def read_image(self, filename): target_size = (self.configuration.get_image_height(), self.configuration.get_image_width()) image = self.process_fun( data.read_image(filename, self.configuration.get_number_of_channels()), target_size) return image
validation_steps=configuration.get_validation_steps(), callbacks=[model_checkpoint_callback], ) elif pargs.mode == "test": model.evaluate(val_dataset, steps=configuration.get_validation_steps()) elif pargs.mode == "predict": filename = input("file :") while filename != "end": target_size = ( configuration.get_image_height(), configuration.get_image_width(), ) image = process_fun( data.read_image(filename, configuration.get_number_of_channels()), target_size, ) image = image - mean_image image = tf.expand_dims(image, 0) pred = model.predict(image) pred = pred[0] # softmax to estimate probs pred = np.exp(pred - max(pred)) pred = pred / np.sum(pred) cla = np.argmax(pred) print(pred) print(cla) filename = input("file :") # save the model if pargs.save:
if pargs.mode == 'train' : history = model.fit(tr_dataset, epochs = configuration.get_number_of_epochs(), validation_data=val_dataset, validation_steps = configuration.get_validation_steps(), callbacks=[model_checkpoint_callback]) elif pargs.mode == 'test' : model.evaluate(val_dataset, steps = configuration.get_validation_steps()) elif pargs.mode == 'predict': filename = input('file :') while(filename != 'end') : target_size = (configuration.get_image_height(), configuration.get_image_width()) image = process_fun(data.read_image(filename, configuration.get_number_of_channels()), target_size ) image = image - mean_image image = tf.expand_dims(image, 0) pred = model.predict(image) pred = pred[0] #softmax to estimate probs pred = np.exp(pred - max(pred)) pred = pred / np.sum(pred) cla = np.argmax(pred) print(pred) print(cla) filename = input('file :') #save the model if pargs.save : saved_to = os.path.join(configuration.get_data_dir(),"cnn-model") model.save(saved_to)