Skip to content

Instantly share code, notes, and snippets.

@weakish

weakish/keras_lstm_tpu.ipynb

Last active November 14, 2018 04:17
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save weakish/53dee45528bb4858a18aa12650a60840 to your computer and use it in GitHub Desktop.
Save weakish/53dee45528bb4858a18aa12650a60840 to your computer and use it in GitHub Desktop.
Download ZIP
Keras_LSTM_TPU.ipynb (change mini batch size from 128 to 128/8=16)
Display the source blob
Display the rendered blob
Raw
keras_lstm_tpu.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Keras_LSTM_TPU.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "TPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/weakish/53dee45528bb4858a18aa12650a60840/keras_lstm_tpu.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "CB43mV-TD1vb",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Tutorial - [How to train Keras model x20 times faster with TPU for free](https://www.dlology.com/blog/how-to-train-keras-model-x20-times-faster-with-tpu-for-free/)"
]
},
{
"metadata": {
"id": "ya06BE0ZU526",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.datasets import imdb\n",
"from tensorflow.keras.preprocessing import sequence\n",
"from tensorflow.python.keras.layers import Input, LSTM, Bidirectional, Dense, Embedding"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "_uSZchXTVOHr",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
},
"outputId": "4886a016-8518-4b58-ca36-3e3278f91322"
},
"cell_type": "code",
"source": [
"\n",
"# Number of words to consider as features\n",
"max_features = 10000\n",
"# Cut texts after this number of words (among top max_features most common words)\n",
"maxlen = 500\n",
"\n",
"# Load data\n",
"(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\n",
"\n",
"# Reverse sequences\n",
"x_train = [x[::-1] for x in x_train]\n",
"x_test = [x[::-1] for x in x_test]\n",
"\n",
"# Pad sequences\n",
"x_train = sequence.pad_sequences(x_train, maxlen=maxlen)\n",
"x_test = sequence.pad_sequences(x_test, maxlen=maxlen)"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
"17465344/17464789 [==============================] - 0s 0us/step\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "p35nSfjbVVBE",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"def make_model(batch_size=None):\n",
" source = Input(shape=(maxlen,), batch_size=batch_size, dtype=tf.int32, name='Input')\n",
" embedding = Embedding(input_dim=max_features, output_dim=128, name='Embedding')(source)\n",
" # lstm = Bidirectional(LSTM(32, name = 'LSTM'), name='Bidirectional')(embedding)\n",
" lstm = LSTM(32, name = 'LSTM')(embedding)\n",
" predicted_var = Dense(1, activation='sigmoid', name='Output')(lstm)\n",
" model = tf.keras.Model(inputs=[source], outputs=[predicted_var])\n",
" model.compile(\n",
" optimizer=tf.train.RMSPropOptimizer(learning_rate=0.01),\n",
" loss='binary_crossentropy',\n",
" metrics=['acc'])\n",
" return model\n",
" "
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "bivVZS0jZhxg",
"colab_type": "code",
"outputId": "fbdb5484-f0e0-46e3-db2d-6af503339b52",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 278
}
},
"cell_type": "code",
"source": [
"tf.keras.backend.clear_session()\n",
"training_model = make_model(batch_size = 128/8)\n",
"training_model.summary()"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"Input (InputLayer) (16, 500) 0 \n",
"_________________________________________________________________\n",
"Embedding (Embedding) (16, 500, 128) 1280000 \n",
"_________________________________________________________________\n",
"LSTM (LSTM) (16, 32) 20608 \n",
"_________________________________________________________________\n",
"Output (Dense) (16, 1) 33 \n",
"=================================================================\n",
"Total params: 1,300,641\n",
"Trainable params: 1,300,641\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "xeGmIQX2aJvw",
"colab_type": "code",
"outputId": "8d4fafb7-1d74-4ead-8f30-79a7ac6fa7b4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 590
}
},
"cell_type": "code",
"source": [
"import os\n",
"# This address identifies the TPU we'll use when configuring TensorFlow.\n",
"TPU_WORKER = 'grpc://' + os.environ['COLAB_TPU_ADDR']\n",
"tf.logging.set_verbosity(tf.logging.INFO)\n",
"\n",
"tpu_model = tf.contrib.tpu.keras_to_tpu_model(\n",
" training_model,\n",
" strategy=tf.contrib.tpu.TPUDistributionStrategy(\n",
" tf.contrib.cluster_resolver.TPUClusterResolver(TPU_WORKER)))\n",
"\n",
"tpu_model.summary()"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"INFO:tensorflow:Querying Tensorflow master (b'grpc://10.68.69.42:8470') for TPU system metadata.\n",
"INFO:tensorflow:Found TPU system:\n",
"INFO:tensorflow:*** Num TPU Cores: 8\n",
"INFO:tensorflow:*** Num TPU Workers: 1\n",
"INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, -1, 17260984861428910334)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 1190064797676527259)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_GPU:0, XLA_GPU, 17179869184, 4948940367157152356)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, 12981579361334909640)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 3383750835275027858)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 17179869184, 16734569315852543769)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 17179869184, 1842442240140948747)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 17179869184, 1141177644839217058)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 17179869184, 6943083046894110387)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 17179869184, 6968958008103944341)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 17179869184, 885304166695756159)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 17179869184, 11658603214034785057)\n",
"WARNING:tensorflow:tpu_model (from tensorflow.contrib.tpu.python.tpu.keras_support) is experimental and may change or be removed at any time, and without warning.\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"Input (InputLayer) (16, 500) 0 \n",
"_________________________________________________________________\n",
"Embedding (Embedding) (16, 500, 128) 1280000 \n",
"_________________________________________________________________\n",
"LSTM (LSTM) (16, 32) 20608 \n",
"_________________________________________________________________\n",
"Output (Dense) (16, 1) 33 \n",
"=================================================================\n",
"Total params: 1,300,641\n",
"Trainable params: 1,300,641\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "XlSm1vd5bteH",
"colab_type": "code",
"outputId": "a397434e-704e-4e8d-83a6-6d416a3af11a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1148
}
},
"cell_type": "code",
"source": [
"import time\n",
"start_time = time.time()\n",
"\n",
"history = tpu_model.fit(x_train, y_train,\n",
" epochs=20,\n",
" batch_size=128,\n",
" validation_split=0.2)\n",
"tpu_model.save_weights('./tpu_model.h5', overwrite=True)\n",
"\n",
"print(\"--- %s seconds ---\" % (time.time() - start_time))\n"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 5000 samples\n",
"Epoch 1/20\n",
"INFO:tensorflow:New input shapes; (re-)compiling: mode=train (# of cores 8), [TensorSpec(shape=(16,), dtype=tf.int32, name='core_id0'), TensorSpec(shape=(16, 500), dtype=tf.int32, name='Input_10'), TensorSpec(shape=(16, 1), dtype=tf.float32, name='Output_target_30')]\n",
"INFO:tensorflow:Overriding default placeholder.\n",
"INFO:tensorflow:Remapping placeholder for Input\n",
"INFO:tensorflow:Started compiling\n",
"INFO:tensorflow:Finished compiling. Time elapsed: 3.27878999710083 secs\n",
"INFO:tensorflow:Setting weights on TPU model.\n",
"24832/25000 [============================>.] - ETA: 0s - loss: 0.6936 - acc: 0.5263INFO:tensorflow:New input shapes; (re-)compiling: mode=train (# of cores 8), [TensorSpec(shape=(5,), dtype=tf.int32, name='core_id0'), TensorSpec(shape=(5, 500), dtype=tf.int32, name='Input_10'), TensorSpec(shape=(5, 1), dtype=tf.float32, name='Output_target_30')]\n",
"INFO:tensorflow:Overriding default placeholder.\n",
"INFO:tensorflow:Remapping placeholder for Input\n",
"INFO:tensorflow:Started compiling\n",
"INFO:tensorflow:Finished compiling. Time elapsed: 5.3668670654296875 secs\n",
"24960/25000 [============================>.] - ETA: 0s - loss: 0.6933 - acc: 0.5269INFO:tensorflow:New input shapes; (re-)compiling: mode=eval (# of cores 8), [TensorSpec(shape=(16,), dtype=tf.int32, name='core_id_10'), TensorSpec(shape=(16, 500), dtype=tf.int32, name='Input_10'), TensorSpec(shape=(16, 1), dtype=tf.float32, name='Output_target_30')]\n",
"INFO:tensorflow:Overriding default placeholder.\n",
"INFO:tensorflow:Remapping placeholder for Input\n",
"INFO:tensorflow:Started compiling\n",
"INFO:tensorflow:Finished compiling. Time elapsed: 6.521803855895996 secs\n",
"INFO:tensorflow:New input shapes; (re-)compiling: mode=eval (# of cores 8), [TensorSpec(shape=(1,), dtype=tf.int32, name='core_id_10'), TensorSpec(shape=(1, 500), dtype=tf.int32, name='Input_10'), TensorSpec(shape=(1, 1), dtype=tf.float32, name='Output_target_30')]\n",
"INFO:tensorflow:Overriding default placeholder.\n",
"INFO:tensorflow:Remapping placeholder for Input\n",
"INFO:tensorflow:Started compiling\n",
"INFO:tensorflow:Finished compiling. Time elapsed: 8.179735898971558 secs\n",
"25000/25000 [==============================] - 60s 2ms/step - loss: 0.6933 - acc: 0.5269 - val_loss: 0.8048 - val_acc: 0.5230\n",
"Epoch 2/20\n",
"25000/25000 [==============================] - 16s 653us/step - loss: 0.5264 - acc: 0.7417 - val_loss: 0.4219 - val_acc: 0.8230\n",
"Epoch 3/20\n",
"25000/25000 [==============================] - 17s 661us/step - loss: 0.2839 - acc: 0.8880 - val_loss: 0.1879 - val_acc: 0.9290\n",
"Epoch 4/20\n",
"25000/25000 [==============================] - 16s 659us/step - loss: 0.2005 - acc: 0.9238 - val_loss: 0.1412 - val_acc: 0.9554\n",
"Epoch 5/20\n",
"25000/25000 [==============================] - 16s 659us/step - loss: 0.1428 - acc: 0.9479 - val_loss: 0.1033 - val_acc: 0.9628\n",
"Epoch 6/20\n",
"25000/25000 [==============================] - 16s 654us/step - loss: 0.1044 - acc: 0.9637 - val_loss: 0.0543 - val_acc: 0.9832\n",
"Epoch 7/20\n",
"25000/25000 [==============================] - 16s 655us/step - loss: 0.0729 - acc: 0.9756 - val_loss: 0.0461 - val_acc: 0.9896\n",
"Epoch 8/20\n",
"25000/25000 [==============================] - 16s 653us/step - loss: 0.0508 - acc: 0.9837 - val_loss: 0.0258 - val_acc: 0.9926\n",
"Epoch 9/20\n",
"25000/25000 [==============================] - 16s 649us/step - loss: 0.0430 - acc: 0.9862 - val_loss: 0.0214 - val_acc: 0.9944\n",
"Epoch 10/20\n",
"25000/25000 [==============================] - 16s 653us/step - loss: 0.0309 - acc: 0.9905 - val_loss: 0.0122 - val_acc: 0.9972\n",
"Epoch 11/20\n",
"25000/25000 [==============================] - 16s 653us/step - loss: 0.0248 - acc: 0.9930 - val_loss: 0.0160 - val_acc: 0.9952\n",
"Epoch 12/20\n",
"25000/25000 [==============================] - 16s 651us/step - loss: 0.0206 - acc: 0.9937 - val_loss: 0.0172 - val_acc: 0.9950\n",
"Epoch 13/20\n",
"25000/25000 [==============================] - 17s 670us/step - loss: 0.0194 - acc: 0.9941 - val_loss: 0.0186 - val_acc: 0.9952\n",
"Epoch 14/20\n",
"25000/25000 [==============================] - 16s 658us/step - loss: 0.0183 - acc: 0.9944 - val_loss: 0.0073 - val_acc: 0.9980\n",
"Epoch 15/20\n",
"25000/25000 [==============================] - 16s 654us/step - loss: 0.0142 - acc: 0.9954 - val_loss: 0.0093 - val_acc: 0.9976\n",
"Epoch 16/20\n",
"25000/25000 [==============================] - 16s 649us/step - loss: 0.0129 - acc: 0.9959 - val_loss: 0.0086 - val_acc: 0.9972\n",
"Epoch 17/20\n",
"25000/25000 [==============================] - 16s 649us/step - loss: 0.0132 - acc: 0.9962 - val_loss: 0.0053 - val_acc: 0.9990\n",
"Epoch 18/20\n",
"25000/25000 [==============================] - 16s 655us/step - loss: 0.0145 - acc: 0.9953 - val_loss: 0.0049 - val_acc: 0.9988\n",
"Epoch 19/20\n",
"25000/25000 [==============================] - 16s 648us/step - loss: 0.0117 - acc: 0.9966 - val_loss: 0.0036 - val_acc: 0.9990\n",
"Epoch 20/20\n",
"25000/25000 [==============================] - 16s 648us/step - loss: 0.0096 - acc: 0.9972 - val_loss: 0.0083 - val_acc: 0.9986\n",
"INFO:tensorflow:Copying TPU weights to the CPU\n",
"--- 376.7751371860504 seconds ---\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "tLjeqllMZzv5",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# history = tpu_model.fit(x_train, y_train,\n",
"# epochs=20,\n",
"# batch_size=128 * 8,\n",
"# validation_split=0.2)\n",
"# tpu_model.save_weights('./tpu_model.h5', overwrite=True)"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "gpcLs6PYatf5",
"colab_type": "code",
"outputId": "4c8ecdca-5a73-4550-e4c1-337cc48bba7d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 278
}
},
"cell_type": "code",
"source": [
"inferencing_model = make_model(batch_size=None)\n",
"inferencing_model.load_weights('./tpu_model.h5')\n",
"inferencing_model.summary()"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"Input (InputLayer) (None, 500) 0 \n",
"_________________________________________________________________\n",
"Embedding (Embedding) (None, 500, 128) 1280000 \n",
"_________________________________________________________________\n",
"LSTM (LSTM) (None, 32) 20608 \n",
"_________________________________________________________________\n",
"Output (Dense) (None, 1) 33 \n",
"=================================================================\n",
"Total params: 1,300,641\n",
"Trainable params: 1,300,641\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "KZyqMpMecAGp",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "2cqqkN84dJQk",
"colab_type": "code",
"outputId": "743f5d12-2e6c-41dc-9c0e-a81b8bd6430e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
}
},
"cell_type": "code",
"source": [
"inferencing_model.evaluate(x_test, y_test)"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"25000/25000 [==============================] - 73s 3ms/step\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0.7590128913450241, 0.85628]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
}
]
},
{
"metadata": {
"id": "XMR_TncEeFKx",
"colab_type": "code",
"outputId": "9ead66f2-209f-4f46-957d-5a1adcf6df97",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 159
}
},
"cell_type": "code",
"source": [
"tpu_model.evaluate(x_test, y_test, batch_size=128)"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"24704/25000 [============================>.] - ETA: 0sINFO:tensorflow:New input shapes; (re-)compiling: mode=eval (# of cores 8), [TensorSpec(shape=(5,), dtype=tf.int32, name='core_id_10'), TensorSpec(shape=(5, 500), dtype=tf.int32, name='Input_10'), TensorSpec(shape=(5, 1), dtype=tf.float32, name='Output_target_30')]\n",
"INFO:tensorflow:Overriding default placeholder.\n",
"INFO:tensorflow:Remapping placeholder for Input\n",
"INFO:tensorflow:Started compiling\n",
"INFO:tensorflow:Finished compiling. Time elapsed: 14.0126211643219 secs\n",
"25000/25000 [==============================] - 25s 984us/step\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0.771998558883667, 0.8562400000572205]"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"metadata": {
"id": "PYCdyFggnKAU",
"colab_type": "code",
"outputId": "d68e3b51-e570-4d1c-d9d4-538ab383e7fe",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
}
},
"cell_type": "code",
"source": [
"tpu_model.evaluate(x_test, y_test, batch_size=128 * 8)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"25000/25000 [==============================] - 2s 94us/step\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0.608636932144165, 0.8271999996948243]"
]
},
"metadata": {
"tags": []
},
"execution_count": 25
}
]
},
{
"metadata": {
"id": "CanGKZdUeOKf",
"colab_type": "code",
"outputId": "c39825ad-9357-4ec2-f8af-9328b0fc6083",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 199
}
},
"cell_type": "code",
"source": [
"inferencing_model.predict(x_test[:10])> 0.5"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[False],\n",
" [ True],\n",
" [ True],\n",
" [ True],\n",
" [ True],\n",
" [ True],\n",
" [ True],\n",
" [False],\n",
" [ True],\n",
" [ True]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
}
]
},
{
"metadata": {
"id": "gVeRjm5AeW-w",
"colab_type": "code",
"outputId": "1db03650-5709-449c-f0dc-d863b47c61f5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"cell_type": "code",
"source": [
"y_test[:10]"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1])"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"metadata": {
"id": "tIw95Yc0eeeY",
"colab_type": "code",
"outputId": "80a74a4a-664d-4937-b242-6a94303e9cca",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 235
}
},
"cell_type": "code",
"source": [
"tpu_model.predict_on_batch(x_train[:128 * 8])>0.5"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"INFO:tensorflow:New input shapes; (re-)compiling: mode=infer, [TensorSpec(shape=(128, 500), dtype=tf.int32, name='Input0')]\n",
"INFO:tensorflow:Overriding default placeholder.\n",
"INFO:tensorflow:Remapping placeholder for Input\n",
"INFO:tensorflow:Started compiling\n",
"INFO:tensorflow:Finished compiling. Time elapsed: 7.4768149852752686 secs\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ True],\n",
" [False],\n",
" [False],\n",
" ...,\n",
" [False],\n",
" [ True],\n",
" [False]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 28
}
]
},
{
"metadata": {
"id": "EiQqm2R_DvIV",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Download the trained model weights to your local file system"
]
},
{
"metadata": {
"id": "UzMykUsdDjiD",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"from google.colab import files\n",
"\n",
"files.download('./tpu_model.h5')"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "apwRGvwWDnau",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment