model = Sequential()
model.add(Conv2D(24,kernel_size=3,padding='same',activation='relu',
        input_shape=(96,96,1)))
model.add(MaxPool2D())
model.add(Conv2D(48,kernel_size=3,padding='same',activation='relu'))
model.add(MaxPool2D())
model.add(Conv2D(64,kernel_size=3,padding='same',activation='relu'))
model.add(MaxPool2D(padding='same'))
model.add(Conv2D(96,kernel_size=3,padding='same',activation='relu'))
model.add(MaxPool2D(padding='same'))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(16, activation='softmax'))
model.compile(optimizer="adam", loss="categorical_crossentropy",metrics=["accuracy"])

#image generation
image_gen.fit(train_X)
train = model.fit_generator(image_gen.flow(train_X, train_label, batch_size=15),epochs=100,verbose=1,validation_data=(valid_X, valid_label),class_weight=class_weights,callbacks=[metrics],steps_per_epoch=len(train_X)/15)