weights -> all_weights, trainable_weights, nontrainable_weights (#966)

* (non)trainable weights, layer all_layers

* weights -> all_weights

* weights -> all_weights, trainable weights, nontrainable_weights

* fix bugs, yapf

* fix bugs

* fix bugs

* fix bugs

Author: warshallrho

CHANGELOG.md

@@ -75,6 +75,8 @@ To release a new version, please update the changelog as followed:
 
 ### Changed
 - remove `tl.layers.initialize_global_variables(sess)` (PR #931)
+- change `tl.layers.core`, `tl.models.core` (PR #966)
+   - change `weights` into `all_weights`, `trainable_weights`, `nontrainable_weights`
 
 ### Dependencies Update
 - nltk>=3.3,<3.4 => nltk>=3.3,<3.5 (PR #892)

docs/modules/files.rst

@@ -142,14 +142,14 @@ sake of cross-platform. Other file formats such as ``.npz`` are also available.
 .. code-block:: python
 
   ## save model as .h5
-  tl.files.save_weights_to_hdf5('model.h5', network.weights)
+  tl.files.save_weights_to_hdf5('model.h5', network.all_weights)
   # restore model from .h5 (in order)
-  tl.files.load_hdf5_to_weights_in_order('model.h5', network.weights)
+  tl.files.load_hdf5_to_weights_in_order('model.h5', network.all_weights)
   # restore model from .h5 (by name)
-  tl.files.load_hdf5_to_weights('model.h5', network.weights)
+  tl.files.load_hdf5_to_weights('model.h5', network.all_weights)
 
   ## save model as .npz
-  tl.files.save_npz(network.weights , name='model.npz')
+  tl.files.save_npz(network.all_weights , name='model.npz')
   # restore model from .npz (method 1)
   load_params = tl.files.load_npz(name='model.npz')
   tl.files.assign_weights(sess, load_params, network)

docs/user/faq.rst

@@ -46,19 +46,19 @@ To choose which variables to update, you can do as below.
 
 .. code-block:: python
 
-  train_params = network.weights[3:]
+  train_params = network.trainable_weights[3:]
 
 The second way is to get the variables by a given name. For example, if you want to get all variables which the layer name contain ``dense``, you can do as below.
 
 .. code-block:: python
 
-  train_params = network.get_layer('dense').weights
+  train_params = network.get_layer('dense').trainable_weights
 
 After you get the variable list, you can define your optimizer like that so as to update only a part of the variables.
 
 .. code-block:: python
 
-    train_weights = network.weights
+    train_weights = network.trainable_weights
     optimizer.apply_gradients(zip(grad, train_weights))
 
 Logging

docs/user/get_start_advance.rst

@@ -36,7 +36,7 @@ Get a part of CNN
   nn = tl.layers.Dense(n_units=100, name='out')(nn)
   model = tl.models.Model(inputs=ni, outputs=nn)
   # train your own classifier (only update the last layer)
-  train_params = model.get_layer('out').weights
+  train_params = model.get_layer('out').all_weights
 
 Reuse CNN
 ------------------

docs/user/get_start_model.rst

@@ -149,11 +149,11 @@ We can get the specific weights by indexing or naming.
 .. code-block:: python
 
   # indexing
-  all_weights = MLP.weights
-  some_weights = MLP.weights[1:3]
+  all_weights = MLP.all_weights
+  some_weights = MLP.all_weights[1:3]
 
   # naming
-  some_weights = MLP.get_layer('dense1').weights
+  some_weights = MLP.get_layer('dense1').all_weights
 
 
 Save and restore model

examples/basic_tutorials/tutorial_cifar10_cnn_static.py

@@ -87,7 +87,7 @@ def get_model_batchnorm(inputs_shape):
 # learning_rate_decay_factor = 0.1
 # num_epoch_decay = 350
 
-train_weights = net.weights
+train_weights = net.trainable_weights
 # learning_rate = tf.Variable(init_learning_rate)
 optimizer = tf.optimizers.Adam(learning_rate)
 

examples/basic_tutorials/tutorial_mnist_mlp_dynamic.py

@@ -46,7 +46,7 @@ def forward(self, x, foo=None):
 n_epoch = 500
 batch_size = 500
 print_freq = 5
-train_weights = MLP.weights
+train_weights = MLP.trainable_weights
 optimizer = tf.optimizers.Adam(learning_rate=0.0001)
 
 ## the following code can help you understand SGD deeply

examples/basic_tutorials/tutorial_mnist_mlp_dynamic_2.py

@@ -65,7 +65,7 @@ def forward(self, x, foo=None):
 n_epoch = 500
 batch_size = 500
 print_freq = 5
-train_weights = MLP1.weights + MLP2.weights
+train_weights = MLP1.trainable_weights + MLP2.trainable_weights
 optimizer = tf.optimizers.Adam(learning_rate=0.0001)
 
 ## the following code can help you understand SGD deeply

examples/basic_tutorials/tutorial_mnist_mlp_static.py

@@ -37,7 +37,7 @@ def get_model(inputs_shape):
 n_epoch = 500
 batch_size = 500
 print_freq = 5
-train_weights = MLP.weights
+train_weights = MLP.trainable_weights
 optimizer = tf.optimizers.Adam(lr=0.0001)
 
 ## the following code can help you understand SGD deeply

examples/basic_tutorials/tutorial_mnist_mlp_static_2.py

@@ -46,7 +46,7 @@ def get_model(inputs_shape, hmodel):
 n_epoch = 500
 batch_size = 500
 print_freq = 5
-train_weights = MLP.weights
+train_weights = MLP.trainable_weights
 optimizer = tf.optimizers.Adam(lr=0.0001)
 
 ## the following code can help you understand SGD deeply