dataaspirant-handle-overfitting-deep-learning-Dropout-model.py

## Model with Dropout
from tensorflow.keras.layers import Dropout
model = Sequential()
model.add(Dense(500, input_dim=2, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

history = model.fit(x_train, y_train,
                    validation_data=(x_test, y_test),
                    epochs=500, verbose=0)