToDisjoin… │ (None), (None), (None), │ │ edge_indices_reverse[0][0], │\n", - "│ │ (None), (None), (None)] │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ (None), (None), (None)] │ │ total_edges[0][0], │\n", + "│ │ │ │ total_reverse[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dense_9[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1254,7 +1283,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_15 (Dropout) │ (None, 128) │ 0 │ activation_15[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_15[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1265,7 +1294,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_16 (Dropout) │ (None, 128) │ 0 │ activation_16[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_16[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1276,7 +1305,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_17 (Dropout) │ (None, 128) │ 0 │ activation_17[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_17[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1287,7 +1316,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_18 (Dropout) │ (None, 128) │ 0 │ activation_18[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_18[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1298,12 +1327,12 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_19 (Dropout) │ (None, 128) │ 0 │ activation_19[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ aggregate_local_edges_23 │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ aggregate_local_edges_23 │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (AggregateLocalEdges) │ │ │ dropout_19[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ concatenate_7 (Concatenate) │ (None, 169) │ 0 │ aggregate_local_edges_23[0][0… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_11 (Dense) │ (None, 128) │ 21,760 │ concatenate_7[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", @@ -1313,8 +1342,8 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ mlp_3 (MLP) │ (None, 1) │ 10,368 │ pooling_nodes_3[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state… │ (None, 1) │ 0 │ mlp_3[0][0] │\n", - "│ (CastDisjointToGraphState) │ │ │ │\n", + "│ cast_disjoint_to_batched_gra… │ (None, 1) │ 0 │ mlp_3[0][0] │\n", + "│ (CastDisjointToBatchedGraphS… │ │ │ │\n", "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n", "\n" ], @@ -1335,28 +1364,33 @@ "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", + "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_attributes (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gather_nodes_outgoing_33 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ gather_nodes_outgoing_18 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mGatherNodesOutgoing\u001b[0m) │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ edge_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ concatenate_6 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ gather_nodes_outgoing_33[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", + "│ concatenate_6 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ gather_nodes_outgoing_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_indices_reverse │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ total_reverse (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_9 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m6,784\u001b[0m │ concatenate_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m), (\u001b[38;5;34m1\u001b[0m, \u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ │ edge_indices_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", + "│ │ │ │ total_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dense_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1373,7 +1407,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_15 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1384,7 +1418,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_16 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1395,7 +1429,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_17 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1406,7 +1440,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_18 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1417,12 +1451,12 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_19 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ aggregate_local_edges_23 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ aggregate_local_edges_23 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mAggregateLocalEdges\u001b[0m) │ │ │ dropout_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ concatenate_7 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m169\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ aggregate_local_edges_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_11 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m21,760\u001b[0m │ concatenate_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", @@ -1432,8 +1466,8 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ mlp_3 (\u001b[38;5;33mMLP\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m10,368\u001b[0m │ pooling_nodes_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mlp_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mCastDisjointToGraphState\u001b[0m) │ │ │ │\n", + "│ cast_disjoint_to_batched_gra… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mlp_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "│ (\u001b[38;5;33mCastDisjointToBatchedGraphS…\u001b[0m │ │ │ │\n", "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n" ] }, @@ -1484,7 +1518,7 @@ "output_type": "stream", "text": [ "None\n", - "Print Time for training: 0:06:16.406250\n", + "Print Time for training: 0:08:37.828125\n", "Running training on fold: 4\n" ] }, @@ -1520,28 +1554,33 @@ "│ │ (None), (None), (None)] │ │ total_nodes[0][0], │\n", "│ │ │ │ total_edges[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ cast_batched_attributes_to_d… │ [(None, 41), (None), │ 0 │ node_attributes[0][0], │\n", + "│ (CastBatchedAttributesToDisj… │ (None), (None)] │ │ total_nodes[0][0] │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_attributes (InputLayer) │ (None, None, 11) │ 0 │ - │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gather_nodes_outgoing_44 │ (None, 41) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ gather_nodes_outgoing_24 │ (None, 41) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (GatherNodesOutgoing) │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_attributes_to_d… │ [(None, 11), (None), │ 0 │ edge_attributes[0][0], │\n", "│ (CastBatchedAttributesToDisj… │ (None), (None)] │ │ total_edges[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ concatenate_8 (Concatenate) │ (None, 52) │ 0 │ gather_nodes_outgoing_44[0][0… │\n", + "│ concatenate_8 (Concatenate) │ (None, 52) │ 0 │ gather_nodes_outgoing_24[0][0… │\n", "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_indices_reverse │ (None, None, 1) │ 0 │ - │\n", "│ (InputLayer) │ │ │ │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ total_reverse (InputLayer) │ (None) │ 0 │ - │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_12 (Dense) │ (None, 128) │ 6,784 │ concatenate_8[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_indices_to_disj… │ [(None, 2), (1, None), │ 0 │ edge_indices[0][0], │\n", "│ (CastBatchedIndicesToDisjoin… │ (None), (None), (None), │ │ edge_indices_reverse[0][0], │\n", - "│ │ (None), (None), (None)] │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ (None), (None), (None)] │ │ total_edges[0][0], │\n", + "│ │ │ │ total_reverse[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dense_12[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1558,7 +1597,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_20 (Dropout) │ (None, 128) │ 0 │ activation_20[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_20[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1569,7 +1608,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_21 (Dropout) │ (None, 128) │ 0 │ activation_21[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_21[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1580,7 +1619,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_22 (Dropout) │ (None, 128) │ 0 │ activation_22[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_22[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1591,7 +1630,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_23 (Dropout) │ (None, 128) │ 0 │ activation_23[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (DMPNNPPoolingEdgesDirected) │ │ │ dropout_23[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1602,12 +1641,12 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_24 (Dropout) │ (None, 128) │ 0 │ activation_24[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ aggregate_local_edges_29 │ (None, 128) │ 0 │ cast_batched_indices_to_disjo… │\n", + "│ aggregate_local_edges_29 │ (None, 128) │ 0 │ cast_batched_attributes_to_di… │\n", "│ (AggregateLocalEdges) │ │ │ dropout_24[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ concatenate_9 (Concatenate) │ (None, 169) │ 0 │ aggregate_local_edges_29[0][0… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_14 (Dense) │ (None, 128) │ 21,760 │ concatenate_9[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", @@ -1617,8 +1656,8 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ mlp_4 (MLP) │ (None, 1) │ 10,368 │ pooling_nodes_4[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state… │ (None, 1) │ 0 │ mlp_4[0][0] │\n", - "│ (CastDisjointToGraphState) │ │ │ │\n", + "│ cast_disjoint_to_batched_gra… │ (None, 1) │ 0 │ mlp_4[0][0] │\n", + "│ (CastDisjointToBatchedGraphS… │ │ │ │\n", "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n", "\n" ], @@ -1639,28 +1678,33 @@ "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", + "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_attributes (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gather_nodes_outgoing_44 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ gather_nodes_outgoing_24 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mGatherNodesOutgoing\u001b[0m) │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ edge_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ concatenate_8 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ gather_nodes_outgoing_44[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", + "│ concatenate_8 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ gather_nodes_outgoing_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ edge_indices_reverse │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", "│ (\u001b[38;5;33mInputLayer\u001b[0m) │ │ │ │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ total_reverse (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_12 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m6,784\u001b[0m │ concatenate_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m), (\u001b[38;5;34m1\u001b[0m, \u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ │ edge_indices_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", + "│ │ │ │ total_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dense_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1677,7 +1721,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_20 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1688,7 +1732,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_21 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1699,7 +1743,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_22 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1710,7 +1754,7 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_23 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m) │ │ │ dropout_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", @@ -1721,12 +1765,12 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dropout_24 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ activation_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ aggregate_local_edges_29 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ aggregate_local_edges_29 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ (\u001b[38;5;33mAggregateLocalEdges\u001b[0m) │ │ │ dropout_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ concatenate_9 (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m169\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ aggregate_local_edges_29[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", + "│ │ │ │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ dense_14 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m21,760\u001b[0m │ concatenate_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", @@ -1736,8 +1780,8 @@ "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ mlp_4 (\u001b[38;5;33mMLP\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m10,368\u001b[0m │ pooling_nodes_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mlp_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mCastDisjointToGraphState\u001b[0m) │ │ │ │\n", + "│ cast_disjoint_to_batched_gra… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ mlp_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "│ (\u001b[38;5;33mCastDisjointToBatchedGraphS…\u001b[0m │ │ │ │\n", "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n" ] }, @@ -1788,213 +1832,7 @@ "output_type": "stream", "text": [ "None\n", - "Print Time for training: 0:06:12.312500\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 101/300\n", - "29/29 - 1s - loss: 0.1426 - scaled_mean_absolute_error: 0.2995 - scaled_root_mean_squared_error: 0.4189 - lr: 0.0010 - 647ms/epoch - 22ms/step\n", - "Epoch 102/300\n", - "29/29 - 1s - loss: 0.1317 - scaled_mean_absolute_error: 0.2764 - scaled_root_mean_squared_error: 0.3872 - lr: 9.9505e-04 - 644ms/epoch - 22ms/step\n", - "Epoch 103/300\n", - "29/29 - 1s - loss: 0.1451 - scaled_mean_absolute_error: 0.3047 - scaled_root_mean_squared_error: 0.4286 - lr: 9.9010e-04 - 647ms/epoch - 22ms/step\n", - "Epoch 104/300\n", - "29/29 - 1s - loss: 0.1314 - scaled_mean_absolute_error: 0.2760 - scaled_root_mean_squared_error: 0.3953 - lr: 9.8515e-04 - 645ms/epoch - 22ms/step\n", - "Epoch 105/300\n", - "29/29 - 1s - loss: 0.1390 - scaled_mean_absolute_error: 0.2919 - scaled_root_mean_squared_error: 0.4088 - lr: 9.8020e-04 - 650ms/epoch - 22ms/step\n", - "Epoch 106/300\n", - "29/29 - 1s - loss: 0.1342 - scaled_mean_absolute_error: 0.2819 - scaled_root_mean_squared_error: 0.3955 - lr: 9.7525e-04 - 650ms/epoch - 22ms/step\n", - "Epoch 107/300\n", - "29/29 - 1s - loss: 0.1274 - scaled_mean_absolute_error: 0.2676 - scaled_root_mean_squared_error: 0.3836 - lr: 9.7030e-04 - 650ms/epoch - 22ms/step\n", - "Epoch 108/300\n", - "29/29 - 1s - loss: 0.1277 - scaled_mean_absolute_error: 0.2681 - scaled_root_mean_squared_error: 0.3767 - lr: 9.6535e-04 - 648ms/epoch - 22ms/step\n", - "Epoch 109/300\n", - "29/29 - 1s - loss: 0.1271 - scaled_mean_absolute_error: 0.2669 - scaled_root_mean_squared_error: 0.3820 - lr: 9.6040e-04 - 654ms/epoch - 23ms/step\n", - "Epoch 110/300\n", - "29/29 - 1s - loss: 0.1299 - scaled_mean_absolute_error: 0.2727 - scaled_root_mean_squared_error: 0.3947 - val_loss: 0.2738 - val_scaled_mean_absolute_error: 0.5748 - val_scaled_root_mean_squared_error: 0.7613 - lr: 9.5545e-04 - 761ms/epoch - 26ms/step\n", - "Epoch 111/300\n", - "29/29 - 1s - loss: 0.1431 - scaled_mean_absolute_error: 0.3005 - scaled_root_mean_squared_error: 0.4287 - lr: 9.5050e-04 - 650ms/epoch - 22ms/step\n", - "Epoch 112/300\n", - "29/29 - 1s - loss: 0.1429 - scaled_mean_absolute_error: 0.3001 - scaled_root_mean_squared_error: 0.4157 - lr: 9.4555e-04 - 657ms/epoch - 23ms/step\n", - "Epoch 113/300\n", - "29/29 - 1s - loss: 0.1216 - scaled_mean_absolute_error: 0.2552 - scaled_root_mean_squared_error: 0.3706 - lr: 9.4060e-04 - 649ms/epoch - 22ms/step\n", - "Epoch 114/300\n", - "29/29 - 1s - loss: 0.1237 - scaled_mean_absolute_error: 0.2598 - scaled_root_mean_squared_error: 0.3769 - lr: 9.3565e-04 - 645ms/epoch - 22ms/step\n", - "Epoch 115/300\n", - "29/29 - 1s - loss: 0.1301 - scaled_mean_absolute_error: 0.2731 - scaled_root_mean_squared_error: 0.3802 - lr: 9.3070e-04 - 641ms/epoch - 22ms/step\n", - "Epoch 116/300\n", - "29/29 - 1s - loss: 0.1389 - scaled_mean_absolute_error: 0.2918 - scaled_root_mean_squared_error: 0.4107 - lr: 9.2575e-04 - 648ms/epoch - 22ms/step\n", - "Epoch 117/300\n", - "29/29 - 1s - loss: 0.1235 - scaled_mean_absolute_error: 0.2593 - scaled_root_mean_squared_error: 0.3663 - lr: 9.2080e-04 - 645ms/epoch - 22ms/step\n", - "Epoch 118/300\n", - "29/29 - 1s - loss: 0.1156 - scaled_mean_absolute_error: 0.2428 - scaled_root_mean_squared_error: 0.3513 - lr: 9.1585e-04 - 647ms/epoch - 22ms/step\n", - "Epoch 119/300\n", - "29/29 - 1s - loss: 0.1181 - scaled_mean_absolute_error: 0.2481 - scaled_root_mean_squared_error: 0.3598 - lr: 9.1090e-04 - 643ms/epoch - 22ms/step\n", - "Epoch 120/300\n", - "29/29 - 1s - loss: 0.1191 - scaled_mean_absolute_error: 0.2501 - scaled_root_mean_squared_error: 0.3586 - val_loss: 0.2215 - val_scaled_mean_absolute_error: 0.4652 - val_scaled_root_mean_squared_error: 0.6257 - lr: 9.0595e-04 - 757ms/epoch - 26ms/step\n", - "Epoch 121/300\n", - "29/29 - 1s - loss: 0.1326 - scaled_mean_absolute_error: 0.2783 - scaled_root_mean_squared_error: 0.3890 - lr: 9.0100e-04 - 650ms/epoch - 22ms/step\n", - "Epoch 122/300\n", - "29/29 - 1s - loss: 0.1196 - scaled_mean_absolute_error: 0.2511 - scaled_root_mean_squared_error: 0.3684 - lr: 8.9605e-04 - 645ms/epoch - 22ms/step\n", - "Epoch 123/300\n", - "29/29 - 1s - loss: 0.1127 - scaled_mean_absolute_error: 0.2367 - scaled_root_mean_squared_error: 0.3375 - lr: 8.9110e-04 - 647ms/epoch - 22ms/step\n", - "Epoch 124/300\n", - "29/29 - 1s - loss: 0.1162 - scaled_mean_absolute_error: 0.2439 - scaled_root_mean_squared_error: 0.3472 - lr: 8.8615e-04 - 644ms/epoch - 22ms/step\n", - "Epoch 125/300\n", - "29/29 - 1s - loss: 0.1248 - scaled_mean_absolute_error: 0.2620 - scaled_root_mean_squared_error: 0.3738 - lr: 8.8120e-04 - 645ms/epoch - 22ms/step\n", - "Epoch 126/300\n", - "29/29 - 1s - loss: 0.1406 - scaled_mean_absolute_error: 0.2952 - scaled_root_mean_squared_error: 0.4015 - lr: 8.7625e-04 - 640ms/epoch - 22ms/step\n", - "Epoch 127/300\n", - "29/29 - 1s - loss: 0.1226 - scaled_mean_absolute_error: 0.2573 - scaled_root_mean_squared_error: 0.3706 - lr: 8.7130e-04 - 557ms/epoch - 19ms/step\n", - "Epoch 128/300\n", - "29/29 - 1s - loss: 0.1269 - scaled_mean_absolute_error: 0.2664 - scaled_root_mean_squared_error: 0.3675 - lr: 8.6635e-04 - 545ms/epoch - 19ms/step\n", - "Epoch 129/300\n", - "29/29 - 1s - loss: 0.1171 - scaled_mean_absolute_error: 0.2458 - scaled_root_mean_squared_error: 0.3518 - lr: 8.6140e-04 - 543ms/epoch - 19ms/step\n", - "Epoch 130/300\n", - "29/29 - 1s - loss: 0.1212 - scaled_mean_absolute_error: 0.2546 - scaled_root_mean_squared_error: 0.3649 - val_loss: 0.2209 - val_scaled_mean_absolute_error: 0.4639 - val_scaled_root_mean_squared_error: 0.6376 - lr: 8.5645e-04 - 645ms/epoch - 22ms/step\n", - "Epoch 131/300\n", - "29/29 - 1s - loss: 0.1099 - scaled_mean_absolute_error: 0.2308 - scaled_root_mean_squared_error: 0.3399 - lr: 8.5150e-04 - 540ms/epoch - 19ms/step\n", - "Epoch 132/300\n", - "29/29 - 1s - loss: 0.1121 - scaled_mean_absolute_error: 0.2354 - scaled_root_mean_squared_error: 0.3348 - lr: 8.4655e-04 - 539ms/epoch - 19ms/step\n", - "Epoch 133/300\n", - "29/29 - 1s - loss: 0.1141 - scaled_mean_absolute_error: 0.2396 - scaled_root_mean_squared_error: 0.3448 - lr: 8.4160e-04 - 545ms/epoch - 19ms/step\n", - "Epoch 134/300\n", - "29/29 - 1s - loss: 0.1082 - scaled_mean_absolute_error: 0.2273 - scaled_root_mean_squared_error: 0.3219 - lr: 8.3665e-04 - 538ms/epoch - 19ms/step\n", - "Epoch 135/300\n", - "29/29 - 1s - loss: 0.1334 - scaled_mean_absolute_error: 0.2800 - scaled_root_mean_squared_error: 0.3850 - lr: 8.3170e-04 - 538ms/epoch - 19ms/step\n", - "Epoch 136/300\n", - "29/29 - 1s - loss: 0.1209 - scaled_mean_absolute_error: 0.2538 - scaled_root_mean_squared_error: 0.3612 - lr: 8.2675e-04 - 539ms/epoch - 19ms/step\n", - "Epoch 137/300\n", - "29/29 - 1s - loss: 0.1155 - scaled_mean_absolute_error: 0.2425 - scaled_root_mean_squared_error: 0.3508 - lr: 8.2180e-04 - 534ms/epoch - 18ms/step\n", - "Epoch 138/300\n", - "29/29 - 1s - loss: 0.1157 - scaled_mean_absolute_error: 0.2429 - scaled_root_mean_squared_error: 0.3448 - lr: 8.1685e-04 - 539ms/epoch - 19ms/step\n", - "Epoch 139/300\n", - "29/29 - 1s - loss: 0.1081 - scaled_mean_absolute_error: 0.2271 - scaled_root_mean_squared_error: 0.3206 - lr: 8.1190e-04 - 537ms/epoch - 19ms/step\n", - "Epoch 140/300\n", - "29/29 - 1s - loss: 0.1030 - scaled_mean_absolute_error: 0.2162 - scaled_root_mean_squared_error: 0.3091 - val_loss: 0.2213 - val_scaled_mean_absolute_error: 0.4648 - val_scaled_root_mean_squared_error: 0.6340 - lr: 8.0695e-04 - 642ms/epoch - 22ms/step\n", - "Epoch 141/300\n", - "29/29 - 1s - loss: 0.1056 - scaled_mean_absolute_error: 0.2217 - scaled_root_mean_squared_error: 0.3132 - lr: 8.0200e-04 - 536ms/epoch - 18ms/step\n", - "Epoch 142/300\n", - "29/29 - 1s - loss: 0.1046 - scaled_mean_absolute_error: 0.2196 - scaled_root_mean_squared_error: 0.3273 - lr: 7.9705e-04 - 539ms/epoch - 19ms/step\n", - "Epoch 143/300\n", - "29/29 - 1s - loss: 0.1066 - scaled_mean_absolute_error: 0.2238 - scaled_root_mean_squared_error: 0.3273 - lr: 7.9210e-04 - 546ms/epoch - 19ms/step\n", - "Epoch 144/300\n", - "29/29 - 1s - loss: 0.1098 - scaled_mean_absolute_error: 0.2306 - scaled_root_mean_squared_error: 0.3314 - lr: 7.8715e-04 - 544ms/epoch - 19ms/step\n", - "Epoch 145/300\n", - "29/29 - 1s - loss: 0.1118 - scaled_mean_absolute_error: 0.2348 - scaled_root_mean_squared_error: 0.3299 - lr: 7.8220e-04 - 539ms/epoch - 19ms/step\n", - "Epoch 146/300\n", - "29/29 - 1s - loss: 0.1099 - scaled_mean_absolute_error: 0.2307 - scaled_root_mean_squared_error: 0.3298 - lr: 7.7725e-04 - 541ms/epoch - 19ms/step\n", - "Epoch 147/300\n", - "29/29 - 1s - loss: 0.1028 - scaled_mean_absolute_error: 0.2159 - scaled_root_mean_squared_error: 0.3138 - lr: 7.7230e-04 - 540ms/epoch - 19ms/step\n", - "Epoch 148/300\n", - "29/29 - 1s - loss: 0.1196 - scaled_mean_absolute_error: 0.2511 - scaled_root_mean_squared_error: 0.3618 - lr: 7.6735e-04 - 539ms/epoch - 19ms/step\n", - "Epoch 149/300\n", - "29/29 - 1s - loss: 0.1041 - scaled_mean_absolute_error: 0.2186 - scaled_root_mean_squared_error: 0.3104 - lr: 7.6240e-04 - 544ms/epoch - 19ms/step\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 150/300\n", - "29/29 - 1s - loss: 0.1069 - scaled_mean_absolute_error: 0.2245 - scaled_root_mean_squared_error: 0.3222 - val_loss: 0.2308 - val_scaled_mean_absolute_error: 0.4847 - val_scaled_root_mean_squared_error: 0.6397 - lr: 7.5745e-04 - 751ms/epoch - 26ms/step\n", - "Epoch 151/300\n", - "29/29 - 1s - loss: 0.1101 - scaled_mean_absolute_error: 0.2313 - scaled_root_mean_squared_error: 0.3306 - lr: 7.5250e-04 - 639ms/epoch - 22ms/step\n", - "Epoch 152/300\n", - "29/29 - 1s - loss: 0.1148 - scaled_mean_absolute_error: 0.2411 - scaled_root_mean_squared_error: 0.3402 - lr: 7.4755e-04 - 642ms/epoch - 22ms/step\n", - "Epoch 153/300\n", - "29/29 - 1s - loss: 0.1029 - scaled_mean_absolute_error: 0.2161 - scaled_root_mean_squared_error: 0.3111 - lr: 7.4260e-04 - 676ms/epoch - 23ms/step\n", - "Epoch 154/300\n", - "29/29 - 1s - loss: 0.1091 - scaled_mean_absolute_error: 0.2291 - scaled_root_mean_squared_error: 0.3208 - lr: 7.3765e-04 - 562ms/epoch - 19ms/step\n", - "Epoch 155/300\n", - "29/29 - 1s - loss: 0.1003 - scaled_mean_absolute_error: 0.2106 - scaled_root_mean_squared_error: 0.3127 - lr: 7.3270e-04 - 553ms/epoch - 19ms/step\n", - "Epoch 156/300\n", - "29/29 - 1s - loss: 0.1022 - scaled_mean_absolute_error: 0.2146 - scaled_root_mean_squared_error: 0.3096 - lr: 7.2775e-04 - 537ms/epoch - 19ms/step\n", - "Epoch 157/300\n", - "29/29 - 1s - loss: 0.1099 - scaled_mean_absolute_error: 0.2308 - scaled_root_mean_squared_error: 0.3230 - lr: 7.2280e-04 - 567ms/epoch - 20ms/step\n", - "Epoch 158/300\n", - "29/29 - 1s - loss: 0.1118 - scaled_mean_absolute_error: 0.2347 - scaled_root_mean_squared_error: 0.3378 - lr: 7.1785e-04 - 639ms/epoch - 22ms/step\n", - "Epoch 159/300\n", - "29/29 - 1s - loss: 0.1091 - scaled_mean_absolute_error: 0.2291 - scaled_root_mean_squared_error: 0.3254 - lr: 7.1290e-04 - 573ms/epoch - 20ms/step\n", - "Epoch 160/300\n", - "29/29 - 1s - loss: 0.0995 - scaled_mean_absolute_error: 0.2089 - scaled_root_mean_squared_error: 0.3012 - val_loss: 0.2138 - val_scaled_mean_absolute_error: 0.4489 - val_scaled_root_mean_squared_error: 0.6067 - lr: 7.0795e-04 - 723ms/epoch - 25ms/step\n", - "Epoch 161/300\n", - "29/29 - 1s - loss: 0.1009 - scaled_mean_absolute_error: 0.2119 - scaled_root_mean_squared_error: 0.2987 - lr: 7.0300e-04 - 605ms/epoch - 21ms/step\n", - "Epoch 162/300\n", - "29/29 - 1s - loss: 0.1065 - scaled_mean_absolute_error: 0.2236 - scaled_root_mean_squared_error: 0.3247 - lr: 6.9805e-04 - 615ms/epoch - 21ms/step\n", - "Epoch 163/300\n", - "29/29 - 1s - loss: 0.1024 - scaled_mean_absolute_error: 0.2149 - scaled_root_mean_squared_error: 0.3047 - lr: 6.9310e-04 - 583ms/epoch - 20ms/step\n", - "Epoch 164/300\n", - "29/29 - 1s - loss: 0.1019 - scaled_mean_absolute_error: 0.2141 - scaled_root_mean_squared_error: 0.3104 - lr: 6.8815e-04 - 595ms/epoch - 21ms/step\n", - "Epoch 165/300\n", - "29/29 - 1s - loss: 0.0977 - scaled_mean_absolute_error: 0.2051 - scaled_root_mean_squared_error: 0.2908 - lr: 6.8320e-04 - 564ms/epoch - 19ms/step\n", - "Epoch 166/300\n", - "29/29 - 1s - loss: 0.1005 - scaled_mean_absolute_error: 0.2111 - scaled_root_mean_squared_error: 0.3047 - lr: 6.7825e-04 - 587ms/epoch - 20ms/step\n", - "Epoch 167/300\n", - "29/29 - 1s - loss: 0.0955 - scaled_mean_absolute_error: 0.2006 - scaled_root_mean_squared_error: 0.2874 - lr: 6.7330e-04 - 549ms/epoch - 19ms/step\n", - "Epoch 168/300\n", - "29/29 - 1s - loss: 0.0895 - scaled_mean_absolute_error: 0.1879 - scaled_root_mean_squared_error: 0.2793 - lr: 6.6835e-04 - 543ms/epoch - 19ms/step\n", - "Epoch 169/300\n", - "29/29 - 1s - loss: 0.0928 - scaled_mean_absolute_error: 0.1950 - scaled_root_mean_squared_error: 0.2770 - lr: 6.6340e-04 - 547ms/epoch - 19ms/step\n", - "Epoch 170/300\n", - "29/29 - 1s - loss: 0.0953 - scaled_mean_absolute_error: 0.2001 - scaled_root_mean_squared_error: 0.2932 - val_loss: 0.2197 - val_scaled_mean_absolute_error: 0.4614 - val_scaled_root_mean_squared_error: 0.6207 - lr: 6.5845e-04 - 652ms/epoch - 22ms/step\n", - "Epoch 171/300\n", - "29/29 - 1s - loss: 0.1000 - scaled_mean_absolute_error: 0.2100 - scaled_root_mean_squared_error: 0.2943 - lr: 6.5350e-04 - 548ms/epoch - 19ms/step\n", - "Epoch 172/300\n", - "29/29 - 1s - loss: 0.1205 - scaled_mean_absolute_error: 0.2530 - scaled_root_mean_squared_error: 0.3470 - lr: 6.4855e-04 - 559ms/epoch - 19ms/step\n", - "Epoch 173/300\n", - "29/29 - 1s - loss: 0.0961 - scaled_mean_absolute_error: 0.2019 - scaled_root_mean_squared_error: 0.2896 - lr: 6.4360e-04 - 546ms/epoch - 19ms/step\n", - "Epoch 174/300\n", - "29/29 - 1s - loss: 0.0929 - scaled_mean_absolute_error: 0.1951 - scaled_root_mean_squared_error: 0.2745 - lr: 6.3865e-04 - 576ms/epoch - 20ms/step\n", - "Epoch 175/300\n", - "29/29 - 1s - loss: 0.0955 - scaled_mean_absolute_error: 0.2006 - scaled_root_mean_squared_error: 0.2817 - lr: 6.3370e-04 - 597ms/epoch - 21ms/step\n", - "Epoch 176/300\n", - "29/29 - 1s - loss: 0.0937 - scaled_mean_absolute_error: 0.1966 - scaled_root_mean_squared_error: 0.2822 - lr: 6.2875e-04 - 587ms/epoch - 20ms/step\n", - "Epoch 177/300\n", - "29/29 - 1s - loss: 0.0911 - scaled_mean_absolute_error: 0.1912 - scaled_root_mean_squared_error: 0.2820 - lr: 6.2380e-04 - 607ms/epoch - 21ms/step\n", - "Epoch 178/300\n", - "29/29 - 1s - loss: 0.0950 - scaled_mean_absolute_error: 0.1995 - scaled_root_mean_squared_error: 0.2839 - lr: 6.1885e-04 - 585ms/epoch - 20ms/step\n", - "Epoch 179/300\n", - "29/29 - 1s - loss: 0.0985 - scaled_mean_absolute_error: 0.2069 - scaled_root_mean_squared_error: 0.2960 - lr: 6.1390e-04 - 585ms/epoch - 20ms/step\n", - "Epoch 180/300\n", - "29/29 - 1s - loss: 0.0929 - scaled_mean_absolute_error: 0.1951 - scaled_root_mean_squared_error: 0.2844 - val_loss: 0.2172 - val_scaled_mean_absolute_error: 0.4560 - val_scaled_root_mean_squared_error: 0.6106 - lr: 6.0895e-04 - 698ms/epoch - 24ms/step\n", - "Epoch 181/300\n", - "29/29 - 1s - loss: 0.0856 - scaled_mean_absolute_error: 0.1797 - scaled_root_mean_squared_error: 0.2630 - lr: 6.0400e-04 - 587ms/epoch - 20ms/step\n", - "Epoch 182/300\n", - "29/29 - 1s - loss: 0.0959 - scaled_mean_absolute_error: 0.2014 - scaled_root_mean_squared_error: 0.2840 - lr: 5.9905e-04 - 546ms/epoch - 19ms/step\n", - "Epoch 183/300\n", - "29/29 - 1s - loss: 0.0937 - scaled_mean_absolute_error: 0.1967 - scaled_root_mean_squared_error: 0.2778 - lr: 5.9410e-04 - 543ms/epoch - 19ms/step\n", - "Epoch 184/300\n", - "29/29 - 1s - loss: 0.0891 - scaled_mean_absolute_error: 0.1870 - scaled_root_mean_squared_error: 0.2822 - lr: 5.8915e-04 - 550ms/epoch - 19ms/step\n", - "Epoch 185/300\n", - "29/29 - 1s - loss: 0.0883 - scaled_mean_absolute_error: 0.1855 - scaled_root_mean_squared_error: 0.2708 - lr: 5.8420e-04 - 549ms/epoch - 19ms/step\n", - "Epoch 186/300\n", - "29/29 - 1s - loss: 0.0891 - scaled_mean_absolute_error: 0.1870 - scaled_root_mean_squared_error: 0.2727 - lr: 5.7925e-04 - 578ms/epoch - 20ms/step\n", - "Epoch 187/300\n", - "29/29 - 1s - loss: 0.0955 - scaled_mean_absolute_error: 0.2006 - scaled_root_mean_squared_error: 0.2774 - lr: 5.7430e-04 - 585ms/epoch - 20ms/step\n", - "Epoch 188/300\n", - "29/29 - 1s - loss: 0.0911 - scaled_mean_absolute_error: 0.1913 - scaled_root_mean_squared_error: 0.2742 - lr: 5.6935e-04 - 581ms/epoch - 20ms/step\n", - "Epoch 189/300\n", - "29/29 - 1s - loss: 0.0894 - scaled_mean_absolute_error: 0.1878 - scaled_root_mean_squared_error: 0.2710 - lr: 5.6440e-04 - 578ms/epoch - 20ms/step\n", - "Epoch 190/300\n", - "29/29 - 1s - loss: 0.0888 - scaled_mean_absolute_error: 0.1864 - scaled_root_mean_squared_error: 0.2772 - val_loss: 0.2136 - val_scaled_mean_absolute_error: 0.4485 - val_scaled_root_mean_squared_error: 0.5983 - lr: 5.5945e-04 - 662ms/epoch - 23ms/step\n", - "Epoch 191/300\n", - "29/29 - 1s - loss: 0.0854 - scaled_mean_absolute_error: 0.1792 - scaled_root_mean_squared_error: 0.2646 - lr: 5.5450e-04 - 543ms/epoch - 19ms/step\n", - "Epoch 192/300\n", - "29/29 - 1s - loss: 0.0885 - scaled_mean_absolute_error: 0.1857 - scaled_root_mean_squared_error: 0.2594 - lr: 5.4955e-04 - 549ms/epoch - 19ms/step\n", - "Epoch 193/300\n", - "29/29 - 1s - loss: 0.0852 - scaled_mean_absolute_error: 0.1788 - scaled_root_mean_squared_error: 0.2653 - lr: 5.4460e-04 - 544ms/epoch - 19ms/step\n", - "Epoch 194/300\n", - "29/29 - 1s - loss: 0.0836 - scaled_mean_absolute_error: 0.1755 - scaled_root_mean_squared_error: 0.2614 - lr: 5.3965e-04 - 552ms/epoch - 19ms/step\n", - "Epoch 195/300\n", - "29/29 - 1s - loss: 0.0879 - scaled_mean_absolute_error: 0.1846 - scaled_root_mean_squared_error: 0.2633 - lr: 5.3470e-04 - 539ms/epoch - 19ms/step\n", - "Epoch 196/300\n", - "29/29 - 1s - loss: 0.0866 - scaled_mean_absolute_error: 0.1819 - scaled_root_mean_squared_error: 0.2615 - lr: 5.2975e-04 - 541ms/epoch - 19ms/step\n", - "Epoch 197/300\n", - "29/29 - 1s - loss: 0.0846 - scaled_mean_absolute_error: 0.1775 - scaled_root_mean_squared_error: 0.2560 - lr: 5.2480e-04 - 543ms/epoch - 19ms/step\n" + "Print Time for training: 0:08:16.421875\n" ] }, { @@ -2226,7 +2064,7 @@ "source": [ "import time\n", "from kgcnn.models.utils import get_model_class\n", - "from keras_core.optimizers import Adam\n", + "from keras.optimizers import Adam\n", "from kgcnn.training.scheduler import LinearLearningRateScheduler\n", "from kgcnn.literature.DMPNN import make_model\n", "from kgcnn.data.transform.scaler.molecule import QMGraphLabelScaler\n", @@ -2302,7 +2140,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -2329,12 +2167,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n" + "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -2385,7 +2223,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.5" } }, "nbformat": 4, diff --git a/notebooks/workflow_qm_regression.ipynb b/notebooks/workflow_qm_regression.ipynb index c1d28735..e7c87a5b 100644 --- a/notebooks/workflow_qm_regression.ipynb +++ b/notebooks/workflow_qm_regression.ipynb @@ -13,17 +13,9 @@ "execution_count": 1, "id": "01403a2c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using PyTorch backend.\n" - ] - } - ], + "outputs": [], "source": [ - "import keras_core as ks" + "import keras as ks" ] }, { @@ -139,93 +131,12 @@ "execution_count": 7, "id": "f7926ab2", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from kgcnn.graph.preprocessor import SetRange\n", "data.map_list(SetRange(max_distance=5.0, in_place=True));\n", "data.map_list(method=\"count_nodes_and_edges\", total_edges=\"total_ranges\", count_edges=\"range_indices\", \n", - " count_nodes=\"node_number\", total_nodes=\"total_nodes\")" + " count_nodes=\"node_number\", total_nodes=\"total_nodes\");" ] }, { @@ -380,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "7a24751c", "metadata": {}, "outputs": [ @@ -428,10 +339,13 @@ "│ cast_batched_attributes_to_d… │ [(None, 3), (None), │ 0 │ node_coordinates[0][0], │\n", "│ (CastBatchedAttributesToDisj… │ (None), (None)] │ │ total_nodes[0][0] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ cast_batched_attributes_to_d… │ [(None), (None), (None), │ 0 │ node_number[0][0], │\n", + "│ (CastBatchedAttributesToDisj… │ (None)] │ │ total_nodes[0][0] │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ node_position (NodePosition) │ [(None, 3), (None, 3)] │ 0 │ cast_batched_attributes_to_di… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ embedding (Embedding) │ (None, 64) │ 6,080 │ cast_batched_indices_to_disjo… │\n", + "│ embedding (Embedding) │ (None, 64) │ 6,080 │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ node_distance_euclidean │ (None, 1) │ 0 │ node_position[0][0], │\n", "│ (NodeDistanceEuclidean) │ │ │ node_position[0][1] │\n", @@ -465,8 +379,8 @@ "│ │ │ │ mlp[0][0], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state │ (None, 1) │ 0 │ pooling_nodes[0][0] │\n", - "│ (CastDisjointToGraphState) │ │ │ │\n", + "│ cast_disjoint_to_batched_gra… │ (None, 1) │ 0 │ pooling_nodes[0][0] │\n", + "│ (CastDisjointToBatchedGraphS… │ │ │ │\n", "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n", "\n" ], @@ -492,10 +406,13 @@ "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_coordinates[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", + "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_number[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", + "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ node_position (\u001b[38;5;33mNodePosition\u001b[0m) │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)] │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m6,080\u001b[0m │ cast_batched_indices_to_disjo… │\n", + "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m6,080\u001b[0m │ cast_batched_attributes_to_di… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", "│ node_distance_euclidean │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ node_position[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ (\u001b[38;5;33mNodeDistanceEuclidean\u001b[0m) │ │ │ node_position[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m] │\n", @@ -529,202 +446,8 @@ "│ │ │ │ mlp[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", "│ │ │ │ cast_batched_indices_to_disjo… │\n", "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ pooling_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mCastDisjointToGraphState\u001b[0m) │ │ │ │\n", - "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Total params: 313,665 (1.20 MB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Trainable params: 313,665 (1.20 MB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Non-trainable params: 0 (0.00 B)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Compiled with jit: False\n", - "Print Time for training: 1:36:07.687500\n", - "Running training on fold: 1\n" - ] - }, - { - "data": { - "text/html": [ - "
Model: \"Schnet\"\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1mModel: \"Schnet\"\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                  ┃ Output Shape              ┃     Param # ┃ Connected to                   ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│ node_number (InputLayer)      │ (None, None)              │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ range_indices (InputLayer)    │ (None, None, 2)           │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ total_nodes (InputLayer)      │ (None)                    │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ total_ranges (InputLayer)     │ (None)                    │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ node_coordinates (InputLayer) │ (None, None, 3)           │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ cast_batched_indices_to_disj… │ [(None), (2, None),       │           0 │ node_number[0][0],             │\n",
-       "│ (CastBatchedIndicesToDisjoin… │ (None), (None), (None),   │             │ range_indices[0][0],           │\n",
-       "│                               │ (None), (None), (None)]   │             │ total_nodes[0][0],             │\n",
-       "│                               │                           │             │ total_ranges[0][0]             │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ cast_batched_attributes_to_d… │ [(None, 3), (None),       │           0 │ node_coordinates[0][0],        │\n",
-       "│ (CastBatchedAttributesToDisj… │ (None), (None)]           │             │ total_nodes[0][0]              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ node_position_1               │ [(None, 3), (None, 3)]    │           0 │ cast_batched_attributes_to_di… │\n",
-       "│ (NodePosition)                │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ embedding_1 (Embedding)       │ (None, 64)                │       6,080 │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ node_distance_euclidean_1     │ (None, 1)                 │           0 │ node_position_1[0][0],         │\n",
-       "│ (NodeDistanceEuclidean)       │                           │             │ node_position_1[0][1]          │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ dense_21 (Dense)              │ (None, 128)               │       8,320 │ embedding_1[0][0]              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ gauss_basis_layer_1           │ (None, 20)                │           0 │ node_distance_euclidean_1[0][… │\n",
-       "│ (GaussBasisLayer)             │                           │             │                                │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_4         │ (None, 128)               │      68,608 │ dense_21[0][0],                │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_1[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_5         │ (None, 128)               │      68,608 │ sch_net_interaction_4[0][0],   │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_1[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_6         │ (None, 128)               │      68,608 │ sch_net_interaction_5[0][0],   │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_1[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_7         │ (None, 128)               │      68,608 │ sch_net_interaction_6[0][0],   │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_1[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ mlp_1 (MLP)                   │ (None, 1)                 │      24,833 │ sch_net_interaction_7[0][0],   │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ pooling_nodes_1               │ (None, 1)                 │           0 │ cast_batched_indices_to_disjo… │\n",
-       "│ (PoolingNodes)                │                           │             │ mlp_1[0][0],                   │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ cast_disjoint_to_graph_state… │ (None, 1)                 │           0 │ pooling_nodes_1[0][0]          │\n",
-       "│ (CastDisjointToGraphState)    │                           │             │                                │\n",
-       "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n",
-       "
\n" - ], - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│ node_number (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ range_indices (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ total_nodes (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ total_ranges (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ node_coordinates (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;34m2\u001b[0m, \u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_number[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ │ range_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ total_ranges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_coordinates[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ node_position_1 │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)] │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", - "│ (\u001b[38;5;33mNodePosition\u001b[0m) │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m6,080\u001b[0m │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ node_distance_euclidean_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ node_position_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mNodeDistanceEuclidean\u001b[0m) │ │ │ node_position_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dense_21 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m8,320\u001b[0m │ embedding_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gauss_basis_layer_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ node_distance_euclidean_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", - "│ (\u001b[38;5;33mGaussBasisLayer\u001b[0m) │ │ │ │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ dense_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_5 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_6 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_7 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ mlp_1 (\u001b[38;5;33mMLP\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m24,833\u001b[0m │ sch_net_interaction_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ pooling_nodes_1 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", - "│ (\u001b[38;5;33mPoolingNodes\u001b[0m) │ │ │ mlp_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ pooling_nodes_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mCastDisjointToGraphState\u001b[0m) │ │ │ │\n", + "│ cast_disjoint_to_batched_gra… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ pooling_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", + "│ (\u001b[38;5;33mCastDisjointToBatchedGraphS…\u001b[0m │ │ │ │\n", "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n" ] }, @@ -774,597 +497,14 @@ "name": "stdout", "output_type": "stream", "text": [ - " Compiled with jit: False\n", - "Print Time for training: 1:49:46.750000\n", - "Running training on fold: 2\n" - ] - }, - { - "data": { - "text/html": [ - "
Model: \"Schnet\"\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1mModel: \"Schnet\"\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                  ┃ Output Shape              ┃     Param # ┃ Connected to                   ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│ node_number (InputLayer)      │ (None, None)              │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ range_indices (InputLayer)    │ (None, None, 2)           │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ total_nodes (InputLayer)      │ (None)                    │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ total_ranges (InputLayer)     │ (None)                    │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ node_coordinates (InputLayer) │ (None, None, 3)           │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ cast_batched_indices_to_disj… │ [(None), (2, None),       │           0 │ node_number[0][0],             │\n",
-       "│ (CastBatchedIndicesToDisjoin… │ (None), (None), (None),   │             │ range_indices[0][0],           │\n",
-       "│                               │ (None), (None), (None)]   │             │ total_nodes[0][0],             │\n",
-       "│                               │                           │             │ total_ranges[0][0]             │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ cast_batched_attributes_to_d… │ [(None, 3), (None),       │           0 │ node_coordinates[0][0],        │\n",
-       "│ (CastBatchedAttributesToDisj… │ (None), (None)]           │             │ total_nodes[0][0]              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ node_position_2               │ [(None, 3), (None, 3)]    │           0 │ cast_batched_attributes_to_di… │\n",
-       "│ (NodePosition)                │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ embedding_2 (Embedding)       │ (None, 64)                │       6,080 │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ node_distance_euclidean_2     │ (None, 1)                 │           0 │ node_position_2[0][0],         │\n",
-       "│ (NodeDistanceEuclidean)       │                           │             │ node_position_2[0][1]          │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ dense_42 (Dense)              │ (None, 128)               │       8,320 │ embedding_2[0][0]              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ gauss_basis_layer_2           │ (None, 20)                │           0 │ node_distance_euclidean_2[0][… │\n",
-       "│ (GaussBasisLayer)             │                           │             │                                │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_8         │ (None, 128)               │      68,608 │ dense_42[0][0],                │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_2[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_9         │ (None, 128)               │      68,608 │ sch_net_interaction_8[0][0],   │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_2[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_10        │ (None, 128)               │      68,608 │ sch_net_interaction_9[0][0],   │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_2[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_11        │ (None, 128)               │      68,608 │ sch_net_interaction_10[0][0],  │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_2[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ mlp_2 (MLP)                   │ (None, 1)                 │      24,833 │ sch_net_interaction_11[0][0],  │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ pooling_nodes_2               │ (None, 1)                 │           0 │ cast_batched_indices_to_disjo… │\n",
-       "│ (PoolingNodes)                │                           │             │ mlp_2[0][0],                   │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ cast_disjoint_to_graph_state… │ (None, 1)                 │           0 │ pooling_nodes_2[0][0]          │\n",
-       "│ (CastDisjointToGraphState)    │                           │             │                                │\n",
-       "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n",
-       "
\n" - ], - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│ node_number (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ range_indices (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ total_nodes (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ total_ranges (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ node_coordinates (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;34m2\u001b[0m, \u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_number[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ │ range_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ total_ranges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_coordinates[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ node_position_2 │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)] │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", - "│ (\u001b[38;5;33mNodePosition\u001b[0m) │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ embedding_2 (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m6,080\u001b[0m │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ node_distance_euclidean_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ node_position_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mNodeDistanceEuclidean\u001b[0m) │ │ │ node_position_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dense_42 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m8,320\u001b[0m │ embedding_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gauss_basis_layer_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ node_distance_euclidean_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", - "│ (\u001b[38;5;33mGaussBasisLayer\u001b[0m) │ │ │ │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_8 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ dense_42[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_9 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_10 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_11 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ mlp_2 (\u001b[38;5;33mMLP\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m24,833\u001b[0m │ sch_net_interaction_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ pooling_nodes_2 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", - "│ (\u001b[38;5;33mPoolingNodes\u001b[0m) │ │ │ mlp_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ pooling_nodes_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mCastDisjointToGraphState\u001b[0m) │ │ │ │\n", - "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Total params: 313,665 (1.20 MB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Trainable params: 313,665 (1.20 MB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Non-trainable params: 0 (0.00 B)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Compiled with jit: False\n", - "Print Time for training: 1:49:31.781250\n", - "Running training on fold: 3\n" - ] - }, - { - "data": { - "text/html": [ - "
Model: \"Schnet\"\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1mModel: \"Schnet\"\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                  ┃ Output Shape              ┃     Param # ┃ Connected to                   ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│ node_number (InputLayer)      │ (None, None)              │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ range_indices (InputLayer)    │ (None, None, 2)           │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ total_nodes (InputLayer)      │ (None)                    │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ total_ranges (InputLayer)     │ (None)                    │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ node_coordinates (InputLayer) │ (None, None, 3)           │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ cast_batched_indices_to_disj… │ [(None), (2, None),       │           0 │ node_number[0][0],             │\n",
-       "│ (CastBatchedIndicesToDisjoin… │ (None), (None), (None),   │             │ range_indices[0][0],           │\n",
-       "│                               │ (None), (None), (None)]   │             │ total_nodes[0][0],             │\n",
-       "│                               │                           │             │ total_ranges[0][0]             │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ cast_batched_attributes_to_d… │ [(None, 3), (None),       │           0 │ node_coordinates[0][0],        │\n",
-       "│ (CastBatchedAttributesToDisj… │ (None), (None)]           │             │ total_nodes[0][0]              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ node_position_3               │ [(None, 3), (None, 3)]    │           0 │ cast_batched_attributes_to_di… │\n",
-       "│ (NodePosition)                │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ embedding_3 (Embedding)       │ (None, 64)                │       6,080 │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ node_distance_euclidean_3     │ (None, 1)                 │           0 │ node_position_3[0][0],         │\n",
-       "│ (NodeDistanceEuclidean)       │                           │             │ node_position_3[0][1]          │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ dense_63 (Dense)              │ (None, 128)               │       8,320 │ embedding_3[0][0]              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ gauss_basis_layer_3           │ (None, 20)                │           0 │ node_distance_euclidean_3[0][… │\n",
-       "│ (GaussBasisLayer)             │                           │             │                                │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_12        │ (None, 128)               │      68,608 │ dense_63[0][0],                │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_3[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_13        │ (None, 128)               │      68,608 │ sch_net_interaction_12[0][0],  │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_3[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_14        │ (None, 128)               │      68,608 │ sch_net_interaction_13[0][0],  │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_3[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_15        │ (None, 128)               │      68,608 │ sch_net_interaction_14[0][0],  │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_3[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ mlp_3 (MLP)                   │ (None, 1)                 │      24,833 │ sch_net_interaction_15[0][0],  │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ pooling_nodes_3               │ (None, 1)                 │           0 │ cast_batched_indices_to_disjo… │\n",
-       "│ (PoolingNodes)                │                           │             │ mlp_3[0][0],                   │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ cast_disjoint_to_graph_state… │ (None, 1)                 │           0 │ pooling_nodes_3[0][0]          │\n",
-       "│ (CastDisjointToGraphState)    │                           │             │                                │\n",
-       "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n",
-       "
\n" - ], - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│ node_number (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ range_indices (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ total_nodes (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ total_ranges (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ node_coordinates (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;34m2\u001b[0m, \u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_number[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ │ range_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ total_ranges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_coordinates[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ node_position_3 │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)] │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", - "│ (\u001b[38;5;33mNodePosition\u001b[0m) │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ embedding_3 (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m6,080\u001b[0m │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ node_distance_euclidean_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ node_position_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mNodeDistanceEuclidean\u001b[0m) │ │ │ node_position_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dense_63 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m8,320\u001b[0m │ embedding_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gauss_basis_layer_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ node_distance_euclidean_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", - "│ (\u001b[38;5;33mGaussBasisLayer\u001b[0m) │ │ │ │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_12 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ dense_63[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_13 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_14 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_15 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ mlp_3 (\u001b[38;5;33mMLP\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m24,833\u001b[0m │ sch_net_interaction_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ pooling_nodes_3 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", - "│ (\u001b[38;5;33mPoolingNodes\u001b[0m) │ │ │ mlp_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ pooling_nodes_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mCastDisjointToGraphState\u001b[0m) │ │ │ │\n", - "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Total params: 313,665 (1.20 MB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Trainable params: 313,665 (1.20 MB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Non-trainable params: 0 (0.00 B)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Compiled with jit: False\n", - "Print Time for training: 1:40:00.984375\n", - "Running training on fold: 4\n" - ] - }, - { - "data": { - "text/html": [ - "
Model: \"Schnet\"\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1mModel: \"Schnet\"\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃ Layer (type)                  ┃ Output Shape              ┃     Param # ┃ Connected to                   ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│ node_number (InputLayer)      │ (None, None)              │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ range_indices (InputLayer)    │ (None, None, 2)           │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ total_nodes (InputLayer)      │ (None)                    │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ total_ranges (InputLayer)     │ (None)                    │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ node_coordinates (InputLayer) │ (None, None, 3)           │           0 │ -                              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ cast_batched_indices_to_disj… │ [(None), (2, None),       │           0 │ node_number[0][0],             │\n",
-       "│ (CastBatchedIndicesToDisjoin… │ (None), (None), (None),   │             │ range_indices[0][0],           │\n",
-       "│                               │ (None), (None), (None)]   │             │ total_nodes[0][0],             │\n",
-       "│                               │                           │             │ total_ranges[0][0]             │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ cast_batched_attributes_to_d… │ [(None, 3), (None),       │           0 │ node_coordinates[0][0],        │\n",
-       "│ (CastBatchedAttributesToDisj… │ (None), (None)]           │             │ total_nodes[0][0]              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ node_position_4               │ [(None, 3), (None, 3)]    │           0 │ cast_batched_attributes_to_di… │\n",
-       "│ (NodePosition)                │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ embedding_4 (Embedding)       │ (None, 64)                │       6,080 │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ node_distance_euclidean_4     │ (None, 1)                 │           0 │ node_position_4[0][0],         │\n",
-       "│ (NodeDistanceEuclidean)       │                           │             │ node_position_4[0][1]          │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ dense_84 (Dense)              │ (None, 128)               │       8,320 │ embedding_4[0][0]              │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ gauss_basis_layer_4           │ (None, 20)                │           0 │ node_distance_euclidean_4[0][… │\n",
-       "│ (GaussBasisLayer)             │                           │             │                                │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_16        │ (None, 128)               │      68,608 │ dense_84[0][0],                │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_4[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_17        │ (None, 128)               │      68,608 │ sch_net_interaction_16[0][0],  │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_4[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_18        │ (None, 128)               │      68,608 │ sch_net_interaction_17[0][0],  │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_4[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ sch_net_interaction_19        │ (None, 128)               │      68,608 │ sch_net_interaction_18[0][0],  │\n",
-       "│ (SchNetInteraction)           │                           │             │ gauss_basis_layer_4[0][0],     │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ mlp_4 (MLP)                   │ (None, 1)                 │      24,833 │ sch_net_interaction_19[0][0],  │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ pooling_nodes_4               │ (None, 1)                 │           0 │ cast_batched_indices_to_disjo… │\n",
-       "│ (PoolingNodes)                │                           │             │ mlp_4[0][0],                   │\n",
-       "│                               │                           │             │ cast_batched_indices_to_disjo… │\n",
-       "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n",
-       "│ cast_disjoint_to_graph_state… │ (None, 1)                 │           0 │ pooling_nodes_4[0][0]          │\n",
-       "│ (CastDisjointToGraphState)    │                           │             │                                │\n",
-       "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n",
-       "
\n" - ], - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to \u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│ node_number (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ range_indices (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ total_nodes (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ total_ranges (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ node_coordinates (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ - │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;34m2\u001b[0m, \u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_number[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ │ range_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ total_ranges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), │ \u001b[38;5;34m0\u001b[0m │ node_coordinates[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)] │ │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ node_position_4 │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)] │ \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_to_di… │\n", - "│ (\u001b[38;5;33mNodePosition\u001b[0m) │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ embedding_4 (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m6,080\u001b[0m │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ node_distance_euclidean_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ node_position_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mNodeDistanceEuclidean\u001b[0m) │ │ │ node_position_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ dense_84 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m8,320\u001b[0m │ embedding_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ gauss_basis_layer_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ node_distance_euclidean_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n", - "│ (\u001b[38;5;33mGaussBasisLayer\u001b[0m) │ │ │ │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_16 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ dense_84[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_17 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_18 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ sch_net_interaction_19 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m) │ │ │ gauss_basis_layer_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ mlp_4 (\u001b[38;5;33mMLP\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m24,833\u001b[0m │ sch_net_interaction_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ pooling_nodes_4 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_disjo… │\n", - "│ (\u001b[38;5;33mPoolingNodes\u001b[0m) │ │ │ mlp_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n", - "│ │ │ │ cast_batched_indices_to_disjo… │\n", - "├───────────────────────────────┼───────────────────────────┼─────────────┼────────────────────────────────┤\n", - "│ cast_disjoint_to_graph_state… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │ pooling_nodes_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n", - "│ (\u001b[38;5;33mCastDisjointToGraphState\u001b[0m) │ │ │ │\n", - "└───────────────────────────────┴───────────────────────────┴─────────────┴────────────────────────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Total params: 313,665 (1.20 MB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Trainable params: 313,665 (1.20 MB)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
 Non-trainable params: 0 (0.00 B)\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Compiled with jit: False\n", - "Print Time for training: 1:40:45.843750\n" + " Compiled with jit: False\n" ] } ], "source": [ "import time\n", "from kgcnn.models.utils import get_model_class\n", - "from keras_core.optimizers import Adam\n", + "from keras.optimizers import Adam\n", "from kgcnn.training.scheduler import LinearLearningRateScheduler\n", "from kgcnn.literature.Schnet import make_model\n", "from kgcnn.data.transform.scaler.molecule import QMGraphLabelScaler\n", @@ -1435,32 +575,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "c1d034b3", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from kgcnn.utils.plots import plot_train_test_loss, plot_predict_true\n", "\n", @@ -1494,7 +612,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.5" } }, "nbformat": 4,