train.py

model = Sequential()

model.add(Conv2D(32,(3,3),input_shape = (180,180,3)))
model.add(Activation('elu'))

model.add(Conv2D(64,(3,3)))
model.add(Activation('elu'))

model.add(MaxPool2D(pool_size = (2,2)))

model.add(Conv2D(64,(3,3)))
model.add(Activation('elu'))

model.add(Conv2D(32,(3,3)))
model.add(Activation('elu'))

model.add(Conv2D(16,(3,3)))
model.add(Activation('elu'))

model.add(MaxPool2D(pool_size = (2,2)))

model.add(Conv2D(8,(3,3)))
model.add(Activation('elu'))

model.add(Conv2D(8,(3,3)))
model.add(Activation('elu'))

model.add(Flatten())

model.add(Dense(128))
model.add(BatchNormalization())
model.add(Activation('elu'))

model.add(Dense(100))
model.add(Activation('elu'))

model.add(Dense(100))
model.add(Activation('elu'))

model.add(Dense(100))
model.add(Activation('elu'))

model.add(Dropout(0.25))

model.add(Dense(100))
model.add(Activation('elu'))

model.add(Dense(trainy.shape[1]))
model.add(Activation('softmax'))

model.summary()