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使用 Colaboratory 创建
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@tjwei
tjwei committed Sep 2, 2019
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225 changes: 225 additions & 0 deletions LSTM-imdb.ipynb
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{
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"metadata": {
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"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
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"name": "python",
"nbconvert_exporter": "python",
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"version": "3.5.3"
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"colab": {
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"version": "0.3.2",
"provenance": []
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "tecE8g82xhGm",
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},
"source": [
"* https://github.com/fchollet/keras/blob/master/examples/imdb_lstm.py"
]
},
{
"cell_type": "code",
"metadata": {
"id": "cK8bGPjnxhGu",
"colab_type": "code",
"colab": {},
"outputId": "d127f15a-51af-4dae-a375-ce45a338430d"
},
"source": [
"import numpy as np\n",
"from keras.preprocessing import sequence\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Embedding\n",
"from keras.layers import LSTM\n",
"from keras.datasets import imdb"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PD1ijEhhxhG7",
"colab_type": "code",
"colab": {}
},
"source": [
"vocabulary_size = 15000\n",
"(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=vocabulary_size)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "tPSBkgBkxhHE",
"colab_type": "code",
"colab": {}
},
"source": [
"maxlen = 80\n",
"x_train = sequence.pad_sequences(x_train, maxlen=maxlen)\n",
"x_test = sequence.pad_sequences(x_test, maxlen=maxlen)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "C9nqoeMxxhHO",
"colab_type": "code",
"colab": {}
},
"source": [
"model = Sequential()\n",
"model.add(Embedding(vocabulary_size, 128))\n",
"model.add(LSTM(64, dropout=0.2, recurrent_dropout=0.2))\n",
"model.add(Dense(1, activation='sigmoid'))\n",
"model.compile(loss='binary_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Y5S4ZQDPxhHY",
"colab_type": "code",
"colab": {},
"outputId": "47ae875c-f484-4011-d610-4b7eaf70ce82"
},
"source": [
"from IPython.display import SVG, display\n",
"from keras.utils.vis_utils import model_to_dot\n",
"\n",
"SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
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"metadata": {
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{
"cell_type": "code",
"metadata": {
"id": "_Lk8halnxhHh",
"colab_type": "code",
"colab": {},
"outputId": "eaf9595f-0864-427e-d077-a00af93c3123"
},
"source": [
"model.fit(x_train, y_train,\n",
" batch_size=32,\n",
" epochs=8,\n",
" validation_data=(x_test, y_test))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/8\n",
"25000/25000 [==============================] - 66s - loss: 0.4564 - acc: 0.7864 - val_loss: 0.3689 - val_acc: 0.8323\n",
"Epoch 2/8\n",
"25000/25000 [==============================] - 63s - loss: 0.3023 - acc: 0.8754 - val_loss: 0.3948 - val_acc: 0.8256\n",
"Epoch 3/8\n",
"25000/25000 [==============================] - 68s - loss: 0.2303 - acc: 0.9099 - val_loss: 0.4206 - val_acc: 0.8339\n",
"Epoch 4/8\n",
"25000/25000 [==============================] - 66s - loss: 0.1733 - acc: 0.9318 - val_loss: 0.4517 - val_acc: 0.8327\n",
"Epoch 5/8\n",
"25000/25000 [==============================] - 64s - loss: 0.1282 - acc: 0.9525 - val_loss: 0.5009 - val_acc: 0.8264\n",
"Epoch 6/8\n",
"25000/25000 [==============================] - 65s - loss: 0.0940 - acc: 0.9651 - val_loss: 0.6640 - val_acc: 0.8196\n",
"Epoch 7/8\n",
"25000/25000 [==============================] - 66s - loss: 0.0724 - acc: 0.9745 - val_loss: 0.7094 - val_acc: 0.8218\n",
"Epoch 8/8\n",
"25000/25000 [==============================] - 65s - loss: 0.0643 - acc: 0.9775 - val_loss: 0.6907 - val_acc: 0.8200\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7fcdcc55c1d0>"
]
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"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "WldAOPrJxhHp",
"colab_type": "code",
"colab": {},
"outputId": "5f4a7eb7-65ab-48d1-c227-456a1442aad5"
},
"source": [
"score, acc = model.evaluate(x_test, y_test, batch_size=32)\n",
"print('Test score:', score)\n",
"print('Test accuracy:', acc)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"25000/25000 [==============================] - 12s \n",
"Test score: 0.690729214754\n",
"Test accuracy: 0.82004\n"
],
"name": "stdout"
}
]
}
]
}

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