training convnet from scratch on small dataset listing defining new convnet hat includes dropout model models sequential(model add(layers conv ( ( )activation='relu'input_shape=( ))model add(layers maxpooling (( ))model add(layers conv ( ( )activation='relu')model add(layers maxpooling (( ))model add(layers conv ( ( )activation='relu')model add(layers maxpooling (( ))model add(layers conv ( ( )activation='relu')model add(layers maxpooling (( ))model add(layers flatten()model add(layers dropout( )model add(layers dense( activation='relu')model add(layers dense( activation='sigmoid')model compile(loss='binary_crossentropy'optimizer=optimizers rmsprop(lr= - )metrics=['acc']let' train the network using data augmentation and dropout listing training he convnet using dat -augment at ion generat ors train_datagen imagedatageneratorrescale= / rotation_range= width_shift_range= height_shift_range= shear_range= zoom_range= horizontal_flip=true,test_datagen imagedatagenerator(rescale= / note that the validation data shouldn' be augmentedtrain_generator train_datagen flow_from_directorytrain_dirtarget target_size=( )resizes all images to directory batch_size= class_mode='binary'validation_generator test_datagen flow_from_directoryvalidation_dirtarget_size=( )batch_size= class_mode='binary'history model fit_generatortrain_generatorsteps_per_epoch= epochs= validation_data=validation_generatorvalidation_steps= licensed to because you use binary_crossentropy lossyou need binary labels