Simple CNN for image classification

cnn.py

from tensorflow.keras.models import Sequential
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense, Conv2D, MaxPooling2D

model = Sequential()

model.add(Conv2D(filters = 32, kernel_size = (3, 3), input_shape = image_shape, activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Conv2D(filters = 32, kernel_size = (3, 3), input_shape = image_shape, activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Conv2D(filters = 32, kernel_size = (3, 3), input_shape = image_shape, activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Flatten())

model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(1))
model.add(Activation('sigmoid'))

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

early_stopping = EarlyStopping(monitor = 'val_loss', patience = 2)

results = model.fit_generator(train_image_aug, epochs = 20, validation_data = test_image_aug, callbacks = [early_stopping])