Solution
Sandeep Kumar answered on
Apr 16 2021
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"kernelspec": {
"name": "python394jvsc74a57bd081118431cc388d258ed977b65143603a98f8ad6ed776c173758a3af876bc6de9",
"display_name": "Python 3.9.4 64-bit"
},
"language_info": {
"codemi
or_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.4-final"
},
"colab": {
"name": "IMDB movie review Modeling DNN CNN LSTM Keras.ipynb",
"provenance": []
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "yJDfTCHvULWg"
},
"source": [
"from keras.datasets import imdb\n",
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"from matplotlib import cm\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import os\n",
"import time"
],
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0Lf43x1oULW7"
},
"source": [
"from keras.preprocessing import sequence\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Activation\n",
"from keras.layers import Embedding\n",
"from keras.layers import Conv1D, GlobalMaxPooling1D\n",
"from keras.callbacks import EarlyStopping\n",
"from keras import models"
],
"execution_count": 29,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https:
localhost:8080/",
"height": 0
},
"id": "9Z2RhfpPULW-",
"outputId": "dffec856-2810-49de-b253-5c0d3
524e6"
},
"source": [
"\n",
"(X_train, y_train), (X_test, y_test) = imdb.load_data()\n",
"X = np.concatenate((X_train, X_test), axis=0)\n",
"y = np.concatenate((y_train, y_test), axis=0)"
],
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"text": [
":6: VisibleDeprecationWarning: Creating an nda
ay from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or nda
ays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the nda
ay\n",
"/us
local/li
python3.7/dist-packages/tensorflow/python/keras/datasets/imdb.py:159: VisibleDeprecationWarning: Creating an nda
ay from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or nda
ays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the nda
ay\n",
" x_train, y_train = np.a
ay(xs[:idx]), np.a
ay(labels[:idx])\n",
"/us
local/li
python3.7/dist-packages/tensorflow/python/keras/datasets/imdb.py:160: VisibleDeprecationWarning: Creating an nda
ay from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or nda
ays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the nda
ay\n",
" x_test, y_test = np.a
ay(xs[idx:]), np.a
ay(labels[idx:])\n"
],
"name": "stde
"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https:
localhost:8080/",
"height": 0
},
"id": "JJXh9NkDULW_",
"outputId": "27ee5cf9-43e0-4501-acdb-532c3e635253"
},
"source": [
"##training data shape review\n",
"print(\"Training data: \")\n",
"print(X.shape)\n",
"print(y.shape)\n",
"print(\"Classes: \")\n",
"print(np.unique(y))"
],
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"text": [
"Training data: \n",
"(50000,)\n",
"(50000,)\n",
"Classes: \n",
"[0 1]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https:
localhost:8080/",
"height": 0
},
"id": "GnKYq1bVULXB",
"outputId": "0adaa77d-9b67-4e58-ad7d-1f62da898acb"
},
"source": [
"print(\"Number of words: \")\n",
"print(len(np.unique(np.hstack(X))))"
],
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"text": [
"Number of words: \n",
"88585\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https:
localhost:8080/",
"height": 299
},
"id": "ffaOSrvEULXC",
"outputId": "10a0455a-d45f-4a7b-8c0c-dc1456211e71"
},
"source": [
"print(\"Review length: \")\n",
"result = [len(x) for x in X]\n",
"print(\"Mean %.2f words (%f)\" % (np.mean(result), np.std(result)))\n",
"# plot review length\n",
"plt.boxplot(result)\n",
"plt.show()"
],
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"text": [
"Review length: \n",
"Mean 234.76 words (172.911495)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
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"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "lRyNiW0sULXE"
},
"source": [
"#####Analysis#####"
],
"execution_count": 34,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https:
localhost:8080/",
"height": 0
},
"id": "6gnnvsvvULXF",
"outputId": "ff1c4c17-c31d-44c1-c2e5-524a636ff310"
},
"source": [
"(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=5000)"
],
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"text": [
":6: VisibleDeprecationWarning: Creating an nda
ay from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or nda
ays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the nda
ay\n",
"/us
local/li
python3.7/dist-packages/tensorflow/python/keras/datasets/imdb.py:159: VisibleDeprecationWarning: Creating an nda
ay from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or nda
ays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the nda
ay\n",
" x_train, y_train = np.a
ay(xs[:idx]), np.a
ay(labels[:idx])\n",
"/us
local/li
python3.7/dist-packages/tensorflow/python/keras/datasets/imdb.py:160: VisibleDeprecationWarning: Creating an nda
ay from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or nda
ays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the nda
ay\n",
" x_test, y_test = np.a
ay(xs[idx:]), np.a
ay(labels[idx:])\n"
],
"name": "stde
"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "JBanO5PMULXG"
},
"source": [
"def vectorize_sequences(sequences, dimension=5000):\n",
" # Create an all-zero matrix of shape (len(sequences), dimension)\n",
" results = np.zeros((len(sequences), dimension))\n",
" for i, sequence in enumerate(sequences):\n",
" results[i, sequence] = 1. # set specific indices of results[i] to 1s\n",
" return results\n",
"\n",
"# Our vectorized training data\n",
"x_train = vectorize_sequences(train_data)\n",
"# Our vectorized test data\n",
"x_test = vectorize_sequences(test_data)"
],
"execution_count": 36,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "m2UgUmlGULXI"
},
"source": [
"# Our vectorized labels one-hot encoder\n",
"y_train = np.asa
ay(train_labels).astype('float32')\n",
"y_test = np.asa
ay(test_labels).astype('float32')"
],
"execution_count": 37,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "995zJLPtULXJ"
},
"source": [
"from keras import layers\n",
"from keras import models\n",
"\n",
"model = models.Sequential()\n",
"model.add(layers.Dense(32, activation='relu', input_shape=(5000,)))\n",
"model.add(layers.Dense(32, activation='relu',))\n",
"model.add(layers.Dense(1, activation='sigmoid'))"
],
"execution_count": 38,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "u6V3sbfeULXK"
},
"source": [
"#Set validation set aside\n",
"\n",
"x_val = x_train[:10000]\n",
"partial_x_train = x_train[10000:]\n",
"\n",
"y_val = y_train[:10000]\n",
"partial_y_train = y_train[10000:]"
],
"execution_count": 39,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "gUl463vOULXL"
},
"source": [
"model.compile(optimizer='adam',\n",
" loss='binary_crossentropy',\n",
" metrics=['acc'])"
],
"execution_count": 40,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https:
localhost:8080/",
"height": 0
},
"id": "Q41E98NWULXM",
"outputId": "f5a27d70-0896-4f4a-cfe3-54d3642afb56"
},
"source": [
"start_time_m1 = time.time()\n",
"history = model.fit(partial_x_train,\n",
" partial_y_train,\n",
" epochs=20,\n",
" batch_size=512,\n",
" validation_data=(x_val, y_val))\n",
"total_time_m1 = time.time() - start_time_m1\n",
"\n",
"print(\"The Dense Convolutional Neural Network 1 layer took %.4f seconds to train.\" % (total_time_m1))"
],
"execution_count": 41,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/20\n",
"30/30 [==============================] - 1s 34ms/step - loss: 0.6197 - acc: 0.6813 - val_loss: 0.3706 - val_acc: 0.8568\n",
"Epoch 2/20\n",
"30/30 [==============================] - 1s 25ms/step - loss: 0.3011 - acc: 0.8871 - val_loss: 0.2959 - val_acc: 0.8813\n",
"Epoch 3/20\n",
"30/30 [==============================] - 1s 25ms/step - loss: 0.2059 - acc: 0.9258 - val_loss: 0.3023 - val_acc: 0.8825\n",
"Epoch 4/20\n",
"30/30 [==============================] - 1s 44ms/step - loss: 0.1605 - acc: 0.9426 - val_loss: 0.3152 - val_acc: 0.8759\n",
"Epoch 5/20\n",
"30/30 [==============================] - 1s 25ms/step - loss: 0.1310 - acc: 0.9543 - val_loss: 0.3386 - val_acc: 0.8757\n",
"Epoch 6/20\n",
"30/30 [==============================] - 1s 24ms/step - loss: 0.1130 - acc: 0.9656 - val_loss: 0.3741 - val_acc: 0.8709\n",
"Epoch 7/20\n",
"30/30 [==============================] - 1s 24ms/step - loss: 0.0872 - acc: 0.9734 - val_loss: 0.3975 - val_acc: 0.8658\n",
"Epoch 8/20\n",
"30/30 [==============================] - 1s 25ms/step - loss: 0.0708 - acc: 0.9801 - val_loss: 0.4328 - val_acc: 0.8649\n",
"Epoch 9/20\n",
"30/30 [==============================] - 1s 25ms/step - loss: 0.0507 - acc: 0.9895 - val_loss: 0.4745 - val_acc: 0.8625\n",
"Epoch 10/20\n",
"30/30 [==============================] - 1s 24ms/step - loss: 0.0345 - acc: 0.9959 - val_loss: 0.5124 - val_acc: 0.8636\n",
"Epoch 11/20\n",
"30/30 [==============================] - 1s 24ms/step - loss: 0.0260 - acc: 0.9973 - val_loss: 0.5605 - val_acc: 0.8575\n",
"Epoch 12/20\n",
"30/30 [==============================] - 1s 24ms/step - loss: 0.0199 - acc: 0.9990 - val_loss: 0.5915 - val_acc: 0.8603\n",
"Epoch 13/20\n",
"30/30 [==============================] - 1s 25ms/step - loss: 0.0136 - acc: 0.9994 - val_loss: 0.6290 - val_acc: 0.8579\n",
"Epoch 14/20\n",
"30/30 [==============================] - 1s 24ms/step - loss: 0.0100 - acc: 0.9994 - val_loss: 0.6548 - val_acc: 0.8587\n",
"Epoch 15/20\n",
"30/30 [==============================] - 1s 24ms/step - loss: 0.0074 - acc: 0.9999 - val_loss: 0.6876 - val_acc: 0.8575\n",
"Epoch 16/20\n",
"30/30 [==============================] - 1s 24ms/step - loss: 0.0058 - acc: 1.0000 - val_loss: 0.7081 - val_acc: 0.8595\n",
"Epoch 17/20\n",
"30/30 [==============================] - 1s 24ms/step - loss: 0.0044 - acc: 0.9999 - val_loss: 0.7309 - val_acc: 0.8581\n",
"Epoch 18/20\n",
"30/30 [==============================] - 1s 24ms/step - loss: 0.0037 - acc: 1.0000 - val_loss: 0.7539 - val_acc: 0.8577\n",
"Epoch 19/20\n",
"30/30 [==============================] - 1s 24ms/step - loss: 0.0030 - acc: 1.0000 - val_loss: 0.7717 - val_acc: 0.8574\n",
"Epoch 20/20\n",
"30/30 [==============================] - 1s 25ms/step - loss: 0.0026 - acc: 1.0000 - val_loss: 0.7911 - val_acc: 0.8573\n",
"The Dense Convolutional Neural Network 1 layer took 16.1613 seconds to train.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https:
localhost:8080/",
"height": 0
},
"id": "t5teUXrBULXN",
"outputId": "7d47241e-9c94-410d-e275-e13c643ef5a4"
},
"source": [
"history_dict = history.history\n",
"history_dict.keys()"
],
"execution_count": 42,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])"
]
},
"metadata": {
"tags": []
},
"execution_count": 42
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https:
localhost:8080/",
"height": 295
},
"id": "DU2YhCTQULXO",
"outputId": "c6029e3d-6e18-4dd3-cac5-c41a97376f9c"
},
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"acc = history.history['acc']\n",
"val_acc = history.history['val_acc']\n",
"loss = history.history['loss']\n",
"val_loss = history.history['val_loss']\n",
"\n",
"epochs = range(1, len(acc) + 1)\n",
"\n",
"# \"bo\" is for \"blue dot\"\n",
"plt.plot(epochs, loss, 'bo', label='Training loss')\n",
"# b is for \"solid blue line\"\n",
"plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
"plt.title('Training and validation loss')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Loss')\n",
"plt.legend()\n",
"\n",
"plt.show()"
],
"execution_count": 43,
"outputs": [
{
"output_type": "display_data",
"data": {
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"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https:
localhost:8080/",
"height": 295
},
"id": "ANUlU5ttULXQ",
"outputId": "33509dc2-ab45-4916-e33f-c2fa4904bccd"
},
"source": [
"plt.clf() # clear figure\n",
"acc_values = history_dict['acc']\n",
"val_acc_values = history_dict['val_acc']\n",
"\n",
"plt.plot(epochs, acc, 'bo', label='Training acc')\n",
"plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
"plt.title('Training and validation accuracy')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Loss')\n",
"plt.legend()\n",
"\n",
"plt.show()"
],
"execution_count": 44,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png":...