diff --git a/ArtificialNeuralNetwork.ipynb b/ArtificialNeuralNetwork.ipynb index 6fa36fd..e8cbff3 100644 --- a/ArtificialNeuralNetwork.ipynb +++ b/ArtificialNeuralNetwork.ipynb @@ -120,9 +120,9 @@ "# Adding the input layer and the first hidden layer\n", "classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))\n", "\n", - "# Adding the second hidden layer\n", - "classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))\n", - "\n", + "# Adding the second and third hidden layer\n", + "classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'tanh'))\n", + "classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'tanh'))\n", "# Adding the output layer\n", "classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n", "\n", @@ -155,15 +155,26 @@ "\n", "# Making the Confusion Matrix\n", "from sklearn.metrics import confusion_matrix\n", - "cm = confusion_matrix(y_test, y_pred)" + "cm = confusion_matrix(y_test, y_pred)\n", + "print(cm)" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "1 relu = [[1505 90]\n", + " [ 196 209]] = 85,7%\n", + " \n", + "2 relu = [[1556 39]\n", + " [ 278 127]] = 84,15%\n", + " \n", + "1 tanh = [[1526 69]\n", + " [ 206 199]] = 86,25%\n", + " \n", + "2 tanh = [[1544 51]\n", + " [ 258 147]] = 84,55%" + ] } ], "metadata": { @@ -182,7 +193,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.5.3" } }, "nbformat": 4, diff --git a/evaluating_improving_tuning.ipynb b/evaluating_improving_tuning.ipynb new file mode 100644 index 0000000..a1c2ace --- /dev/null +++ b/evaluating_improving_tuning.ipynb @@ -0,0 +1,20490 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Artificial Neural Network\n", + "\n", + "# Installing Theano\n", + "# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git\n", + "\n", + "# Installing Tensorflow\n", + "# pip install tensorflow\n", + "\n", + "# Installing Keras\n", + "# pip install --upgrade keras\n", + "\n", + "# Part 1 - Data Preprocessing\n", + "\n", + "# Importing the libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Importing the dataset\n", + "dataset = pd.read_csv('Churn_Modelling.csv')\n", + "X = dataset.iloc[:, 3:13].values\n", + "y = dataset.iloc[:, 13].values" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Now let's make the ANN!\n", + "\n", + "# Importing the Keras libraries and packages\n", + "import keras\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "from keras.layers import Dropout" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/cjjcastro/anaconda3/lib/python3.7/site-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", + "WARNING:tensorflow:From /home/cjjcastro/anaconda3/lib/python3.7/site-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", + "Epoch 1/100\n", + "8000/8000 [==============================] - 1s 181us/step - loss: 0.4764 - acc: 0.7960\n", + "Epoch 2/100\n", + "8000/8000 [==============================] - 1s 145us/step - loss: 0.4294 - acc: 0.7960\n", + "Epoch 3/100\n", + "8000/8000 [==============================] - 1s 139us/step - loss: 0.4238 - acc: 0.8000\n", + "Epoch 4/100\n", + "8000/8000 [==============================] - 1s 112us/step - loss: 0.4192 - acc: 0.8215\n", + "Epoch 5/100\n", + "8000/8000 [==============================] - 1s 112us/step - loss: 0.4162 - acc: 0.8246\n", + "Epoch 6/100\n", + "8000/8000 [==============================] - 1s 154us/step - loss: 0.4140 - acc: 0.8292\n", + "Epoch 7/100\n", + "8000/8000 [==============================] - 1s 158us/step - loss: 0.4124 - acc: 0.8310\n", + "Epoch 8/100\n", + "8000/8000 [==============================] - 1s 136us/step - loss: 0.4112 - acc: 0.8312\n", + "Epoch 9/100\n", + "8000/8000 [==============================] - 1s 119us/step - loss: 0.4100 - acc: 0.8345\n", + "Epoch 10/100\n", + "8000/8000 [==============================] - 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1s 123us/step - loss: 0.4007 - acc: 0.8346\n", + "Epoch 65/100\n", + "8000/8000 [==============================] - 1s 127us/step - loss: 0.4008 - acc: 0.8367\n", + "Epoch 66/100\n", + "8000/8000 [==============================] - 1s 124us/step - loss: 0.4008 - acc: 0.8346\n", + "Epoch 67/100\n", + "8000/8000 [==============================] - 1s 118us/step - loss: 0.4004 - acc: 0.8352\n", + "Epoch 68/100\n", + "8000/8000 [==============================] - 1s 127us/step - loss: 0.4008 - acc: 0.8341\n", + "Epoch 69/100\n", + "8000/8000 [==============================] - 1s 128us/step - loss: 0.4009 - acc: 0.8337\n", + "Epoch 70/100\n", + "8000/8000 [==============================] - 1s 132us/step - loss: 0.4007 - acc: 0.8347\n", + "Epoch 71/100\n", + "8000/8000 [==============================] - 1s 120us/step - loss: 0.4010 - acc: 0.8342\n", + "Epoch 72/100\n", + "8000/8000 [==============================] - 1s 122us/step - loss: 0.4002 - acc: 0.8347\n", + "Epoch 73/100\n", + "8000/8000 [==============================] - 1s 144us/step - loss: 0.4003 - acc: 0.8347\n", + "Epoch 74/100\n", + "8000/8000 [==============================] - 1s 176us/step - loss: 0.4008 - acc: 0.8344\n", + "Epoch 75/100\n", + "8000/8000 [==============================] - 2s 311us/step - loss: 0.4000 - acc: 0.8337\n", + "Epoch 76/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8000/8000 [==============================] - 1s 177us/step - loss: 0.4007 - acc: 0.8355\n", + "Epoch 77/100\n", + "8000/8000 [==============================] - 1s 133us/step - loss: 0.4008 - acc: 0.8347\n", + "Epoch 78/100\n", + "8000/8000 [==============================] - 1s 118us/step - loss: 0.4006 - acc: 0.8357\n", + "Epoch 79/100\n", + "8000/8000 [==============================] - 1s 113us/step - loss: 0.4005 - acc: 0.8351\n", + "Epoch 80/100\n", + "8000/8000 [==============================] - 1s 114us/step - loss: 0.4005 - acc: 0.8346\n", + "Epoch 81/100\n", + "8000/8000 [==============================] - 1s 108us/step - loss: 0.4011 - acc: 0.8334\n", + "Epoch 82/100\n", + "8000/8000 [==============================] - 1s 133us/step - loss: 0.4005 - acc: 0.8341\n", + "Epoch 83/100\n", + "8000/8000 [==============================] - 1s 113us/step - loss: 0.4000 - acc: 0.8347\n", + "Epoch 84/100\n", + "8000/8000 [==============================] - 1s 115us/step - loss: 0.4005 - acc: 0.8346\n", + "Epoch 85/100\n", + "8000/8000 [==============================] - 1s 110us/step - loss: 0.4004 - acc: 0.8346\n", + "Epoch 86/100\n", + "8000/8000 [==============================] - 1s 110us/step - loss: 0.4005 - acc: 0.8352\n", + "Epoch 87/100\n", + "8000/8000 [==============================] - 1s 112us/step - loss: 0.4001 - acc: 0.8356\n", + "Epoch 88/100\n", + "8000/8000 [==============================] - 1s 116us/step - loss: 0.3998 - acc: 0.8341\n", + "Epoch 89/100\n", + "8000/8000 [==============================] - 1s 142us/step - loss: 0.4006 - acc: 0.8355\n", + "Epoch 90/100\n", + "8000/8000 [==============================] - 1s 126us/step - loss: 0.3996 - acc: 0.8357\n", + "Epoch 91/100\n", + "8000/8000 [==============================] - 1s 109us/step - loss: 0.4006 - acc: 0.8342\n", + "Epoch 92/100\n", + "8000/8000 [==============================] - 1s 111us/step - loss: 0.3999 - acc: 0.8347\n", + "Epoch 93/100\n", + "8000/8000 [==============================] - 1s 111us/step - loss: 0.4003 - acc: 0.8349\n", + "Epoch 94/100\n", + "8000/8000 [==============================] - 1s 143us/step - loss: 0.4003 - acc: 0.8359\n", + "Epoch 95/100\n", + "8000/8000 [==============================] - 1s 124us/step - loss: 0.4001 - acc: 0.8346\n", + "Epoch 96/100\n", + "8000/8000 [==============================] - 1s 111us/step - loss: 0.3999 - acc: 0.8347\n", + "Epoch 97/100\n", + "8000/8000 [==============================] - 1s 110us/step - loss: 0.4006 - acc: 0.8351\n", + "Epoch 98/100\n", + "8000/8000 [==============================] - 1s 110us/step - loss: 0.4006 - acc: 0.8345\n", + "Epoch 99/100\n", + "8000/8000 [==============================] - 1s 112us/step - loss: 0.4001 - acc: 0.8357\n", + "Epoch 100/100\n", + "8000/8000 [==============================] - 1s 110us/step - loss: 0.4000 - acc: 0.8332\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Initialising the ANN\n", + "classifier = Sequential()\n", + "\n", + "# Adding the input layer and the first hidden layer\n", + "classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))\n", + "# classifier.add(Dropout(p = 0.1))\n", + "\n", + "# Adding the second hidden layer\n", + "classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))\n", + "# classifier.add(Dropout(p = 0.1))\n", + "\n", + "# Adding the output layer\n", + "classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n", + "\n", + "# Compiling the ANN\n", + "classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n", + "\n", + "# Fitting the ANN to the Training set\n", + "classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Part 3 - Making predictions and evaluating the model\n", + "\n", + "# Predicting the Test set results\n", + "y_pred = classifier.predict(X_test)\n", + "y_pred = (y_pred > 0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Predicting a single new observation\n", + "\"\"\"Predict if the customer with the following informations will leave the bank:\n", + "Geography: France\n", + "Credit Score: 600\n", + "Gender: Male\n", + "Age: 40\n", + "Tenure: 3\n", + "Balance: 60000\n", + "Number of Products: 2\n", + "Has Credit Card: Yes\n", + "Is Active Member: Yes\n", + "Estimated Salary: 50000\"\"\"\n", + "new_prediction = classifier.predict(sc.transform(np.array([[0.0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]])))\n", + "new_prediction = (new_prediction > 0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1539 56]\n", + " [ 258 147]]\n" + ] + } + ], + "source": [ + "\n", + "# Making the Confusion Matrix\n", + "from sklearn.metrics import confusion_matrix, classification_report\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "print(cm)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.86 0.96 0.91 1595\n", + " 1 0.72 0.36 0.48 405\n", + "\n", + " micro avg 0.84 0.84 0.84 2000\n", + " macro avg 0.79 0.66 0.70 2000\n", + "weighted avg 0.83 0.84 0.82 2000\n", + "\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluating the ANN\n", + "from keras.wrappers.scikit_learn import KerasClassifier\n", + "from sklearn.model_selection import cross_val_score\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "def build_classifier():\n", + " classifier = Sequential()\n", + " classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))\n", + " classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))\n", + " classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n", + " classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n", + " return classifier\n", + "classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, epochs = 100)\n", + "accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)\n", + "mean = accuracies.mean()\n", + "variance = accuracies.std()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Improving the ANN\n", + "# Dropout Regularization to reduce overfitting if needed\n", + "\n", + "# Tuning the ANN\n", + "from keras.wrappers.scikit_learn import KerasClassifier\n", + "from sklearn.model_selection import GridSearchCV\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense\n", + "\n", + "def build_classifier(optimizer):\n", + " classifier = Sequential()\n", + " classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))\n", + " classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))\n", + " classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n", + " classifier.compile(optimizer = optimizer, loss = 'binary_crossentropy', metrics = ['accuracy'])\n", + " return classifier" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n", + "7200/7200 [==============================] - 1s 88us/step - loss: 0.5534 - acc: 0.7967\n", + "Epoch 2/100\n", + "7200/7200 [==============================] - 1s 106us/step - loss: 0.4378 - acc: 0.7971\n", + "Epoch 3/100\n", + "7200/7200 [==============================] - 0s 46us/step - loss: 0.4305 - acc: 0.7971\n", + "Epoch 4/100\n", + "7200/7200 [==============================] - 0s 68us/step - loss: 0.4276 - acc: 0.7971\n", + "Epoch 5/100\n", + "7200/7200 [==============================] - 1s 125us/step - loss: 0.4249 - acc: 0.7971\n", + "Epoch 6/100\n", + "7200/7200 [==============================] - 1s 93us/step - loss: 0.4218 - acc: 0.8124\n", + "Epoch 7/100\n", + "7200/7200 [==============================] - 0s 52us/step - loss: 0.4179 - acc: 0.8239\n", + "Epoch 8/100\n", + "7200/7200 [==============================] - 0s 51us/step - loss: 0.4143 - acc: 0.8290\n", + "Epoch 9/100\n", + "7200/7200 [==============================] - 0s 53us/step - loss: 0.4115 - acc: 0.8312\n", + "Epoch 10/100\n", + "7200/7200 [==============================] - 0s 53us/step - loss: 0.4089 - acc: 0.8317\n", + "Epoch 11/100\n", + "7200/7200 [==============================] - 0s 50us/step - loss: 0.4071 - acc: 0.8337\n", + "Epoch 12/100\n", + "7200/7200 [==============================] - 0s 54us/step - loss: 0.4055 - acc: 0.8328\n", + "Epoch 13/100\n", + "7200/7200 [==============================] - 0s 51us/step - loss: 0.4042 - acc: 0.8358\n", + "Epoch 14/100\n", + "7200/7200 [==============================] - 0s 57us/step - loss: 0.4033 - acc: 0.8351\n", + "Epoch 15/100\n", + "7200/7200 [==============================] - 0s 67us/step - loss: 0.4022 - acc: 0.8353\n", + "Epoch 16/100\n", + "7200/7200 [==============================] - 0s 52us/step - loss: 0.4013 - acc: 0.8365\n", + "Epoch 17/100\n", + "7200/7200 [==============================] - 0s 51us/step - loss: 0.4010 - acc: 0.8351\n", + "Epoch 18/100\n", + "7200/7200 [==============================] - 0s 54us/step - loss: 0.3996 - acc: 0.8347\n", + "Epoch 19/100\n", + "7200/7200 [==============================] - 0s 52us/step - loss: 0.3996 - acc: 0.8350\n", + "Epoch 20/100\n", + "7200/7200 [==============================] - 0s 51us/step - loss: 0.3990 - acc: 0.8360\n", + "Epoch 21/100\n", + "7200/7200 [==============================] - 0s 52us/step - loss: 0.3982 - acc: 0.8349\n", + "Epoch 22/100\n", + "7200/7200 [==============================] - 0s 54us/step - loss: 0.3978 - acc: 0.8364\n", + "Epoch 23/100\n", + "7200/7200 [==============================] - 0s 52us/step - loss: 0.3970 - acc: 0.8360\n", + "Epoch 24/100\n", + "7200/7200 [==============================] - 1s 76us/step - loss: 0.3965 - acc: 0.8369\n", + "Epoch 25/100\n", + "7200/7200 [==============================] - 0s 57us/step - loss: 0.3963 - acc: 0.8358\n", + "Epoch 26/100\n", + "7200/7200 [==============================] - 0s 56us/step - loss: 0.3948 - acc: 0.8376\n", + "Epoch 27/100\n", + "7200/7200 [==============================] - 0s 55us/step - loss: 0.3945 - acc: 0.8365\n", + "Epoch 28/100\n", + "7200/7200 [==============================] - 0s 55us/step - loss: 0.3938 - acc: 0.8379\n", + "Epoch 29/100\n", + "7200/7200 [==============================] - 0s 54us/step - loss: 0.3925 - acc: 0.8369\n", + "Epoch 30/100\n", + "7200/7200 [==============================] - 0s 57us/step - loss: 0.3910 - acc: 0.8392\n", + "Epoch 31/100\n", + "7200/7200 [==============================] - 0s 56us/step - loss: 0.3900 - acc: 0.8368\n", + "Epoch 32/100\n", + "7200/7200 [==============================] - 0s 56us/step - loss: 0.3890 - acc: 0.8401\n", + "Epoch 33/100\n", + "7200/7200 [==============================] - 0s 55us/step - loss: 0.3866 - acc: 0.8406\n", + "Epoch 34/100\n", + "7200/7200 [==============================] - 0s 54us/step - loss: 0.3848 - acc: 0.8404\n", + "Epoch 35/100\n", + "7200/7200 [==============================] - 0s 53us/step - loss: 0.3832 - acc: 0.8428\n", + "Epoch 36/100\n", + "7200/7200 [==============================] - 0s 56us/step - loss: 0.3821 - acc: 0.8424\n", + "Epoch 37/100\n", + "7200/7200 [==============================] - 0s 60us/step - loss: 0.3806 - acc: 0.8414\n", + "Epoch 38/100\n", + "7200/7200 [==============================] - 0s 58us/step - loss: 0.3790 - acc: 0.8412\n", + "Epoch 39/100\n", + "7200/7200 [==============================] - 0s 62us/step - loss: 0.3771 - acc: 0.8457\n", + "Epoch 40/100\n", + "7200/7200 [==============================] - 1s 76us/step - loss: 0.3758 - acc: 0.8443\n", + "Epoch 41/100\n", + "7200/7200 [==============================] - 1s 71us/step - loss: 0.3740 - acc: 0.8458\n", + "Epoch 42/100\n", + "7200/7200 [==============================] - 0s 54us/step - loss: 0.3724 - acc: 0.8450\n", + "Epoch 43/100\n", + "7200/7200 [==============================] - 0s 54us/step - loss: 0.3708 - acc: 0.8456\n", + "Epoch 44/100\n", + "7200/7200 [==============================] - 0s 56us/step - loss: 0.3687 - acc: 0.8468\n", + "Epoch 45/100\n", + "7200/7200 [==============================] - 0s 53us/step - loss: 0.3664 - acc: 0.8482\n", + "Epoch 46/100\n", + "7200/7200 [==============================] - 0s 55us/step - loss: 0.3643 - acc: 0.8482\n", + "Epoch 47/100\n", + "7200/7200 [==============================] - 0s 57us/step - loss: 0.3620 - acc: 0.8489\n", + "Epoch 48/100\n", + "7200/7200 [==============================] - 1s 86us/step - loss: 0.3604 - acc: 0.8500\n", + "Epoch 49/100\n", + "7200/7200 [==============================] - 0s 67us/step - loss: 0.3588 - acc: 0.8517\n", + "Epoch 50/100\n", + "7200/7200 [==============================] - 0s 57us/step - loss: 0.3564 - acc: 0.8554\n", + "Epoch 51/100\n", + "7200/7200 [==============================] - 0s 56us/step - loss: 0.3555 - acc: 0.8521\n", + "Epoch 52/100\n", + "7200/7200 [==============================] - 0s 55us/step - loss: 0.3542 - acc: 0.8551\n", + "Epoch 53/100\n", + "7200/7200 [==============================] - 0s 62us/step - loss: 0.3529 - acc: 0.8568\n", + "Epoch 54/100\n", + "7200/7200 [==============================] - 0s 54us/step - loss: 0.3521 - acc: 0.8551\n", + "Epoch 55/100\n", + "7200/7200 [==============================] - 0s 56us/step - loss: 0.3509 - acc: 0.8554\n", + "Epoch 56/100\n", + "7200/7200 [==============================] - 0s 51us/step - loss: 0.3501 - acc: 0.8558\n", + "Epoch 57/100\n", + "7200/7200 [==============================] - 0s 56us/step - loss: 0.3496 - acc: 0.8565\n", + "Epoch 58/100\n", + "7200/7200 [==============================] - 0s 57us/step - loss: 0.3491 - acc: 0.8565\n", + "Epoch 59/100\n", + "7200/7200 [==============================] - 0s 57us/step - loss: 0.3487 - acc: 0.8561\n", + "Epoch 60/100\n", + "7200/7200 [==============================] - 0s 55us/step - loss: 0.3480 - acc: 0.8556\n", + "Epoch 61/100\n", + "7200/7200 [==============================] - 0s 54us/step - loss: 0.3471 - acc: 0.8562\n", + "Epoch 62/100\n", + "7200/7200 [==============================] - 0s 68us/step - loss: 0.3470 - acc: 0.8553\n", + "Epoch 63/100\n", + "7200/7200 [==============================] - 0s 58us/step - loss: 0.3468 - acc: 0.8578\n", + "Epoch 64/100\n", + "7200/7200 [==============================] - 0s 59us/step - loss: 0.3458 - acc: 0.8578\n", + "Epoch 65/100\n", + "7200/7200 [==============================] - 0s 63us/step - loss: 0.3468 - acc: 0.8556\n", + "Epoch 66/100\n", + "7200/7200 [==============================] - 0s 65us/step - loss: 0.3463 - acc: 0.8572\n", + "Epoch 67/100\n", + "7200/7200 [==============================] - 0s 62us/step - loss: 0.3448 - acc: 0.8583\n", + "Epoch 68/100\n", + "7200/7200 [==============================] - 1s 70us/step - loss: 0.3444 - acc: 0.8583\n", + "Epoch 69/100\n", + "7200/7200 [==============================] - 0s 66us/step - loss: 0.3450 - acc: 0.8571\n", + "Epoch 70/100\n", + "7200/7200 [==============================] - 1s 116us/step - loss: 0.3448 - acc: 0.8571\n", + "Epoch 71/100\n", + "7200/7200 [==============================] - 0s 59us/step - loss: 0.3449 - acc: 0.8586\n", + "Epoch 72/100\n", + "7200/7200 [==============================] - 1s 83us/step - loss: 0.3431 - acc: 0.8587\n", + "Epoch 73/100\n", + "7200/7200 [==============================] - 1s 79us/step - loss: 0.3434 - acc: 0.8596\n", + "Epoch 74/100\n", + "7200/7200 [==============================] - 1s 74us/step - loss: 0.3441 - acc: 0.8568\n", + "Epoch 75/100\n", + "7200/7200 [==============================] - 0s 60us/step - loss: 0.3435 - acc: 0.8578\n", + "Epoch 76/100\n", + "7200/7200 [==============================] - 1s 74us/step - loss: 0.3426 - acc: 0.8582\n", + "Epoch 77/100\n", + "7200/7200 [==============================] - 0s 52us/step - loss: 0.3437 - acc: 0.8572\n", + "Epoch 78/100\n", + "7200/7200 [==============================] - 0s 67us/step - loss: 0.3438 - acc: 0.8569\n", + "Epoch 79/100\n", + "7200/7200 [==============================] - 0s 50us/step - loss: 0.3432 - acc: 0.8569\n", + "Epoch 80/100\n", + "7200/7200 [==============================] - 0s 50us/step - loss: 0.3423 - acc: 0.8586\n", + "Epoch 81/100\n", + "7200/7200 [==============================] - 0s 51us/step - loss: 0.3424 - acc: 0.8575\n", + "Epoch 82/100\n", + "7200/7200 [==============================] - 0s 55us/step - loss: 0.3426 - acc: 0.8578\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 83/100\n", + "7200/7200 [==============================] - 0s 59us/step - loss: 0.3422 - acc: 0.8579\n", + "Epoch 84/100\n", + "7200/7200 [==============================] - 0s 46us/step - loss: 0.3428 - acc: 0.8578\n", + "Epoch 85/100\n", + "7200/7200 [==============================] - 0s 45us/step - loss: 0.3429 - acc: 0.8593\n", + "Epoch 86/100\n", + "7200/7200 [==============================] - 1s 73us/step - loss: 0.3426 - acc: 0.8615\n", + "Epoch 87/100\n", + "7200/7200 [==============================] - 0s 50us/step - loss: 0.3421 - acc: 0.8587\n", + "Epoch 88/100\n", + "7200/7200 [==============================] - 0s 56us/step - loss: 0.3421 - acc: 0.8594\n", + "Epoch 89/100\n", + "7200/7200 [==============================] - 0s 55us/step - loss: 0.3423 - acc: 0.8587\n", + "Epoch 90/100\n", + "7200/7200 [==============================] - 0s 46us/step - loss: 0.3422 - acc: 0.8579\n", + "Epoch 91/100\n", + "7200/7200 [==============================] - 0s 46us/step - loss: 0.3419 - acc: 0.8583\n", + "Epoch 92/100\n", + "7200/7200 [==============================] - 0s 51us/step - loss: 0.3412 - acc: 0.8592\n", + "Epoch 93/100\n", + "7200/7200 [==============================] - 0s 53us/step - loss: 0.3419 - acc: 0.8594\n", + "Epoch 94/100\n", + "7200/7200 [==============================] - 0s 52us/step - loss: 0.3420 - acc: 0.8597\n", + "Epoch 95/100\n", + "7200/7200 [==============================] - 1s 81us/step - loss: 0.3418 - acc: 0.8581\n", + "Epoch 96/100\n", + "7200/7200 [==============================] - 1s 73us/step - loss: 0.3416 - acc: 0.8583\n", + "Epoch 97/100\n", + "7200/7200 [==============================] - 0s 46us/step - loss: 0.3416 - acc: 0.8599\n", + "Epoch 98/100\n", + "7200/7200 [==============================] - 1s 81us/step - loss: 0.3412 - acc: 0.8571\n", + "Epoch 99/100\n", + "7200/7200 [==============================] - 1s 76us/step - 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1s 113us/step - loss: 0.4017 - acc: 0.8332\n", + "Epoch 435/500\n", + "7200/7200 [==============================] - 1s 70us/step - loss: 0.4016 - acc: 0.8346\n", + "Epoch 436/500\n", + "7200/7200 [==============================] - 1s 79us/step - loss: 0.4018 - acc: 0.8343\n", + "Epoch 437/500\n", + "7200/7200 [==============================] - 1s 91us/step - loss: 0.4015 - acc: 0.8362\n", + "Epoch 438/500\n", + "7200/7200 [==============================] - 1s 81us/step - loss: 0.4016 - acc: 0.8346\n", + "Epoch 439/500\n", + "7200/7200 [==============================] - 1s 78us/step - loss: 0.4019 - acc: 0.8333\n", + "Epoch 440/500\n", + "7200/7200 [==============================] - 1s 82us/step - loss: 0.4018 - acc: 0.8349\n", + "Epoch 441/500\n", + "7200/7200 [==============================] - 1s 73us/step - loss: 0.4017 - acc: 0.8340\n", + "Epoch 442/500\n", + "7200/7200 [==============================] - 1s 70us/step - loss: 0.4015 - acc: 0.8347\n", + "Epoch 443/500\n", + "7200/7200 [==============================] - 1s 89us/step - loss: 0.4017 - acc: 0.8353\n", + "Epoch 444/500\n", + "7200/7200 [==============================] - 1s 85us/step - loss: 0.4019 - acc: 0.8340\n", + "Epoch 445/500\n", + "7200/7200 [==============================] - 1s 110us/step - loss: 0.4019 - acc: 0.8350\n", + "Epoch 446/500\n", + "7200/7200 [==============================] - 1s 112us/step - loss: 0.4014 - acc: 0.8360\n", + "Epoch 447/500\n", + "7200/7200 [==============================] - 1s 91us/step - loss: 0.4017 - acc: 0.8344\n", + "Epoch 448/500\n", + "7200/7200 [==============================] - 1s 85us/step - loss: 0.4016 - acc: 0.8342\n", + "Epoch 449/500\n", + "7200/7200 [==============================] - 1s 76us/step - loss: 0.4013 - acc: 0.8342\n", + "Epoch 450/500\n", + "7200/7200 [==============================] - 1s 77us/step - loss: 0.4015 - acc: 0.8349\n", + "Epoch 451/500\n", + "7200/7200 [==============================] - 1s 75us/step - loss: 0.4018 - acc: 0.8353\n", + "Epoch 452/500\n", + "7200/7200 [==============================] - 0s 65us/step - loss: 0.4018 - acc: 0.8343\n", + "Epoch 453/500\n", + "7200/7200 [==============================] - 0s 67us/step - loss: 0.4018 - acc: 0.8335\n", + "Epoch 454/500\n", + "7200/7200 [==============================] - 0s 67us/step - loss: 0.4010 - acc: 0.8333\n", + "Epoch 455/500\n", + "7200/7200 [==============================] - 0s 67us/step - loss: 0.4016 - acc: 0.8350\n", + "Epoch 456/500\n", + "7200/7200 [==============================] - 1s 70us/step - loss: 0.4016 - acc: 0.8353\n", + "Epoch 457/500\n", + "7200/7200 [==============================] - 0s 68us/step - loss: 0.4014 - acc: 0.8361\n", + "Epoch 458/500\n", + "7200/7200 [==============================] - 0s 68us/step - loss: 0.4016 - acc: 0.8350\n", + "Epoch 459/500\n", + "7000/7200 [============================>.] - ETA: 0s - loss: 0.3985 - acc: 0.8363" + ] + } + ], + "source": [ + "classifier = KerasClassifier(build_fn = build_classifier)\n", + "parameters = {'batch_size': [25, 32],\n", + " 'epochs': [100, 500],\n", + " 'optimizer': ['adam', 'rmsprop']}\n", + "\n", + "grid_search = GridSearchCV(estimator = classifier,\n", + " param_grid = parameters,\n", + " scoring = 'accuracy',\n", + " cv = 10)\n", + "\n", + "grid_search = grid_search.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "best_parameters = grid_search.best_params_\n", + "best_accuracy = grid_search.best_score_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(best_parameters)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(best_accuracy)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}