text_plain_output_1.png
"""
model = Sequential()
model.add(Conv2D(filters=64, kernel_size=(5, 5), input_shape=(256, 256, 3), activation='relu'))
model.add(BatchNormalization(axis=3))
model.add(Conv2D(filters=64, kernel_size=(5, 5), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(BatchNormalization(axis=3))
model.add(Dropout(0.1))
model.add(Conv2D(filters=128, kernel_size=(5, 5), activation='relu'))
model.add(BatchNormalization(axis=3))
model.add(Conv2D(filters=128, kernel_size=(5, 5), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(BatchNormalization(axis=3))
model.add(Dropout(0.1))
model.add(Conv2D(filters=256, kernel_size=(5, 5), activation='relu'))
model.add(BatchNormalization(axis=3))
model.add(Conv2D(filters=256, kernel_size=(5, 5), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(BatchNormalization(axis=3))
model.add(Dropout(0.1))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(256, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(9, activation='sigmoid'))
"""