amazon.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"amazon.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"1T3yvf0VDoXK","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"f6b85071-9ec0-4004-af77-4afb2bac7dc7","executionInfo":{"status":"ok","timestamp":1558451462760,"user_tz":-270,"elapsed":4447,"user":{"displayName":"Navid Adelpour","photoUrl":"https://lh3.googleusercontent.com/-mAcQMj8zeb8/AAAAAAAAAAI/AAAAAAAAAwA/8VuZFUj2GbU/s64/photo.jpg","userId":"00725423770723278141"}}},"source":["import os\n","import sys\n","import pandas\n","import numpy as np\n","import json\n","from keras import optimizers\n","from keras.utils import np_utils\n","from google.colab import drive\n","from keras.callbacks import LearningRateScheduler\n","from keras.models import  Sequential\n","from keras.layers import  Embedding, Dense, Activation, Conv1D, GlobalAveragePooling1D, Flatten\n","from sklearn.model_selection import train_test_split\n","import matplotlib.pyplot as plt"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"iJ7iIe4zEp9U","colab_type":"code","outputId":"a09ca795-e403-4362-8fcb-e621ff98afca","executionInfo":{"status":"ok","timestamp":1558451462769,"user_tz":-270,"elapsed":4372,"user":{"displayName":"Navid Adelpour","photoUrl":"https://lh3.googleusercontent.com/-mAcQMj8zeb8/AAAAAAAAAAI/AAAAAAAAAwA/8VuZFUj2GbU/s64/photo.jpg","userId":"00725423770723278141"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["drive.mount('/content/gdrive/')\n","\n","root_path = '/content/gdrive/My Drive/Colab Notebooks/nn-homework1/amazon'\n","sys.path.append(root_path)\n","\n","dictionary_path = os.path.join(root_path, '/dictionary.json')"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/gdrive/; to attempt to forcibly remount, call drive.mount(\"/content/gdrive/\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"p7PvRaRIztAS","colab_type":"code","colab":{}},"source":["train = pandas.read_csv(os.path.join(root_path, 'amazon.csv')).values"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"xwbp26RV8pCY","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"cb8df2e0-4609-4b90-dc8b-a226b230bf94","executionInfo":{"status":"ok","timestamp":1558451580824,"user_tz":-270,"elapsed":1499,"user":{"displayName":"Navid Adelpour","photoUrl":"https://lh3.googleusercontent.com/-mAcQMj8zeb8/AAAAAAAAAAI/AAAAAAAAAwA/8VuZFUj2GbU/s64/photo.jpg","userId":"00725423770723278141"}}},"source":[""],"execution_count":10,"outputs":[{"output_type":"stream","text":["1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"esa10v2u4aVo","colab_type":"code","colab":{}},"source":["x_raw = train[:, 9]\n","y_raw = train[:, 10]\n","\n","# x_raw = np.array(x_raw)\n","# y_raw = np.array(y_raw)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9-q8Mu23lFqr","colab_type":"code","colab":{}},"source":["dictionary = {}\n","j = 1\n","for i in range(len(x_raw)):\n","  sentence = x_raw[i].split(\" \")\n","  for word in sentence:\n","    if not word in dictionary.keys():\n","      dictionary[word] = j\n","      j += 1"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"5yvn5DhCwZP4","colab_type":"code","colab":{}},"source":["with open(dictionary_path, 'w') as f:\n","  f.write(json.dumps(dictionary))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jWPydZSxxc9C","colab_type":"code","colab":{}},"source":["with open(dictionary_path, 'r') as f:\n","  dictionary = json.loads(f.read())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"OH5N_hqXyGR5","colab_type":"code","colab":{}},"source":["x_raw_indexed = np.zeros((len(x_raw), 500))\n","for i in range(len(x_raw)):\n","  sliced_array = np.array(list(map(lambda x: dictionary[x], x_raw[i].split(\" \"))))[:500]\n","  x_raw_indexed[i] = np.pad(sliced_array, (0, 500 - len(sliced_array)), 'constant')\n","  \n","x_raw_indexed = np.array(x_raw_indexed)\n","y_raw = np.array(y_raw)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dFQ5pbNSzcVs","colab_type":"code","colab":{}},"source":["x_train, x_test, y_train, y_test = train_test_split(x_raw_indexed, y_raw, test_size = .2)\n","# print(x_train.shape)\n","# print(y_train.shape)\n","# print(x_test.shape)\n","# print(y_test.shape)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"W8D7eWDR0ZJL","colab_type":"code","colab":{}},"source":["# hyper parameters\n","epochs = 10\n","batch_size = 32\n","learning_rate = 0.01\n","momentum = 0.5\n","decay = 0.5"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"hcgY5IW80WK7","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":88},"outputId":"1083dd2c-1eae-416d-9127-40a0e03aa058","executionInfo":{"status":"ok","timestamp":1558451703806,"user_tz":-270,"elapsed":1510,"user":{"displayName":"Navid Adelpour","photoUrl":"https://lh3.googleusercontent.com/-mAcQMj8zeb8/AAAAAAAAAAI/AAAAAAAAAwA/8VuZFUj2GbU/s64/photo.jpg","userId":"00725423770723278141"}}},"source":["sgd = optimizers.SGD(lr=learning_rate, momentum=momentum)\n","scheduler = LearningRateScheduler(\n","    lambda epoch: learning_rate * 1/(1 + decay * epoch)\n",")"],"execution_count":18,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Colocations handled automatically by placer.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"7_SsgvRVEqan","colab_type":"code","outputId":"a25d4122-435c-494a-f3a5-2e864a1168cd","executionInfo":{"status":"ok","timestamp":1558451709885,"user_tz":-270,"elapsed":3384,"user":{"displayName":"Navid Adelpour","photoUrl":"https://lh3.googleusercontent.com/-mAcQMj8zeb8/AAAAAAAAAAI/AAAAAAAAAwA/8VuZFUj2GbU/s64/photo.jpg","userId":"00725423770723278141"}},"colab":{"base_uri":"https://localhost:8080/","height":544}},"source":["model = Sequential()\n","\n","model.add(Embedding(output_dim = 100, input_dim=len(dictionary) + 1))\n","\n","model.add(Conv1D(64, 3))\n","model.add(Activation('relu'))\n","\n","model.add(Conv1D(32, 3))\n","model.add(Activation('relu'))\n","\n","model.add(Conv1D(16, 3))\n","model.add(Activation('relu'))\n","\n","model.add(GlobalAveragePooling1D())\n","\n","model.add(Dense(12))\n","model.add(Activation('relu'))\n","\n","model.add(Dense(1))\n","model.add(Activation('sigmoid'))\n","\n","model.compile(\n","    loss=\"binary_crossentropy\",\n","    optimizer=sgd,\n","    metrics=['accuracy'],\n",")\n","\n","model.summary()"],"execution_count":19,"outputs":[{"output_type":"stream","text":["_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","embedding_1 (Embedding)      (None, None, 100)         14220300  \n","_________________________________________________________________\n","conv1d_1 (Conv1D)            (None, None, 64)          19264     \n","_________________________________________________________________\n","activation_1 (Activation)    (None, None, 64)          0         \n","_________________________________________________________________\n","conv1d_2 (Conv1D)            (None, None, 32)          6176      \n","_________________________________________________________________\n","activation_2 (Activation)    (None, None, 32)          0         \n","_________________________________________________________________\n","conv1d_3 (Conv1D)            (None, None, 16)          1552      \n","_________________________________________________________________\n","activation_3 (Activation)    (None, None, 16)          0         \n","_________________________________________________________________\n","global_average_pooling1d_1 ( (None, 16)                0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 12)                204       \n","_________________________________________________________________\n","activation_4 (Activation)    (None, 12)                0         \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 1)                 13        \n","_________________________________________________________________\n","activation_5 (Activation)    (None, 1)                 0         \n","=================================================================\n","Total params: 14,247,509\n","Trainable params: 14,247,509\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"EGrcHtsw090U","colab_type":"code","outputId":"b7fab302-7736-4a62-bdcc-9688094e04fe","executionInfo":{"status":"ok","timestamp":1558452583562,"user_tz":-270,"elapsed":3853,"user":{"displayName":"Navid Adelpour","photoUrl":"https://lh3.googleusercontent.com/-mAcQMj8zeb8/AAAAAAAAAAI/AAAAAAAAAwA/8VuZFUj2GbU/s64/photo.jpg","userId":"00725423770723278141"}},"colab":{"base_uri":"https://localhost:8080/","height":445}},"source":["history = model.fit(\n","    x_train, y_train,\n","    batch_size = batch_size, epochs = epochs,\n","    validation_data = (x_test, y_test),\n","    callbacks=[scheduler]\n",")"],"execution_count":20,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use tf.cast instead.\n","Train on 40000 samples, validate on 10000 samples\n","Epoch 1/10\n","40000/40000 [==============================] - 31s 779us/step - loss: 0.4515 - acc: 0.8349 - val_loss: 0.4442 - val_acc: 0.8371\n","Epoch 2/10\n","40000/40000 [==============================] - 28s 694us/step - loss: 0.4457 - acc: 0.8354 - val_loss: 0.4434 - val_acc: 0.8371\n","Epoch 3/10\n","40000/40000 [==============================] - 28s 697us/step - loss: 0.4456 - acc: 0.8354 - val_loss: 0.4434 - val_acc: 0.8371\n","Epoch 4/10\n","40000/40000 [==============================] - 28s 688us/step - loss: 0.4455 - acc: 0.8354 - val_loss: 0.4433 - val_acc: 0.8371\n","Epoch 5/10\n","40000/40000 [==============================] - 28s 697us/step - loss: 0.4454 - acc: 0.8354 - val_loss: 0.4433 - val_acc: 0.8371\n","Epoch 6/10\n","40000/40000 [==============================] - 28s 692us/step - loss: 0.4453 - acc: 0.8354 - val_loss: 0.4433 - val_acc: 0.8371\n","Epoch 7/10\n","40000/40000 [==============================] - 28s 693us/step - loss: 0.4453 - acc: 0.8354 - val_loss: 0.4433 - val_acc: 0.8371\n","Epoch 8/10\n","40000/40000 [==============================] - 27s 665us/step - loss: 0.4453 - acc: 0.8354 - val_loss: 0.4435 - val_acc: 0.8371\n","Epoch 9/10\n","40000/40000 [==============================] - 27s 674us/step - loss: 0.4453 - acc: 0.8354 - val_loss: 0.4434 - val_acc: 0.8371\n","Epoch 10/10\n","40000/40000 [==============================] - 27s 676us/step - loss: 0.4453 - acc: 0.8354 - val_loss: 0.4434 - val_acc: 0.8371\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"0h861bupkEBa","colab_type":"code","outputId":"e308c82b-e5f5-464b-e451-722c8cacf828","executionInfo":{"status":"ok","timestamp":1558453094085,"user_tz":-270,"elapsed":2224,"user":{"displayName":"Navid Adelpour","photoUrl":"https://lh3.googleusercontent.com/-mAcQMj8zeb8/AAAAAAAAAAI/AAAAAAAAAwA/8VuZFUj2GbU/s64/photo.jpg","userId":"00725423770723278141"}},"colab":{"base_uri":"https://localhost:8080/","height":1225}},"source":["#  plot accuracy\n","plt.figure(figsize=(20, 10))\n","plt.plot(history.history['acc'])\n","plt.plot(history.history['val_acc'])\n","\n","plt.title('model accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(\n","    [\n","        'train', 'test',\n","    ],\n","    loc='lower right')\n","\n","plt.show()\n","\n","#  plot accuracy\n","plt.figure(figsize=(20, 10))\n","plt.plot(history.history['loss'])\n","plt.plot(history.history['val_loss'])\n","\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(\n","    [\n","        'train', 'test',\n","    ],\n","    loc='lower right')\n","\n","plt.show()\n"],"execution_count":22,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABKUAAAJcCAYAAAAo8BegAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntXZta21lkk8mefZa+7Qk2wyf5ya5OUlaayuGAJUkc4b9UlVzk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WFr7cOttXmttXk77bTT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wifGJZ6GqVPPZIfz7/9KGMjE82XQ4AAAAwQ/jEsjHY7s+Zscn8zne03gEAAMBiIXxi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\nDJ8kSZIkSZKUG8MnSZIkSZIk5eb/A85MOxgffAc8AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 1440x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"1lLFc_H7mc5i","colab_type":"code","outputId":"e778fc15-9f0c-4002-e85a-73819d700d3a","executionInfo":{"status":"ok","timestamp":1558453105019,"user_tz":-270,"elapsed":2411,"user":{"displayName":"Navid Adelpour","photoUrl":"https://lh3.googleusercontent.com/-mAcQMj8zeb8/AAAAAAAAAAI/AAAAAAAAAwA/8VuZFUj2GbU/s64/photo.jpg","userId":"00725423770723278141"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["scores = model.evaluate(x_test, y_test, verbose=0)\n","print('accuracy:', scores[1] * 100)"],"execution_count":23,"outputs":[{"output_type":"stream","text":["accuracy: 83.71\n"],"name":"stdout"}]}]}