ValueError: Cannot feed value of shape (100, 160) for Tensor 'Placeholder:0', which has shape '(?,

Python在使用Tensorflow過程中，運行程序出現如下錯誤：
ValueError: Cannot feed value of shape (100, 160) for Tensor ‘Placeholder:0’, which has shape '(?,16000)
錯誤的含義是TensorFlow無法爲Tensor’佔位符：0’提供形狀值。

placeholder是TensorFlow的佔位符節點，由placeholder方法創建，也是一種常量，是由用戶在調用run方法是傳遞的，也可以將placeholder理解爲一種形參。本例創建如下：

#佔位符，通過爲輸入圖像和目標輸出類別創建節點，來開始構建計算圖。
x = tf.placeholder("float", shape=[None, 16000]) #160*100
y_ = tf.placeholder("float", shape=[None, 4])

參數說明：

• dtype：數據類型，必填，默認爲value的數據類型，傳入參數爲tensorflow下的枚舉值（float32，float64…）
• shape：數據形狀，選填，不填則隨傳入數據的形狀自行變動，可以在多次調用中傳入不同形狀的數據
• name：常量名，選填，默認值不重複，根據創建順序爲（Placeholder，Placeholder_1，Placeholder_2…）

給模型喂數據，傳參如下：

x_train,x_test,y_train,y_test = train_test_split(x_data,y_data,test_size=0.2,random_state=1)
......
    batch_x = x_train[i]
    if i%100 == 0:
        train_accuracy = accuracy.eval(feed_dict={x:batch_x, y_: batch_y, keep_prob: 1.0})
        print ("step {}, training accuracy {}".format(i, train_accuracy))

圖片數據獲取及數據源如下;

for j in [file_name for file_name in list][1]:
    for i in data_read(datapath + '/' + j):
        a = cv2.imread('./' + datapath + '/' + j + '/' + i + '.jpg', cv2.IMREAD_GRAYSCALE)
        sum.append([a, j])

y_data = [v[1] for v in sum]
x_data = [v[0] for v in sum]
x_data = np.array(x_data)

在調試程序過程中，出現瞭如下等錯誤提示：

ValueError: Cannot feed value of shape (10，100, 160) for Tensor ‘Placeholder:0’, which has shape ‘(?, 16000)’

ValueError: Cannot feed value of shape (100, 160) for Tensor ‘Placeholder:0’, which has shape ‘(?, 16000)’

ValueError: Cannot feed value of shape (16000,) for Tensor ‘Placeholder:0’, which has shape ‘(?, 16000)’

根本原因是給模型喂數據的形狀（shape）不一致，解決方案如下：

    batch_x = x_train[i]
    batch_x = np.reshape(batch_x, (-1, 16000))
    if i%100 == 0:
        train_accuracy = accuracy.eval(feed_dict={x:batch_x, y_: batch_y, keep_prob: 1.0})
        print ("step {}, training accuracy {}".format(i, train_accuracy))    

使用Python numpy函數：reshape（），修改數組對象形狀爲（？，16000），batch_x = np.reshape(batch_x, (-1, 16000))。