model instant

cnn_arch.py

model = Sequential()
#Conv2D Layers
model.add(Conv2D(12, (25, 25), padding='same',input_shape=img_list.shape[1:], activation = 'relu'))
model.add(Conv2D(12, (25, 25), activation = 'relu'))
#Max Pooling Layer
model.add(MaxPooling2D(pool_size=(2, 2)))
#Conv2D Layer
model.add(Conv2D(12, (13, 13), padding='same', activation = 'relu'))
model.add(Conv2D(12, (13, 13), activation = 'relu'))
#Max Pooling
model.add(MaxPooling2D(pool_size=(2, 2)))
#Flattening Layer
model.add(Flatten())
#Dense Layer
model.add(Dense(1024, activation = 'relu'))
model.add(Dense(1, activation = 'sigmoid'))

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

model.summary()