early_stopping = callbacks.EarlyStopping(
min_delta=0.001, # minimium amount of change to count as an improvement patience=20, # how many epochs to wait before stopping
restore_best_weights=True)
# Initialising the NN model = Sequential()
# layers
model.add(Dense(units = 16, kernel_initializer = 'uniform', activation = 'relu', input_dim = 12)) model.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu'))
model.add(Dropout(0.25))
model.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu')) model.add(Dropout(0.5))
model.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
# Compiling the ANN
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
# Train the ANN
history = model.fit(X_train, y_train, batch_size = 25, epochs = 80,callbacks=[early_stopping], validation_split=0.25)
2022-04-17 03:48:57.855815: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threa ds for best performance.
2022-04-17 03:48:58.056715: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2) In [8]:
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Epoch 1/80
7/7 [==============================] - 1s 46ms/step - loss: 0.6928 - accuracy: 0.5833 - val_loss: 0.6911 - val_accuracy: 0.8302 Epoch 2/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6919 - accuracy: 0.6346 - val_loss: 0.6892 - val_accuracy: 0.8302 Epoch 3/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6913 - accuracy: 0.6346 - val_loss: 0.6872 - val_accuracy: 0.8302 Epoch 4/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6905 - accuracy: 0.6346 - val_loss: 0.6849 - val_accuracy: 0.8302 Epoch 5/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6895 - accuracy: 0.6346 - val_loss: 0.6828 - val_accuracy: 0.8302 Epoch 6/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6886 - accuracy: 0.6346 - val_loss: 0.6807 - val_accuracy: 0.8302 Epoch 7/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6877 - accuracy: 0.6346 - val_loss: 0.6784 - val_accuracy: 0.8302 Epoch 8/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6865 - accuracy: 0.6346 - val_loss: 0.6759 - val_accuracy: 0.8302 Epoch 9/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6850 - accuracy: 0.6346 - val_loss: 0.6731 - val_accuracy: 0.8302 Epoch 10/80
7/7 [==============================] - 0s 9ms/step - loss: 0.6843 - accuracy: 0.6346 - val_loss: 0.6693 - val_accuracy: 0.8302 Epoch 11/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6824 - accuracy: 0.6346 - val_loss: 0.6650 - val_accuracy: 0.8302 Epoch 12/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6797 - accuracy: 0.6346 - val_loss: 0.6597 - val_accuracy: 0.8302 Epoch 13/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6762 - accuracy: 0.6346 - val_loss: 0.6532 - val_accuracy: 0.8302 Epoch 14/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6713 - accuracy: 0.6346 - val_loss: 0.6454 - val_accuracy: 0.8302 Epoch 15/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6635 - accuracy: 0.6346 - val_loss: 0.6348 - val_accuracy: 0.8302 Epoch 16/80
7/7 [==============================] - 0s 9ms/step - loss: 0.6601 - accuracy: 0.6346 - val_loss: 0.6211 - val_accuracy: 0.8302 Epoch 17/80
7/7 [==============================] - 0s 9ms/step - loss: 0.6415 - accuracy: 0.6346 - val_loss: 0.6035 - val_accuracy: 0.8302 Epoch 18/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6407 - accuracy: 0.6346 - val_loss: 0.5828 - val_accuracy: 0.8302 Epoch 19/80
7/7 [==============================] - 0s 8ms/step - loss: 0.6131 - accuracy: 0.6346 - val_loss: 0.5586 - val_accuracy: 0.8302 Epoch 20/80
7/7 [==============================] - 0s 8ms/step - loss: 0.5973 - accuracy: 0.6346 - val_loss: 0.5295 - val_accuracy: 0.8302 Epoch 21/80
7/7 [==============================] - 0s 8ms/step - loss: 0.5817 - accuracy: 0.6346 - val_loss: 0.4999 - val_accuracy: 0.8302 Epoch 22/80
7/7 [==============================] - 0s 8ms/step - loss: 0.5703 - accuracy: 0.6346 - val_loss: 0.4719 - val_accuracy: 0.8302 Epoch 23/80
7/7 [==============================] - 0s 8ms/step - loss: 0.5436 - accuracy: 0.6410 - val_loss: 0.4403 - val_accuracy: 0.8302 Epoch 24/80
7/7 [==============================] - 0s 8ms/step - loss: 0.5406 - accuracy: 0.6667 - val_loss: 0.4137 - val_accuracy: 0.8302 Epoch 25/80
7/7 [==============================] - 0s 8ms/step - loss: 0.5305 - accuracy: 0.7244 - val_loss: 0.3943 - val_accuracy: 0.8113 Epoch 26/80
7/7 [==============================] - 0s 9ms/step - loss: 0.5193 - accuracy: 0.7051 - val_loss: 0.3760 - val_accuracy: 0.8491 Epoch 27/80
7/7 [==============================] - 0s 9ms/step - loss: 0.5287 - accuracy: 0.7692 - val_loss: 0.3630 - val_accuracy: 0.9057 Epoch 28/80
7/7 [==============================] - 0s 9ms/step - loss: 0.5159 - accuracy: 0.7756 - val_loss: 0.3476 - val_accuracy: 0.9057 Epoch 29/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4942 - accuracy: 0.7756 - val_loss: 0.3369 - val_accuracy: 0.9057 Epoch 30/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4778 - accuracy: 0.8013 - val_loss: 0.3272 - val_accuracy: 0.9057 Epoch 31/80
7/7 [==============================] - 0s 10ms/step - loss: 0.4825 - accuracy: 0.7564 - val_loss: 0.3267 - val_accuracy: 0.9057 Epoch 32/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4568 - accuracy: 0.8013 - val_loss: 0.3338 - val_accuracy: 0.8868 Epoch 33/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4634 - accuracy: 0.7756 - val_loss: 0.3317 - val_accuracy: 0.8868 Epoch 34/80
7/7 [==============================] - 0s 8ms/step - loss: 0.4410 - accuracy: 0.8013 - val_loss: 0.3251 - val_accuracy: 0.8868 Epoch 35/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4746 - accuracy: 0.7949 - val_loss: 0.3202 - val_accuracy: 0.8868 Epoch 36/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4541 - accuracy: 0.7821 - val_loss: 0.3132 - val_accuracy: 0.8868 Epoch 37/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4544 - accuracy: 0.8077 - val_loss: 0.3081 - val_accuracy: 0.8868 Epoch 38/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4201 - accuracy: 0.7885 - val_loss: 0.3075 - val_accuracy: 0.8868 Epoch 39/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4216 - accuracy: 0.7949 - val_loss: 0.3068 - val_accuracy: 0.8868 Epoch 40/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4492 - accuracy: 0.7500 - val_loss: 0.3116 - val_accuracy: 0.8679 Epoch 41/80
7/7 [==============================] - 0s 10ms/step - loss: 0.4490 - accuracy: 0.7628 - val_loss: 0.3128 - val_accuracy: 0.8679 Epoch 42/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4206 - accuracy: 0.7885 - val_loss: 0.3111 - val_accuracy: 0.8679 Epoch 43/80
7/7 [==============================] - 0s 9ms/step - loss: 0.3887 - accuracy: 0.7756 - val_loss: 0.3134 - val_accuracy: 0.8679 Epoch 44/80
7/7 [==============================] - 0s 9ms/step - loss: 0.3973 - accuracy: 0.7628 - val_loss: 0.3132 - val_accuracy: 0.8491 Epoch 45/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4163 - accuracy: 0.7885 - val_loss: 0.3181 - val_accuracy: 0.8491 Epoch 46/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4261 - accuracy: 0.7628 - val_loss: 0.3196 - val_accuracy: 0.8491 Epoch 47/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4111 - accuracy: 0.8077 - val_loss: 0.3194 - val_accuracy: 0.8491 Epoch 48/80
7/7 [==============================] - 0s 12ms/step - loss: 0.3845 - accuracy: 0.8269 - val_loss: 0.3189 - val_accuracy: 0.8491 Epoch 49/80
7/7 [==============================] - 0s 12ms/step - loss: 0.3838 - accuracy: 0.7628 - val_loss: 0.3183 - val_accuracy: 0.8491 Epoch 50/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4235 - accuracy: 0.7949 - val_loss: 0.3166 - val_accuracy: 0.8491 Epoch 51/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4103 - accuracy: 0.8269 - val_loss: 0.3176 - val_accuracy: 0.8491 Epoch 52/80
7/7 [==============================] - 0s 8ms/step - loss: 0.4265 - accuracy: 0.7885 - val_loss: 0.3137 - val_accuracy: 0.8491 Epoch 53/80
7/7 [==============================] - 0s 8ms/step - loss: 0.3947 - accuracy: 0.8077 - val_loss: 0.3048 - val_accuracy: 0.8491 Epoch 54/80
7/7 [==============================] - 0s 9ms/step - loss: 0.3978 - accuracy: 0.8397 - val_loss: 0.3030 - val_accuracy: 0.8302 Epoch 55/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4177 - accuracy: 0.8013 - val_loss: 0.2975 - val_accuracy: 0.8679 Epoch 56/80
7/7 [==============================] - 0s 8ms/step - loss: 0.3841 - accuracy: 0.8205 - val_loss: 0.2908 - val_accuracy: 0.8679 Epoch 57/80
7/7 [==============================] - 0s 9ms/step - loss: 0.3683 - accuracy: 0.7756 - val_loss: 0.2903 - val_accuracy: 0.8679 Epoch 58/80
7/7 [==============================] - 0s 8ms/step - loss: 0.4619 - accuracy: 0.7628 - val_loss: 0.2893 - val_accuracy: 0.8679 Epoch 59/80
7/7 [==============================] - 0s 9ms/step - loss: 0.3937 - accuracy: 0.8013 - val_loss: 0.2922 - val_accuracy: 0.8491 Epoch 60/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4249 - accuracy: 0.8269 - val_loss: 0.2985 - val_accuracy: 0.8302
Epoch 61/80
7/7 [==============================] - 0s 9ms/step - loss: 0.3583 - accuracy: 0.8333 - val_loss: 0.3030 - val_accuracy: 0.8302 Epoch 62/80
7/7 [==============================] - 0s 8ms/step - loss: 0.3919 - accuracy: 0.8205 - val_loss: 0.3079 - val_accuracy: 0.8302 Epoch 63/80
7/7 [==============================] - 0s 9ms/step - loss: 0.3529 - accuracy: 0.8397 - val_loss: 0.3164 - val_accuracy: 0.8302 Epoch 64/80
7/7 [==============================] - 0s 8ms/step - loss: 0.3854 - accuracy: 0.7949 - val_loss: 0.3194 - val_accuracy: 0.8302 Epoch 65/80
7/7 [==============================] - 0s 9ms/step - loss: 0.3917 - accuracy: 0.7949 - val_loss: 0.3145 - val_accuracy: 0.8302 Epoch 66/80
7/7 [==============================] - 0s 8ms/step - loss: 0.3924 - accuracy: 0.8205 - val_loss: 0.3086 - val_accuracy: 0.8302 Epoch 67/80
7/7 [==============================] - 0s 8ms/step - loss: 0.3855 - accuracy: 0.8205 - val_loss: 0.3048 - val_accuracy: 0.8302 Epoch 68/80
7/7 [==============================] - 0s 9ms/step - loss: 0.3713 - accuracy: 0.8141 - val_loss: 0.3053 - val_accuracy: 0.8302 Epoch 69/80
7/7 [==============================] - 0s 8ms/step - loss: 0.3748 - accuracy: 0.8462 - val_loss: 0.3093 - val_accuracy: 0.8302 Epoch 70/80
7/7 [==============================] - 0s 8ms/step - loss: 0.3413 - accuracy: 0.8718 - val_loss: 0.3108 - val_accuracy: 0.8302 Epoch 71/80
7/7 [==============================] - 0s 8ms/step - loss: 0.3824 - accuracy: 0.8205 - val_loss: 0.3147 - val_accuracy: 0.8302 Epoch 72/80
7/7 [==============================] - 0s 8ms/step - loss: 0.3971 - accuracy: 0.7949 - val_loss: 0.3172 - val_accuracy: 0.8302 Epoch 73/80
7/7 [==============================] - 0s 8ms/step - loss: 0.3649 - accuracy: 0.8590 - val_loss: 0.3233 - val_accuracy: 0.8302 Epoch 74/80
7/7 [==============================] - 0s 9ms/step - loss: 0.4162 - accuracy: 0.8269 - val_loss: 0.3234 - val_accuracy: 0.8302 Epoch 75/80
7/7 [==============================] - 0s 9ms/step - loss: 0.3730 - accuracy: 0.8205 - val_loss: 0.3195 - val_accuracy: 0.8302 Epoch 76/80
7/7 [==============================] - 0s 9ms/step - loss: 0.3739 - accuracy: 0.8654 - val_loss: 0.3143 - val_accuracy: 0.8302 Epoch 77/80
7/7 [==============================] - 0s 9ms/step - loss: 0.3951 - accuracy: 0.8333 - val_loss: 0.3118 - val_accuracy: 0.8302 Epoch 78/80
7/7 [==============================] - 0s 8ms/step - loss: 0.3756 - accuracy: 0.8141 - val_loss: 0.3175 - val_accuracy: 0.8302
Accuracy and evaluation
val_accuracy = np.mean(history.history['val_accuracy']) print("\n%s: %.2f%%" % ('val_accuracy is', val_accuracy*100)) val_accuracy is: 84.74%
history_df = pd.DataFrame(history.history)
plt.plot(history_df.loc[:, ['loss']], "#CD5C5C", label='Training loss') plt.plot(history_df.loc[:, ['val_loss']],"#FF0000", label='Validation loss') plt.title('Training and Validation loss')
plt.xlabel('Epochs') plt.ylabel('Loss') plt.legend(loc="best") plt.show()
history_df = pd.DataFrame(history.history)
plt.plot(history_df.loc[:, ['accuracy']], "#CD5C5C", label='Training accuracy') plt.plot(history_df.loc[:, ['val_accuracy']],"#FF0000", label='Validation accuracy') plt.title('Training and Validation accuracy')
plt.xlabel('Epochs') plt.ylabel('Accuracy') plt.legend() plt.show() In [19]:
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