deep_learning.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"deep_learning.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"fzn4jvPKNJkE","colab_type":"text"},"source":["# Keras Neural Network Models\n","\n","## Set up and Preprocessing\n","Import relevant packages and download the dataset from Kaggle. Unzip the data, create a directory on the Google VM's Linux server, and save the data there."]},{"cell_type":"code","metadata":{"id":"p7yKOOLS9wnP","colab_type":"code","outputId":"6ed399cc-405a-401f-a80f-bf9383713e6a","executionInfo":{"status":"ok","timestamp":1575076210759,"user_tz":300,"elapsed":3442,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":83}},"source":["import json, os, zipfile\n","import pandas as pd\n","import numpy as np\n","from sklearn.model_selection import train_test_split\n","from keras.preprocessing.text import Tokenizer\n","from keras.preprocessing.sequence import pad_sequences\n","from keras.models import Sequential\n","from keras.layers import Embedding, Dense, Conv1D, MaxPooling1D, Flatten, Dropout\n","from keras import optimizers\n","from keras.callbacks import EarlyStopping\n","import matplotlib.pyplot as plt\n","from google.colab import files"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"},{"output_type":"display_data","data":{"text/html":["<p style=\"color: red;\">\n","The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n","We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n","or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n","<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"I8GZS_6HK1FS","colab_type":"code","colab":{}},"source":["api_token = ... #REDACTED\n","!mkdir ~/.kaggle\n","!cd ~/.kaggle\n","!touch kaggle.json\n","\n","with open('/root/.kaggle/kaggle.json', 'w') as file:\n","    json.dump(api_token, file)\n","\n","!chmod 600 ~/.kaggle/kaggle.json"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"dnDWlTmQK5r0","colab_type":"code","outputId":"70565490-968d-483e-e6a9-e25e9d5c0862","executionInfo":{"status":"ok","timestamp":1575076235577,"user_tz":300,"elapsed":26737,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":90}},"source":["!mkdir content\n","!kaggle datasets download rouzbehm/jigsawdataset -p content\n","!ls content\n","\n","\n","os.chdir('content')\n","for file in os.listdir():\n","    zip_ref = zipfile.ZipFile(file, 'r')\n","    zip_ref.extractall()\n","    zip_ref.close()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Downloading jigsawdataset.zip to content\n","100% 48.9M/48.9M [00:02<00:00, 21.1MB/s]\n","\n","jigsawdataset.zip\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ex08hOYiK7k0","colab_type":"code","outputId":"98760c15-3bd0-4315-9098-18c08e80a464","executionInfo":{"status":"ok","timestamp":1575076259301,"user_tz":300,"elapsed":4396,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["!ls"],"execution_count":0,"outputs":[{"output_type":"stream","text":["data.csv  jigsawdataset.zip\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gIC2aX-9NqLQ","colab_type":"text"},"source":["## Data Preprocessing\n","Split the data into train, test, and validation sets, with the 90:5:5 allocation. Then use the Keras Tokenizer to generate a dictionary consisting of each word in the dataset (in lower case), after removing the following characters: !\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n. The tokenizer replaces each word in each sentence with an integer that is the index in the dictionary. Finally, pad the ends of each sentence with 0s such that each sentence has the same length."]},{"cell_type":"code","metadata":{"id":"TSNmWGHsNIHF","colab_type":"code","colab":{}},"source":["data = pd.read_csv(\"data.csv\")\n","X_train_raw, X_test_raw, y_train, y_test = train_test_split(data[\"comment_text\"], data[\"unsafe\"], test_size = 0.1, random_state = 123)\n","X_test_raw, X_validation_raw, y_test, y_validation = train_test_split(X_test_raw, y_test, test_size = 0.5, random_state = 123)\n","\n","max_len = max(len(x.split()) for x in data[\"comment_text\"]) #length of longest sentence"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"oECd8e62OCaG","colab_type":"code","colab":{}},"source":["tokenizer = Tokenizer()\n","tokenizer.fit_on_texts(X_train_raw)\n","\n","X_train_tokenized = tokenizer.texts_to_sequences(X_train_raw)\n","X_test_tokenized = tokenizer.texts_to_sequences(X_test_raw)\n","X_validation_tokenized = tokenizer.texts_to_sequences(X_validation_raw)\n","\n","X_train = pad_sequences(X_train_tokenized, padding='post', maxlen = max_len)\n","X_test = pad_sequences(X_test_tokenized, padding='post', maxlen = max_len)\n","X_validation = pad_sequences(X_validation_tokenized, padding='post', maxlen = max_len)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"_c41tFB2SKYS","colab_type":"text"},"source":["Define methods to calculate the accuracy, precision, and recall, and plot the validation loss and accuracy against the training loss and accuracy"]},{"cell_type":"code","metadata":{"id":"CuTwWFdBSFwP","colab_type":"code","colab":{}},"source":["def accuracy(actual, prediction):\n","  correct = sum([x == y for x,y in zip(actual, prediction)])\n","  total = prediction.shape[0]\n","  return correct / total\n","\n","def precision(actual, prediction, class_of_interest):\n","  correct = sum([x == y and x == class_of_interest for x,y in zip(actual, prediction)])\n","  total = list(prediction).count(class_of_interest)\n","  return correct / total\n","\n","def recall(actual, prediction, class_of_interest):\n","  correct = sum([x == y and x == class_of_interest for x,y in zip(actual, prediction)])\n","  total = list(actual).count(class_of_interest)\n","  return correct / total\n","\n","def plot_metrics(history, num_epochs):\n","  plt.plot(list(range(1,num_epochs+1)), history.history['acc'],\n","           list(range(1,num_epochs+1)), history.history['val_acc'])\n","  plt.title(\"Accuracy Curves\")\n","  plt.xlabel(\"Number of Epochs\")\n","  plt.ylabel(\"Accuracy\")\n","  plt.show()\n","\n","  plt.plot(list(range(1,num_epochs+1)), history.history['loss'],\n","           list(range(1,num_epochs+1)), history.history['val_loss'])\n","  plt.title(\"Loss Curves\")\n","  plt.xlabel(\"Number of Epochs\")\n","  plt.ylabel(\"Loss curves\")\n","  plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ufu_Q_aB9W1r","colab_type":"text"},"source":["## Model 0: Simple Feed-Forward Neural Network\n","\n","Create the baseline Keras model. It is a sequential model with the following structure:\n","- Embedding layer that converts each integer sequence (previously a sentence) into a dense vector, which is easier for the model to use.\n","- Flattening layer to convert the matrix of values to a single vector\n","- Dense layer with 1 output neuron and sigmoid activation to classify each sentence as either safe or unsafe\n","\n","The best optimizer was found to be Adam, and the best learning rate was found to be 0.0001. These were used in all subsequent models.\n","\n","Train the model for up to 10 epochs, with early stopping (patience of 1). Plot the accuracy and loss curves (training vs validation), and print the accuracy, precision, and recall."]},{"cell_type":"code","metadata":{"id":"P4YpLxBeySPi","colab_type":"code","outputId":"7c8a481a-1696-4351-c723-db197c3e780d","executionInfo":{"status":"ok","timestamp":1574991189334,"user_tz":300,"elapsed":1079,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":533}},"source":["model0 = Sequential()\n","model0.add(Embedding(len(tokenizer.word_index) + 1, 128, input_length = max_len))\n","model0.add(Flatten())\n","model0.add(Dense(1, activation = \"sigmoid\"))\n","\n","model0.compile(optimizer = optimizers.Adam(lr=0.0001), \n","                loss = 'binary_crossentropy', \n","                metrics = ['accuracy'])\n","\n","model0.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use tf.where in 2.0, which has the same broadcast rule as np.where\n","Model: \"sequential_1\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","embedding_1 (Embedding)      (None, 2321, 128)         24291328  \n","_________________________________________________________________\n","flatten_1 (Flatten)          (None, 297088)            0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 1)                 297089    \n","=================================================================\n","Total params: 24,588,417\n","Trainable params: 24,588,417\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"mU5kn0tVyX-u","colab_type":"code","outputId":"b0284084-2336-4fbf-e05a-ce18a7366100","executionInfo":{"status":"ok","timestamp":1574991849504,"user_tz":300,"elapsed":661103,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["model0_history = model0.fit(X_train, y_train, epochs=10, validation_data=(X_validation, y_validation),\n","                            batch_size=128, use_multiprocessing=True, callbacks = [EarlyStopping(monitor='val_loss', patience=1)])\n","plot_metrics(model0_history, len(model0_history.history[\"val_loss\"]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n","\n","Train on 328050 samples, validate on 18225 samples\n","Epoch 1/10\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n","328050/328050 [==============================] - 139s 425us/step - loss: 0.5527 - acc: 0.7177 - val_loss: 0.4189 - val_acc: 0.8273\n","Epoch 2/10\n","328050/328050 [==============================] - 132s 402us/step - loss: 0.3607 - acc: 0.8548 - val_loss: 0.3483 - val_acc: 0.8584\n","Epoch 3/10\n","328050/328050 [==============================] - 129s 395us/step - loss: 0.3053 - acc: 0.8789 - val_loss: 0.3315 - val_acc: 0.8652\n","Epoch 4/10\n","328050/328050 [==============================] - 129s 394us/step - loss: 0.2794 - acc: 0.8907 - val_loss: 0.3265 - val_acc: 0.8721\n","Epoch 5/10\n","328050/328050 [==============================] - 129s 394us/step - loss: 0.2617 - acc: 0.8986 - val_loss: 0.3265 - val_acc: 0.8708\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwksfrWf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5zex+M8s3s/zs7OyD/QyHpKEhaNsYld2TCyYOiksMzjnX1mKZOBYA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\nH1PXtC4AAAAASUVORK5CYII=\n","text/plain":["<Figure 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j9y4t5aeEaumRn8LVjBvP1YwbTrVOCiSKe2i6r2jGO2Nt043ZZFe55h5W6rKSVUYKQ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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"OhEIvRvTycsy","colab_type":"code","outputId":"085289e8-2a8f-4902-c677-18ed2fa820c5","executionInfo":{"status":"ok","timestamp":1574991852016,"user_tz":300,"elapsed":2499,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":109}},"source":["y_pred0 = np.round(model0.predict(X_test))\n","print(\"Accuracy\", accuracy(y_test, y_pred0))\n","print(\"Precision (Safe)\", precision(y_test, y_pred0, 0))\n","print(\"Precision (Unsafe)\", precision(y_test, y_pred0, 1))\n","print(\"Recall (Safe)\", recall(y_test, y_pred0, 0))\n","print(\"Recall (Unsafe)\", recall(y_test, y_pred0, 1))\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Accuracy [0.86737997]\n","Precision (Safe) [0.84751883]\n","Precision (Unsafe) [0.88994374]\n","Recall (Safe) [0.89742189]\n","Recall (Unsafe) [0.83706317]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"FyhsiWnoNAqI","colab_type":"text"},"source":["## Model 1: Feed-Forward Neural Network with Extra Dense Layer\n","\n","Create a more refined version of the baseline Keras model. It is a sequential model with the following structure:\n","- Embedding layer that converts each integer sequence (previously a sentence) into a dense vector, which is easier for the model to use.\n","- Flattening layer to convert the matrix of values to a single vector\n","- Dense layer with 256 output neurons and relu activation as an intermediate neural network\n","- Dense layer with 1 output neuron and sigmoid activation to classify each sentence as either safe or unsafe\n","\n","Train the model for up to 10 epochs, with early stopping (patience of 1). Plot the accuracy and loss curves (training vs validation), and print the accuracy, precision, and recall."]},{"cell_type":"code","metadata":{"id":"d9bTTdF7SPn3","colab_type":"code","outputId":"302bf4e0-e63a-4c35-9519-42905bd0e4b0","executionInfo":{"status":"ok","timestamp":1574886500838,"user_tz":300,"elapsed":26934,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":570}},"source":["model1 = Sequential()\n","model1.add(Embedding(len(tokenizer.word_index) + 1, 128, input_length = max_len))\n","model1.add(Flatten())\n","model1.add(Dense(256, activation = \"relu\"))\n","model1.add(Dense(1, activation = \"sigmoid\"))\n","\n","model1.compile(optimizer = optimizers.Adam(lr=0.0001), \n","                loss = 'binary_crossentropy', \n","                metrics = ['accuracy'])\n","\n","model1.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use tf.where in 2.0, which has the same broadcast rule as np.where\n","Model: \"sequential_1\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","embedding_1 (Embedding)      (None, 2321, 128)         24291328  \n","_________________________________________________________________\n","flatten_1 (Flatten)          (None, 297088)            0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 256)               76054784  \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 1)                 257       \n","=================================================================\n","Total params: 100,346,369\n","Trainable params: 100,346,369\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"OuUt8bDISk1L","colab_type":"text"},"source":["Train the model with maximum of ten epochs, with early stopping base on the validation loss. If the validation loss does not improve after 2 epochs, stop training the model. Plot the training loss against the validation loss, and the training accuracy against the validation accuracy."]},{"cell_type":"code","metadata":{"id":"8HZukCDByQb9","colab_type":"code","outputId":"1faaf855-3fdc-46af-d896-9d34b51f53fe","executionInfo":{"status":"ok","timestamp":1574887336398,"user_tz":300,"elapsed":859994,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["model1_history = model1.fit(X_train, y_train, epochs=10, validation_data=(X_validation, y_validation),\n","                            batch_size=128, use_multiprocessing=True, callbacks = [EarlyStopping(monitor='val_loss', patience=1)])\n","plot_metrics(model1_history, len(model1_history.history[\"val_loss\"]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n","\n","Train on 328050 samples, validate on 18225 samples\n","Epoch 1/10\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n","328050/328050 [==============================] - 173s 526us/step - loss: 0.5255 - acc: 0.7286 - val_loss: 0.3830 - val_acc: 0.8343\n","Epoch 2/10\n","328050/328050 [==============================] - 166s 505us/step - loss: 0.3231 - acc: 0.8669 - val_loss: 0.3224 - val_acc: 0.8733\n","Epoch 3/10\n","328050/328050 [==============================] - 166s 505us/step - loss: 0.2677 - acc: 0.8916 - val_loss: 0.3124 - val_acc: 0.8716\n","Epoch 4/10\n","328050/328050 [==============================] - 166s 506us/step - loss: 0.2371 - acc: 0.9060 - val_loss: 0.3027 - val_acc: 0.8829\n","Epoch 5/10\n","328050/328050 [==============================] - 166s 505us/step - loss: 0.2141 - acc: 0.9170 - val_loss: 0.3065 - val_acc: 0.8791\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrEksLCp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WwT8OnM7ytj2xrAWGMMU1IiYtmdP9URvdPrZu2u7ySpZt2HwyNjWV88WkJ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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"lw3LnHdcTJOW","colab_type":"text"},"source":["Test the trained model on the test set. Print the accuracy, as well as the recall and precision for both classes."]},{"cell_type":"code","metadata":{"id":"KNool-CPz4v9","colab_type":"code","outputId":"e08bef65-2820-44cc-ede3-39dddef77aee","executionInfo":{"status":"ok","timestamp":1574887338498,"user_tz":300,"elapsed":650209,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":109}},"source":["y_pred1 = np.round(model1.predict(X_test))\n","print(\"Accuracy\", accuracy(y_test, y_pred1))\n","print(\"Precision (Safe)\", precision(y_test, y_pred1, 0))\n","print(\"Precision (Unsafe)\", precision(y_test, y_pred1, 1))\n","print(\"Recall (Safe)\", recall(y_test, y_pred1, 0))\n","print(\"Recall (Unsafe)\", recall(y_test, y_pred1, 1))\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Accuracy [0.87572016]\n","Precision (Safe) [0.89469463]\n","Precision (Unsafe) [0.85828595]\n","Recall (Safe) [0.85296045]\n","Recall (Unsafe) [0.89868813]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Ic9v6JuJAnfv","colab_type":"text"},"source":["## Model 2: Convolutional Neural Network with One Convolutional Layer\n","\n","Create the first variation of the CNN. It is a sequential model with the following structure:\n","- Embedding layer that converts each integer sequence (previously a sentence) into a dense vector, which is easier for the model to use.\n","- One-Dimensional Convolutional layer with 32 filters, filter size of 3, and relu activation\n","- One-Dimensional Maximum Pooling layer\n","- Flattening layer to convert the matrix of values to a single vector\n","- Dense layer with 1 output neuron and sigmoid activation to classify each sentence as either safe or unsafe\n","\n","The pool size was varied between 3 and 5, and it was found to have negligible impact, and thus 3 was chosen. Convolutional filter variations of 16, 32, 64, and 128 were explored. Ultimately, 32 filters provided the best result. This is the model that is shown.\n","\n","Train the model for up to 10 epochs, with early stopping (patience of 1). Plot the accuracy and loss curves (training vs validation), and print the accuracy, precision, and recall.\n","\n"]},{"cell_type":"code","metadata":{"id":"_zNMFcT8AeUB","colab_type":"code","outputId":"ac227a1d-4c99-4c27-a4dc-c3821cd727ad","executionInfo":{"status":"ok","timestamp":1574887338501,"user_tz":300,"elapsed":647398,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":441}},"source":["model2_32 = Sequential()\n","\n","model2_32.add(Embedding(len(tokenizer.word_index) + 1, 128, input_length = max_len))\n","model2_32.add(Conv1D(32, 3, activation = \"relu\"))\n","model2_32.add(MaxPooling1D())\n","\n","model2_32.add(Flatten())\n","model2_32.add(Dense(256, activation = \"relu\"))\n","model2_32.add(Dense(1, activation = \"sigmoid\"))\n","\n","model2_32.compile(optimizer = optimizers.Adam(lr=0.0001), \n","               loss = 'binary_crossentropy', \n","               metrics = ['accuracy'])\n","\n","model2_32.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n","\n","Model: \"sequential_2\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","embedding_2 (Embedding)      (None, 2321, 128)         24291328  \n","_________________________________________________________________\n","conv1d_1 (Conv1D)            (None, 2319, 32)          12320     \n","_________________________________________________________________\n","max_pooling1d_1 (MaxPooling1 (None, 1159, 32)          0         \n","_________________________________________________________________\n","flatten_2 (Flatten)          (None, 37088)             0         \n","_________________________________________________________________\n","dense_3 (Dense)              (None, 256)               9494784   \n","_________________________________________________________________\n","dense_4 (Dense)              (None, 1)                 257       \n","=================================================================\n","Total params: 33,798,689\n","Trainable params: 33,798,689\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"w4td7WrKTacX","colab_type":"text"},"source":["Train the model, with early stopping based on the validation loss with a patience of two. Plot the training loss against the validation loss, and the training accuracy against the validation accuracy."]},{"cell_type":"code","metadata":{"id":"MaLlNf-hBGJf","colab_type":"code","outputId":"4493ec81-02e8-4a34-de94-056b0c006854","executionInfo":{"status":"ok","timestamp":1574887647593,"user_tz":300,"elapsed":954430,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":702}},"source":["model2_32_history = model2_32.fit(X_train, y_train, epochs=10, validation_data=(X_validation, y_validation), \n","                            batch_size=128, use_multiprocessing=True, callbacks = [EarlyStopping(monitor='val_loss', patience=1)])\n","plot_metrics(model2_32_history, len(model2_32_history.history[\"val_loss\"]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Train on 328050 samples, validate on 18225 samples\n","Epoch 1/10\n","328050/328050 [==============================] - 105s 320us/step - loss: 0.4140 - acc: 0.7933 - val_loss: 0.2919 - val_acc: 0.8816\n","Epoch 2/10\n","328050/328050 [==============================] - 102s 310us/step - loss: 0.2536 - acc: 0.8972 - val_loss: 0.2700 - val_acc: 0.8906\n","Epoch 3/10\n","328050/328050 [==============================] - 101s 309us/step - loss: 0.2236 - acc: 0.9108 - val_loss: 0.2769 - val_acc: 0.8882\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuvShMqu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P7595aNThOOeSKJE2iK8BT0i6LxwvA+I+Xe1SW+mKjXxrxnxGF/bg7otHkZbm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\nMjnqAPbC42oej6t5PK7m6VB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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"twtfGOuYTnom","colab_type":"text"},"source":["Test the model on the test set, and print out the accuracy as well as the recall and precision for both classes."]},{"cell_type":"code","metadata":{"id":"3RCqAEBNBIZv","colab_type":"code","outputId":"f85ec6f3-38a4-4f5b-9436-6c3f0c8ba328","executionInfo":{"status":"ok","timestamp":1574887648965,"user_tz":300,"elapsed":953074,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":109}},"source":["y_pred2_32 = np.round(model2_32.predict(X_test))\n","print(\"Accuracy\", accuracy(y_test, y_pred2_32))\n","print(\"Precision (Safe)\", precision(y_test, y_pred2_32, 0))\n","print(\"Precision (Unsafe)\", precision(y_test, y_pred2_32, 1))\n","print(\"Recall (Safe)\", recall(y_test, y_pred2_32, 0))\n","print(\"Recall (Unsafe)\", recall(y_test, y_pred2_32, 1))\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Accuracy [0.88888889]\n","Precision (Safe) [0.90975974]\n","Precision (Unsafe) [0.86982994]\n","Recall (Safe) [0.86454009]\n","Recall (Unsafe) [0.91346048]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"m_ooG9b8TtRx","colab_type":"text"},"source":["## Model 3: Convolutional Neural Network with Two Convolutional Layers\n","\n","Create the second variation of the CNN. It is a sequential model with the following structure:\n","- Embedding layer that converts each integer sequence (previously a sentence) into a dense vector, which is easier for the model to use.\n","- Two One-Dimensional Convolutional layers with 64 filters, filter size of 3, and relu activation\n","- One-Dimensional Maximum Pooling layer\n","- Flattening layer to convert the matrix of values to a single vector\n","- Dense layer with 1 output neuron and sigmoid activation to classify each sentence as either safe or unsafe\n","\n","Convolutional filter variations of 16, 32, 64, and 128 were explored. Ultimately, 64 filters provided the best result. This is the model that is shown.\n","\n","Train the model for up to 10 epochs, with early stopping (patience of 1). Plot the accuracy and loss curves (training vs validation), and print the accuracy, precision, and recall.\n","\n"]},{"cell_type":"code","metadata":{"id":"cmYnwvfozULP","colab_type":"code","outputId":"13218bd6-e042-4248-b87b-56662343f25a","executionInfo":{"status":"ok","timestamp":1574891240284,"user_tz":300,"elapsed":339208,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":421}},"source":["model3_64 = Sequential()\n","\n","model3_64.add(Embedding(len(tokenizer.word_index) + 1, 128, input_length = max_len))\n","model3_64.add(Conv1D(64, 3, activation = \"relu\"))\n","model3_64.add(Conv1D(64, 3, activation = \"relu\"))\n","model3_64.add(MaxPooling1D())\n","\n","model3_64.add(Flatten())\n","model3_64.add(Dense(256, activation = \"relu\"))\n","model3_64.add(Dense(1, activation = \"sigmoid\"))\n","\n","model3_64.compile(optimizer = optimizers.Adam(lr=0.0001), \n","               loss = 'binary_crossentropy', \n","               metrics = ['accuracy'])\n","\n","model3_64.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model: \"sequential_10\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","embedding_10 (Embedding)     (None, 2321, 128)         24291328  \n","_________________________________________________________________\n","conv1d_13 (Conv1D)           (None, 2319, 64)          24640     \n","_________________________________________________________________\n","conv1d_14 (Conv1D)           (None, 2317, 64)          12352     \n","_________________________________________________________________\n","max_pooling1d_9 (MaxPooling1 (None, 1158, 64)          0         \n","_________________________________________________________________\n","flatten_10 (Flatten)         (None, 74112)             0         \n","_________________________________________________________________\n","dense_19 (Dense)             (None, 256)               18972928  \n","_________________________________________________________________\n","dense_20 (Dense)             (None, 1)                 257       \n","=================================================================\n","Total params: 43,301,505\n","Trainable params: 43,301,505\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"cdM7pIFEzZvR","colab_type":"code","outputId":"932d0d17-d2da-4861-eed6-80e548b76caf","executionInfo":{"status":"ok","timestamp":1574891684977,"user_tz":300,"elapsed":783896,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":702}},"source":["model3_64_history = model3_64.fit(X_train, y_train, epochs=10, validation_data=(X_validation, y_validation), \n","                            batch_size=128, use_multiprocessing=True, callbacks = [EarlyStopping(monitor='val_loss', patience=1)])\n","plot_metrics(model3_64_history, len(model3_64_history.history[\"val_loss\"]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Train on 328050 samples, validate on 18225 samples\n","Epoch 1/10\n","328050/328050 [==============================] - 149s 454us/step - loss: 0.4095 - acc: 0.7909 - val_loss: 0.2767 - val_acc: 0.8871\n","Epoch 2/10\n","328050/328050 [==============================] - 148s 450us/step - loss: 0.2494 - acc: 0.8993 - val_loss: 0.2661 - val_acc: 0.8899\n","Epoch 3/10\n","328050/328050 [==============================] - 147s 450us/step - loss: 0.2219 - acc: 0.9116 - val_loss: 0.2699 - val_acc: 0.8911\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nt+QBGt0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TjDuvG3RNH8c6qrVw/y3uXO3eoGkwcZpZjZrn1PHLMLJr+H861GuOP7sXtE47mHx9s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\nOedi8v8BiPkJbAGXfhQAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"CpASVp-9zg0f","colab_type":"code","outputId":"77d14440-fb44-4a4a-b93c-5f9786bf6e29","executionInfo":{"status":"ok","timestamp":1574891687628,"user_tz":300,"elapsed":786540,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":109}},"source":["y_pred3_64 = np.round(model3_64.predict(X_test))\n","print(\"Accuracy\", accuracy(y_test, y_pred3_64))\n","print(\"Precision (Safe)\", precision(y_test, y_pred3_64, 0))\n","print(\"Precision (Unsafe)\", precision(y_test, y_pred3_64, 1))\n","print(\"Recall (Safe)\", recall(y_test, y_pred3_64, 0))\n","print(\"Recall (Unsafe)\", recall(y_test, y_pred3_64, 1))\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Accuracy [0.88899863]\n","Precision (Safe) [0.90706702]\n","Precision (Unsafe) [0.87227974]\n","Recall (Safe) [0.86792659]\n","Recall (Unsafe) [0.91026348]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"jwZ2AsTmUaMB","colab_type":"text"},"source":["## Model 4: Convolutional Neural Network with Two Convolutional Layers and Dropout\n","\n","Create the third variation of the CNN. It is a sequential model with the following structure:\n","- Embedding layer that converts each integer sequence (previously a sentence) into a dense vector, which is easier for the model to use.\n","- Two One-Dimensional Convolutional layers with 64 filters, filter size of 3, and relu activation\n","- One-Dimensional Maximum Pooling layer\n","- Dropout layer with 0.15 dropout\n","- Flattening layer to convert the matrix of values to a single vector\n","- Dense layer with 1 output neuron and sigmoid activation to classify each sentence as either safe or unsafe\n","\n","This model is built on top of model3, with 64 convolutional filters.\n","\n","Dropout variations of 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, and 0.75 were explored. Ultimately, 0.15 provided the best result. This is the model that was shown.\n","\n","Train the model for up to 10 epochs, with early stopping (patience of 1). Plot the accuracy and loss curves (training vs validation), and print the accuracy, precision, and recall.\n","\n"]},{"cell_type":"code","metadata":{"id":"tXcjIKD262sO","colab_type":"code","outputId":"810aa59f-d0b6-40f7-d536-2e5a1a17a2b6","executionInfo":{"status":"ok","timestamp":1574976633212,"user_tz":300,"elapsed":936,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":458}},"source":["model4_15 = Sequential()\n","\n","model4_15.add(Embedding(len(tokenizer.word_index) + 1, 128, input_length = max_len))\n","model4_15.add(Conv1D(64, 3, activation = \"relu\"))\n","model4_15.add(Conv1D(64, 3, activation = \"relu\"))\n","model4_15.add(MaxPooling1D())\n","model4_15.add(Dropout(0.15))\n","model4_15.add(Flatten())\n","model4_15.add(Dense(256, activation = \"relu\"))\n","model4_15.add(Dense(1, activation = \"sigmoid\"))\n","\n","model4_15.compile(optimizer = optimizers.Adam(lr=0.0001), \n","               loss = 'binary_crossentropy', \n","               metrics = ['accuracy'])\n","\n","model4_15.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model: \"sequential_6\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","embedding_6 (Embedding)      (None, 2321, 128)         24291328  \n","_________________________________________________________________\n","conv1d_19 (Conv1D)           (None, 2319, 64)          24640     \n","_________________________________________________________________\n","conv1d_20 (Conv1D)           (None, 2317, 64)          12352     \n","_________________________________________________________________\n","max_pooling1d_10 (MaxPooling (None, 1158, 64)          0         \n","_________________________________________________________________\n","dropout_2 (Dropout)          (None, 1158, 64)          0         \n","_________________________________________________________________\n","flatten_6 (Flatten)          (None, 74112)             0         \n","_________________________________________________________________\n","dense_11 (Dense)             (None, 256)               18972928  \n","_________________________________________________________________\n","dense_12 (Dense)             (None, 1)                 257       \n","=================================================================\n","Total params: 43,301,505\n","Trainable params: 43,301,505\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"takmcGAo6_px","colab_type":"code","outputId":"ea550270-22b7-44aa-9821-10378b6a1363","executionInfo":{"status":"ok","timestamp":1574977087652,"user_tz":300,"elapsed":454752,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":702}},"source":["model4_15_history = model4_15.fit(X_train, y_train, epochs=10, validation_data=(X_validation, y_validation),\n","                            batch_size=128, use_multiprocessing=True, callbacks = [EarlyStopping(monitor='val_loss', patience=1)])\n","plot_metrics(model4_15_history, len(model4_15_history.history[\"val_loss\"]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Train on 328050 samples, validate on 18225 samples\n","Epoch 1/10\n","328050/328050 [==============================] - 152s 463us/step - loss: 0.4851 - acc: 0.7171 - val_loss: 0.2841 - val_acc: 0.8840\n","Epoch 2/10\n","328050/328050 [==============================] - 151s 462us/step - loss: 0.2581 - acc: 0.8954 - val_loss: 0.2662 - val_acc: 0.8908\n","Epoch 3/10\n","328050/328050 [==============================] - 150s 458us/step - loss: 0.2283 - acc: 0.9088 - val_loss: 0.2670 - val_acc: 0.8882\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nv3J01OE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9ZSrP6SIgAGqxPBIDzJg5lw/YG7njuXUr75/Cts0+MOpJI5FQgREJfPW0U67cdvI/E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\nRchIsoIAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"JoUW6ZZF7CMf","colab_type":"code","outputId":"5ab7caf4-5498-4723-c95b-bd72479e9ae6","executionInfo":{"status":"ok","timestamp":1574977091404,"user_tz":300,"elapsed":3152,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":109}},"source":["y_pred4_15 = np.round(model4_15.predict(X_test))\n","print(\"Accuracy\", accuracy(y_test, y_pred4_15))\n","print(\"Precision (Safe)\", precision(y_test, y_pred4_15, 0))\n","print(\"Precision (Unsafe)\", precision(y_test, y_pred4_15, 1))\n","print(\"Recall (Safe)\", recall(y_test, y_pred4_15, 0))\n","print(\"Recall (Unsafe)\", recall(y_test, y_pred4_15, 1))\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Accuracy [0.89091907]\n","Precision (Safe) [0.90836563]\n","Precision (Unsafe) [0.87472225]\n","Recall (Safe) [0.87065764]\n","Recall (Unsafe) [0.91136589]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"wh_b5FwWWVEX","colab_type":"text"},"source":["## Model 5: Convolutional Neural Network with Two Sets of Two Convolutional Layers\n","\n","Create the fourth variation of the CNN. It is a sequential model with the following structure:\n","- Embedding layer that converts each integer sequence (previously a sentence) into a dense vector, which is easier for the model to use.\n","- Two One-Dimensional Convolutional layers with 32 filters, filter size of 3, and relu activation\n","- One-Dimensional Maximum Pooling layer\n","- Two One-Dimensional Convolutional layers with 64 filters, filter size of 3, and relu activation\n","- One-Dimensional Maximum Pooling layer\n","- Flattening layer to convert the matrix of values to a single vector\n","- Dense layer with 1 output neuron and sigmoid activation to classify each sentence as either safe or unsafe\n","\n","The following convolutional filter combinations were explored (the first number is the number of filters in the first set of conv layers; the second number is the number of filters in the second set of conv layers): 16/32, 32/64, 16/128, 32/128, 64/128. Ultimately, 32/64 provided the best results, which is the model shown.\n","\n","Train the model for up to 10 epochs, with early stopping (patience of 1). Plot the accuracy and loss curves (training vs validation), and print the accuracy, precision, and recall.\n","\n"]},{"cell_type":"code","metadata":{"id":"6tU9G6V2DfO0","colab_type":"code","outputId":"fbccd298-58bc-428c-d291-898af71068b5","executionInfo":{"status":"ok","timestamp":1574971471412,"user_tz":300,"elapsed":1193,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":827}},"source":["model5_3264 = Sequential()\n","\n","model5_3264.add(Embedding(len(tokenizer.word_index) + 1, 128, input_length = max_len))\n","model5_3264.add(Conv1D(32, 3, activation = \"relu\"))\n","model5_3264.add(Conv1D(32, 3, activation = \"relu\"))\n","model5_3264.add(MaxPooling1D())\n","model5_3264.add(Conv1D(64, 3, activation = \"relu\"))\n","model5_3264.add(Conv1D(64, 3, activation = \"relu\"))\n","model5_3264.add(MaxPooling1D())\n","model5_3264.add(Flatten())\n","model5_3264.add(Dense(256, activation = \"relu\"))\n","model5_3264.add(Dense(1, activation = \"sigmoid\"))\n","\n","model5_3264.compile(optimizer = optimizers.Adam(lr=0.0001), \n","               loss = 'binary_crossentropy', \n","               metrics = ['accuracy'])\n","\n","model5_3264.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use tf.where in 2.0, which has the same broadcast rule as np.where\n","Model: \"sequential_1\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","embedding_1 (Embedding)      (None, 2321, 128)         24291328  \n","_________________________________________________________________\n","conv1d_1 (Conv1D)            (None, 2319, 32)          12320     \n","_________________________________________________________________\n","conv1d_2 (Conv1D)            (None, 2317, 32)          3104      \n","_________________________________________________________________\n","max_pooling1d_1 (MaxPooling1 (None, 1158, 32)          0         \n","_________________________________________________________________\n","conv1d_3 (Conv1D)            (None, 1156, 64)          6208      \n","_________________________________________________________________\n","conv1d_4 (Conv1D)            (None, 1154, 64)          12352     \n","_________________________________________________________________\n","max_pooling1d_2 (MaxPooling1 (None, 577, 64)           0         \n","_________________________________________________________________\n","flatten_1 (Flatten)          (None, 36928)             0         \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 256)               9453824   \n","_________________________________________________________________\n","dense_2 (Dense)              (None, 1)                 257       \n","=================================================================\n","Total params: 33,779,393\n","Trainable params: 33,779,393\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"LVsRqq1gXwUw","colab_type":"text"},"source":["Train the model, using early stopping with validation loss and patience of 2 epochs"]},{"cell_type":"code","metadata":{"id":"LP1lWDAgM7su","colab_type":"code","outputId":"e3f8d581-8f5f-483f-cc25-79798ab72421","executionInfo":{"status":"ok","timestamp":1574971865320,"user_tz":300,"elapsed":389577,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["model5_3264_history = model5_3264.fit(X_train, y_train, epochs=10, validation_data=(X_validation, y_validation), \n","                            batch_size=128, use_multiprocessing=True, callbacks = [EarlyStopping(monitor='val_loss', patience=1)])\n","plot_metrics(model5_3264_history, len(model5_3264_history.history[\"val_loss\"]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n","\n","Train on 328050 samples, validate on 18225 samples\n","Epoch 1/10\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n","328050/328050 [==============================] - 134s 410us/step - loss: 0.3866 - acc: 0.8095 - val_loss: 0.2711 - val_acc: 0.8900\n","Epoch 2/10\n","328050/328050 [==============================] - 127s 387us/step - loss: 0.2425 - acc: 0.9021 - val_loss: 0.2652 - val_acc: 0.8918\n","Epoch 3/10\n","328050/328050 [==============================] - 127s 386us/step - loss: 0.2105 - acc: 0.9171 - val_loss: 0.2798 - val_acc: 0.8889\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1Oxaa6i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l/6mhys5r7v4IkVO2Opi9FrXofvF49lvSsYLygsBf0HA5HnbV/QijsGSSNdP3R0ByN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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"O2u2qc4gX08J","colab_type":"text"},"source":["Test the model using the test set. Print the accuracy, as well as the recall and precision for both classes."]},{"cell_type":"code","metadata":{"id":"dFsAFDoRM_ez","colab_type":"code","outputId":"b3f89b17-a915-4810-cd31-5e50a0097f8d","executionInfo":{"status":"ok","timestamp":1574971867267,"user_tz":300,"elapsed":388705,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":109}},"source":["y_pred5_3264 = np.round(model5_3264.predict(X_test))\n","print(\"Accuracy\", accuracy(y_test, y_pred5_3264))\n","print(\"Precision (Safe)\", precision(y_test, y_pred5_3264, 0))\n","print(\"Precision (Unsafe)\", precision(y_test, y_pred5_3264, 1))\n","print(\"Recall (Safe)\", recall(y_test, y_pred5_3264, 0))\n","print(\"Recall (Unsafe)\", recall(y_test, y_pred5_3264, 1))\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Accuracy [0.88658436]\n","Precision (Safe) [0.89185005]\n","Precision (Unsafe) [0.88139839]\n","Recall (Safe) [0.88103561]\n","Recall (Unsafe) [0.89218388]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"i7XOpx2AQF6F","colab_type":"text"},"source":["## Model 6: Convolutional Neural Network with Two Sets of Two Convolutional Layers with Dropout\n","\n","Create the fifth and final variation of the CNN. It is a sequential model with the following structure:\n","- Embedding layer that converts each integer sequence (previously a sentence) into a dense vector, which is easier for the model to use.\n","- Two One-Dimensional Convolutional layers with 32 filters, filter size of 3, and relu activation\n","- One-Dimensional Maximum Pooling layer\n","- Dropout layer with 0.75 dropout\n","- Two One-Dimensional Convolutional layers with 64 filters, filter size of 3, and relu activation\n","- One-Dimensional Maximum Pooling layer\n","- Dropout layer with 0.75 dropout\n","- Flattening layer to convert the matrix of values to a single vector\n","- Dense layer with 1 output neuron and sigmoid activation to classify each sentence as either safe or unsafe\n","\n","Dropout variations of 0.1, 0.15, 0.2, 0.25, 0.5, 0.7, 0.75, 0.8, and 0.9 were explore. Ulimately, 0.75 provided the best result and this is the model that is shown.\n","\n","Train the model for up to 10 epochs, with early stopping (patience of 1). Plot the accuracy and loss curves (training vs validation), and print the accuracy, precision, and recall.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"PbqK-NJdP8_R","colab_type":"text"},"source":[""]},{"cell_type":"code","metadata":{"id":"hkv6vjRyqlsi","colab_type":"code","outputId":"e121c142-7750-42ab-d4d1-61f12e10776e","executionInfo":{"status":"ok","timestamp":1575067789033,"user_tz":300,"elapsed":787,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":605}},"source":["model6_3264_drop = Sequential()\n","\n","model6_3264_drop.add(Embedding(len(tokenizer.word_index) + 1, 128, input_length = max_len))\n","model6_3264_drop.add(Conv1D(32, 3, activation = \"relu\"))\n","model6_3264_drop.add(Conv1D(32, 3, activation = \"relu\"))\n","model6_3264_drop.add(MaxPooling1D())\n","model6_3264_drop.add(Dropout(0.15))\n","model6_3264_drop.add(Conv1D(64, 3, activation = \"relu\"))\n","model6_3264_drop.add(Conv1D(64, 3, activation = \"relu\"))\n","model6_3264_drop.add(MaxPooling1D())\n","model6_3264_drop.add(Dropout(0.15))\n","model6_3264_drop.add(Flatten())\n","model6_3264_drop.add(Dense(256, activation = \"relu\"))\n","model6_3264_drop.add(Dense(1, activation = \"sigmoid\"))\n","\n","model6_3264_drop.compile(optimizer = optimizers.Adam(lr=0.0001), \n","               loss = 'binary_crossentropy', \n","               metrics = ['accuracy'])\n","\n","model6_3264_drop.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model: \"sequential_12\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","embedding_12 (Embedding)     (None, 2321, 128)         24291328  \n","_________________________________________________________________\n","conv1d_45 (Conv1D)           (None, 2319, 32)          12320     \n","_________________________________________________________________\n","conv1d_46 (Conv1D)           (None, 2317, 32)          3104      \n","_________________________________________________________________\n","max_pooling1d_23 (MaxPooling (None, 1158, 32)          0         \n","_________________________________________________________________\n","dropout_23 (Dropout)         (None, 1158, 32)          0         \n","_________________________________________________________________\n","conv1d_47 (Conv1D)           (None, 1156, 64)          6208      \n","_________________________________________________________________\n","conv1d_48 (Conv1D)           (None, 1154, 64)          12352     \n","_________________________________________________________________\n","max_pooling1d_24 (MaxPooling (None, 577, 64)           0         \n","_________________________________________________________________\n","dropout_24 (Dropout)         (None, 577, 64)           0         \n","_________________________________________________________________\n","flatten_12 (Flatten)         (None, 36928)             0         \n","_________________________________________________________________\n","dense_23 (Dense)             (None, 256)               9453824   \n","_________________________________________________________________\n","dense_24 (Dense)             (None, 1)                 257       \n","=================================================================\n","Total params: 33,779,393\n","Trainable params: 33,779,393\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"cW5ibvhP9I0N","colab_type":"code","outputId":"c62e4db8-46e1-4714-91c5-e9778aec50c0","executionInfo":{"status":"ok","timestamp":1575068205678,"user_tz":300,"elapsed":417262,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":702}},"source":["model6_3264_drop_history = model6_3264_drop.fit(X_train, y_train, epochs=10, validation_data=(X_validation, y_validation), \n","                            batch_size=128, use_multiprocessing=True, callbacks = [EarlyStopping(monitor='val_loss', patience=1)])\n","plot_metrics(model6_3264_drop_history, len(model6_3264_drop_history.history[\"val_loss\"]))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Train on 328050 samples, validate on 18225 samples\n","Epoch 1/10\n","328050/328050 [==============================] - 141s 429us/step - loss: 0.3992 - acc: 0.8017 - val_loss: 0.2756 - val_acc: 0.8876\n","Epoch 2/10\n","328050/328050 [==============================] - 137s 418us/step - loss: 0.2467 - acc: 0.9008 - val_loss: 0.2678 - val_acc: 0.8906\n","Epoch 3/10\n","328050/328050 [==============================] - 137s 419us/step - loss: 0.2140 - acc: 0.9152 - val_loss: 0.2727 - val_acc: 0.8908\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuLLD2rn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5ofELydU3JaXljH5+Ois2bOfpG3LJ6ZiR6JCcc3UglgSRIim18omkFkDqftZ3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\nZYdWmdPMGvQD+CYwEJhXzfK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+qfHp3zmT63KW8s2VvokNyrkWqb+LwC+Ndi9SlQzrzp48jOzOVK+csYc32fYkOybkW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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"xQbSRU1I9Nwd","colab_type":"code","outputId":"db780b68-1265-4062-ff70-605aafe0b62c","executionInfo":{"status":"ok","timestamp":1575068208874,"user_tz":300,"elapsed":419958,"user":{"displayName":"Chaitanya Parekh","photoUrl":"","userId":"02319740950516569165"}},"colab":{"base_uri":"https://localhost:8080/","height":109}},"source":["y_pred6_3264_drop = np.round(model6_3264_drop.predict(X_test))\n","print(\"Accuracy\", accuracy(y_test, y_pred6_3264_drop))\n","print(\"Precision (Safe)\", precision(y_test, y_pred6_3264_drop, 0))\n","print(\"Precision (Unsafe)\", precision(y_test, y_pred6_3264_drop, 1))\n","print(\"Recall (Safe)\", recall(y_test, y_pred6_3264_drop, 0))\n","print(\"Recall (Unsafe)\", recall(y_test, y_pred6_3264_drop, 1))\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Accuracy [0.88828532]\n","Precision (Safe) [0.90767468]\n","Precision (Unsafe) [0.87045814]\n","Recall (Safe) [0.86563251]\n","Recall (Unsafe) [0.91114541]\n"],"name":"stdout"}]}]}