TensorFlow——训练自己的数据（四）模型测试-同乐学堂

参考：Tensorflow教程-猫狗大战数据集

测试一张图片

获取一张图片

函数：def get_one_image(train):

输入参数：train,训练图片的路径

返回参数：image，从训练图片中随机抽取一张图片

				n = len(train)
			
				ind = np.random.randint(0, n)
			
				img_dir = train[ind]
			
				image = Image.open(img_dir)
			
				plt.imshow(image)
			
				image = image.resize([208, 208])
			
				image = np.array(image)
			
				return image

测试图片

函数：def evaluate_one_image():

				
				with tf.Graph().as_default():
			
				BATCH_SIZE = 1
			
				N_CLASSES = 2
			
				image = tf.cast(image_array, tf.float32)
			
				image = tf.image.per_image_standardization(image)
			
				image = tf.reshape(image, [1, 208, 208, 3])
			
				logit = model.inference(image, BATCH_SIZE, N_CLASSES)
			
				logit = tf.nn.softmax(logit)
			
				x = tf.placeholder(tf.float32, shape=[208, 208, 3])
			
				# you need to change the directories to yours.
			
				logs_train_dir = 'D:/Study/Python/Projects/Cats_vs_Dogs/Logs/train'
			
				saver = tf.train.Saver()
			
				with tf.Session() as sess:
			
				print("Reading checkpoints...")
			
				ckpt = tf.train.get_checkpoint_state(logs_train_dir)
			
				if ckpt and ckpt.model_checkpoint_path:
			
				global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
			
				saver.restore(sess, ckpt.model_checkpoint_path)
			
				print('Loading success, global_step is %s' % global_step)
			
				else:
			
				print('No checkpoint file found')
			
				prediction = sess.run(logit, feed_dict={x: image_array})
			
				max_index = np.argmax(prediction)
			
				if max_index==0:
			
				print('This is a cat with possibility %.6f' %prediction[:, 0])
			
				else:
			
				print('This is a dog with possibility %.6f' %prediction[:, 1])

训练过程中按步骤测试图片

在获取文件时，取出训练图片的20%作为测试数据

函数：def get_files(file_dir, ratio):中修改

				#所有的img和lab的list
			
				all_image_list = temp[:, 0]
			
				all_label_list = temp[:, 1]
			
				#将所得List分为两部分，一部分用来训练tra，一部分用来测试val#ratio是测试集的比例
			
				n_sample = len(all_label_list)
			
				n_val = math.ceil(n_sample*ratio) #测试样本数
			
				n_train = n_sample - n_val # 训练样本数
			
				tra_images = all_image_list[0:n_train]
			
				tra_labels = all_label_list[0:n_train]
			
				tra_labels = [int(float(i)) for i in tra_labels]
			
				val_images = all_image_list[n_train:-1]
			
				val_labels = all_label_list[n_train:-1]
			
				val_labels = [int(float(i)) for i in val_labels]
			
				return tra_images,tra_labels,val_images,val_labels

函数：def get_files(file_dir, ratio):中修改

获取train和validation的batch

				train_batch, train_label_batch = input_train_val_split.get_batch(train,
			
				train_label,
			
				IMG_W,
			
				IMG_H,
			
				BATCH_SIZE,
			
				CAPACITY)
			
				val_batch, val_label_batch = input_train_val_split.get_batch(val,
			
				val_label,
			
				IMG_W,
			
				IMG_H,
			
				BATCH_SIZE,
			
				CAPACITY)

每隔200步，测试一批，同时记录log

				if step % 200 == 0 or (step + 1) == MAX_STEP:
			
				val_images, val_labels = sess.run([val_batch, val_label_batch])
			
				val_loss, val_acc = sess.run([loss, acc],
			
				feed_dict={x:val_images, y_:val_labels})
			
				print('** Step %d, val loss = %.2f, val accuracy = %.2f%% **' %(step, val_loss, val_acc*100.0))
			
				summary_str = sess.run(summary_op)
			
				val_writer.add_summary(summary_str, step)