cnn.py

# Create model structure
model = keras.Sequential([
    # Input Layer
    keras.layers.Conv2D(input_shape=(224,224,3),filters=32,kernel_size=(3,3),activation='relu'),
    keras.layers.MaxPooling2D(),
    
    # Hidden Layer
    keras.layers.Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(3,3),activation='relu'),
    keras.layers.MaxPooling2D(),
    keras.layers.Conv2D(input_shape=(224,224,3),filters=128,kernel_size=(3,3),activation='relu'),
    keras.layers.MaxPooling2D(),
    keras.layers.Flatten(),
    keras.layers.Dense(128),
    keras.layers.Activation('relu'),
    
    # Output Layer
    keras.layers.Dense(2),
    keras.layers.Activation('softmax')
])

# Compile model
model.compile(loss='categorical_crossentropy',optimizer=keras.optimizers.Adam(),metrics=['acc'])