獲取一張圖片

函式：def get_one_image(train):

• 輸入引數：train,訓練圖片的路徑
• 返回引數：image，從訓練圖片中隨機抽取一張圖片

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

測試圖片

函式：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])

訓練過程中按步驟測試圖片

在獲取檔案時，取出訓練圖片的20%作為測試資料

函式：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]

#將所得List分為兩部分，一部分用來訓練tra，一部分用來測試val
#ratio是測試集的比例
n_sample = len(all_label_list)
n_val = math.ceil(n_sample*ratio) #測試樣本數
n_train = n_sample - n_val # 訓練樣本數

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

函式：def get_files(file_dir, ratio):中修改

獲取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，測試一批，同時記錄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)  

結果
這張圖片是貓的概率為0.987972，所用模型的訓練步驟是6000步