from keras.models import Sequential
from keras.layers import Flatten, Dense, Dropout

model = Sequential()
model.add(Flatten())
# block5_pool shape (input_dim = 4*4*512) using 150x150 pixels
model.add(Dense(256, activation = 'relu', input_dim = 4*4*512)) 
model.add(Dropout(0.5))
model.add(Dense(1, activation = 'sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

EPOCHS = 20
history = model.fit(train_activations, train_labels, epochs = EPOCHS,
                    batch_size = 32, validation_data =
                 (validation_activations, validation_labels), verbose = 0)

plot(history)
