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maxbadermaxbader
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‎IMDB-keras/IMDB_In_Keras.ipynb

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"cell_type": "code",
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"execution_count": null,
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -36,9 +36,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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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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"(25000,)\n",
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"(25000,)\n"
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]
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}
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],
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"source": [
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"# Loading the data (it's preloaded in Keras)\n",
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"(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=1000)\n",
@@ -59,9 +68,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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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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"[1, 14, 22, 16, 43, 530, 973, 2, 2, 65, 458, 2, 66, 2, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 2, 2, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2, 19, 14, 22, 4, 2, 2, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 2, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2, 2, 16, 480, 66, 2, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 2, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 2, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 2, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 2, 88, 12, 16, 283, 5, 16, 2, 113, 103, 32, 15, 16, 2, 19, 178, 32]\n",
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"1\n"
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]
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}
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],
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"source": [
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"print(x_train[0])\n",
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"print(y_train[0])"
@@ -80,6 +98,62 @@
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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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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]
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}
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],
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"source": [
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"# One-hot encoding the output into vector mode, each of length 1000\n",
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"tokenizer = Tokenizer(num_words=1000)\n",
@@ -97,9 +171,20 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"execution_count": 19,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(25000, 2)\n",
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"(25000, 2)\n"
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]
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}
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],
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"source": [
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"# One-hot encoding the output\n",
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"num_classes = 2\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1000\n"
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]
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}
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],
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"source": [
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"print (x_train.shape[1])"
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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": 29,
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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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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"dense_9 (Dense) (None, 512) 512512 \n",
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"_________________________________________________________________\n",
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"dropout_2 (Dropout) (None, 512) 0 \n",
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"_________________________________________________________________\n",
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"dense_10 (Dense) (None, 2) 1026 \n",
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"=================================================================\n",
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"Total params: 513,538\n",
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"Trainable params: 513,538\n",
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"Non-trainable params: 0\n",
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"_________________________________________________________________\n"
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]
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}
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],
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"source": [
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"# TODO: Build the model architecture\n",
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"\n",
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"# TODO: Compile the model using a loss function and an optimizer.\n"
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"model = Sequential()\n",
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"model.add(Dense(512, activation='relu', input_dim=1000))\n",
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"model.add(Dropout(0.5))\n",
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"model.add(Dense(num_classes, activation='softmax'))\n",
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"model.summary()\n",
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"\n",
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"# Compiling the model using categorical_crossentropy loss, and rmsprop optimizer.\n",
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"model.compile(loss='categorical_crossentropy',\n",
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" optimizer='rmsprop',\n",
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" metrics=['accuracy'])\n"
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]
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},
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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": null,
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"execution_count": 31,
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"metadata": {},
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"outputs": [],
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"outputs": [
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Train on 25000 samples, validate on 25000 samples\n",
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"Epoch 1/10\n",
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" - 5s - loss: 0.4017 - acc: 0.8248 - val_loss: 0.3512 - val_acc: 0.8527\n",
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"Epoch 2/10\n",
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" - 5s - loss: 0.3429 - acc: 0.8610 - val_loss: 0.3430 - val_acc: 0.8600\n",
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"Epoch 3/10\n",
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" - 5s - loss: 0.3235 - acc: 0.8730 - val_loss: 0.3494 - val_acc: 0.8586\n",
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"Epoch 4/10\n",
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" - 5s - loss: 0.3204 - acc: 0.8779 - val_loss: 0.4351 - val_acc: 0.8349\n",
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"Epoch 5/10\n",
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" - 5s - loss: 0.3131 - acc: 0.8816 - val_loss: 0.3816 - val_acc: 0.8612\n",
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"Epoch 6/10\n",
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" - 5s - loss: 0.3031 - acc: 0.8895 - val_loss: 0.4305 - val_acc: 0.8450\n",
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"Epoch 7/10\n",
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" - 5s - loss: 0.2942 - acc: 0.8944 - val_loss: 0.4123 - val_acc: 0.8509\n",
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"Epoch 8/10\n",
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" - 5s - loss: 0.2842 - acc: 0.9013 - val_loss: 0.4251 - val_acc: 0.8597\n",
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"Epoch 9/10\n",
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" - 5s - loss: 0.2718 - acc: 0.9058 - val_loss: 0.4589 - val_acc: 0.8558\n",
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"Epoch 10/10\n",
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" - 5s - loss: 0.2639 - acc: 0.9130 - val_loss: 0.4578 - val_acc: 0.8548\n"
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]
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}
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],
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"source": [
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"# TODO: Run the model. Feel free to experiment with different batch sizes and number of epochs."
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"# TODO: Run the model. Feel free to experiment with different batch sizes and number of epochs.\n",
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"hist = model.fit(x_train, y_train,\n",
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" batch_size=32,\n",
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" epochs=10,\n",
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" validation_data=(x_test, y_test), \n",
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" verbose=2)"
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]
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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": null,
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"execution_count": 32,
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"metadata": {},
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"outputs": [],
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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: 0.8548\n"
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]
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}
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],
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"source": [
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"score = model.evaluate(x_test, y_test, verbose=0)\n",
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"print(\"Accuracy: \", score[1])"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎IMDB-keras/IMDB_In_Keras_Solutions.ipynb

+1-1
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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"version": "3.6.6"
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}
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},
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"nbformat": 4,

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