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Ilia Karmanov
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V1.0
K80 and P100
1 parent 804baf7 commit e1f14ba

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‎CNTK_CNN.ipynb

+55-43
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@@ -26,6 +26,16 @@
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Force one-gpu\n",
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"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
@@ -34,9 +44,11 @@
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"OS: linux\n",
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"Python: 3.5.2 |Anaconda custom (64-bit)| (default, Jul 2 2016, 17:53:06) \n",
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"[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
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"Numpy: 1.13.3\n",
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"CNTK: 2.2\n",
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"GPU: ['Tesla K80']\n"
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"Numpy: 1.14.1\n",
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"CNTK: 2.4\n",
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"GPU: ['Tesla P100-PCIE-16GB', 'Tesla P100-PCIE-16GB']\n",
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"CUDA Version 8.0.61\n",
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"CuDNN Version 6.0.21\n"
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]
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}
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],
@@ -45,16 +57,18 @@
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"print(\"Python: \", sys.version)\n",
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"print(\"Numpy: \", np.__version__)\n",
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"print(\"CNTK: \", cntk.__version__)\n",
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"print(\"GPU: \", get_gpu_name())"
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"print(\"GPU: \", get_gpu_name())\n",
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"print(get_cuda_version())\n",
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"print(\"CuDNN Version \", get_cudnn_version())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"def create_symbol():\n",
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"def create_symbol(n_classes=N_CLASSES):\n",
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" # Weight initialiser from uniform distribution\n",
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" # Activation (unless states) is None\n",
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" with cntk.layers.default_options(init = cntk.glorot_uniform(), activation = cntk.relu):\n",
@@ -70,34 +84,34 @@
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" \n",
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" x = Dense(512)(x)\n",
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" x = Dropout(0.5)(x)\n",
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" x = Dense(N_CLASSES, activation=None)(x)\n",
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" x = Dense(n_classes, activation=None)(x)\n",
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" return x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def init_model(m):\n",
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"def init_model(m, labels, lr=LR, momentum=MOMENTUM):\n",
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" # Loss (dense labels); check if support for sparse labels\n",
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" loss = cntk.cross_entropy_with_softmax(m, labels) \n",
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" # Momentum SGD\n",
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" # https://github.com/Microsoft/CNTK/blob/master/Manual/Manual_How_to_use_learners.ipynb\n",
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" # unit_gain=False: momentum_direction = momentum*old_momentum_direction + gradient\n",
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" # if unit_gain=True then ...(1-momentum)*gradient\n",
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" learner = cntk.momentum_sgd(m.parameters,\n",
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" lr=cntk.learning_rate_schedule(LR, cntk.UnitType.minibatch) ,\n",
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" momentum=cntk.momentum_schedule(MOMENTUM), \n",
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" lr=cntk.learning_rate_schedule(lr, cntk.UnitType.minibatch) ,\n",
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" momentum=cntk.momentum_schedule(momentum), \n",
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" unit_gain=False)\n",
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" trainer = cntk.Trainer(m, (loss, cntk.classification_error(m, labels)), [learner])\n",
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" return trainer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
@@ -108,8 +122,8 @@
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"Preparing test set...\n",
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"(50000, 3, 32, 32) (10000, 3, 32, 32) (50000, 10) (10000, 10)\n",
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"float32 float32 float32 float32\n",
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"CPU times: user 833 ms, sys: 553 ms, total: 1.39 s\n",
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"Wall time: 1.38 s\n"
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"CPU times: user 671 ms, sys: 576 ms, total: 1.25 s\n",
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"Wall time: 1.25 s\n"
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]
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}
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],
@@ -126,15 +140,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 22.6 ms, sys: 28.6 ms, total: 51.2 ms\n",
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"Wall time: 76.1 ms\n"
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"CPU times: user 24 ms, sys: 32 ms, total: 56 ms\n",
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"Wall time: 69 ms\n"
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]
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}
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],
@@ -149,51 +163,50 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 72.1 ms, sys: 224 ms, total: 297 ms\n",
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"Wall time: 303 ms\n"
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"CPU times: user 119 ms, sys: 116 ms, total: 235 ms\n",
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"Wall time: 236 ms\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"trainer = init_model(sym)"
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"trainer = init_model(sym, labels)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1 | Accuracy: 0.562500\n",
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"Epoch 1 | Accuracy: 0.468750\n",
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"Epoch 2 | Accuracy: 0.640625\n",
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"Epoch 3 | Accuracy: 0.625000\n",
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"Epoch 4 | Accuracy: 0.703125\n",
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"Epoch 5 | Accuracy: 0.703125\n",
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"Epoch 6 | Accuracy: 0.765625\n",
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"Epoch 7 | Accuracy: 0.859375\n",
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"Epoch 8 | Accuracy: 0.796875\n",
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"Epoch 9 | Accuracy: 0.781250\n",
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"Epoch 10 | Accuracy: 0.796875\n",
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"CPU times: user 2min 19s, sys: 21.4 s, total: 2min 40s\n",
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"Wall time: 2min 43s\n"
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"Epoch 3 | Accuracy: 0.609375\n",
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"Epoch 4 | Accuracy: 0.578125\n",
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"Epoch 5 | Accuracy: 0.812500\n",
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"Epoch 6 | Accuracy: 0.781250\n",
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"Epoch 7 | Accuracy: 0.671875\n",
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"Epoch 8 | Accuracy: 0.843750\n",
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"Epoch 9 | Accuracy: 0.796875\n",
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"Epoch 10 | Accuracy: 0.843750\n",
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"CPU times: user 40.3 s, sys: 13.1 s, total: 53.3 s\n",
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"Wall time: 53.2 s\n"
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]
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}
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],
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"source": [
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"%%time \n",
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"# 163s\n",
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"# Train model\n",
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"# Main training loop: 53s\n",
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"for j in range(EPOCHS):\n",
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" for data, label in yield_mb(x_train, y_train, BATCHSIZE, shuffle=True):\n",
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" trainer.train_minibatch({features: data, labels: label})\n",
@@ -204,22 +217,21 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 850 ms, sys: 337 ms, total: 1.19 s\n",
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"Wall time: 1.4 s\n"
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"CPU times: user 291 ms, sys: 88.9 ms, total: 379 ms\n",
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"Wall time: 408 ms\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"# Predict and then score accuracy\n",
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"# (We don't need softmax -> monotonic function)\n",
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"# Main evaluation loop: 343ms\n",
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"n_samples = (y_test.shape[0]//BATCHSIZE)*BATCHSIZE\n",
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"y_guess = np.zeros(n_samples, dtype=np.int)\n",
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"y_truth = np.argmax(y_test[:n_samples], axis=-1)\n",
@@ -232,14 +244,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy: 0.780649038462\n"
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"Accuracy: 0.7701322115384616\n"
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]
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [default]",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},

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