ValueError: Cannot feed value of shape (9, 240, 320) for Tensor 'x:0', which has shape 的解決辦法

今天寫了如下的代碼，用來測試手勢識別的神經網絡算法準確性：

from skimage import io,transform
import tensorflow as tf
import numpy as np
import os

path =  "./Images/"

dict = {0:'palm',1:'l',2:'fist',3:'fist_move',4:'thumb',5:'index',6:'ok',7:'palm_move',8:'c',9:'down'}

w=240
h=320
c=1

def read_one_image(path):
    img = io.imread(path)
    img = transform.resize(img,(w,h))
    return np.asarray(img)

with tf.Session() as sess:
    data = []

    for images in os.listdir(path):
        #data1 = read_one_image(os.path.join(path,images))
        data1 = read_one_image(path + images)
        data.append(data1)

    #data = np.array(data).reshape(-1,w,h,1)

    saver = tf.train.import_meta_graph('./modelSave/model.ckpt.meta')
    saver.restore(sess,tf.train.latest_checkpoint('./modelSave/'))

    graph = tf.get_default_graph()
    x = graph.get_tensor_by_name("x:0")
    feed_dict = {x:data}

    logits = graph.get_tensor_by_name("logits_eval:0")

    classification_result = sess.run(logits,feed_dict)

    # 打印出預測矩陣
    print(classification_result)
    # 打印出預測矩陣每一行最大值的索引
    print(tf.argmax(classification_result, 1).eval())
    # 根據索引通過字典對應花的分類
    output = []
    output = tf.argmax(classification_result, 1).eval()
    for i in range(len(output)):
        print("第",i+1,"個手勢預測:"+dict[output[i]])

可是運行的時候卻報錯：ValueError: Cannot feed value of shape (9, 240, 320) for Tensor 'x:0', which has shape '(?, 240, 320, 1)'

原來是data的維度和Tensor x不匹配，添加紅色部分的那一句data = np.array(data).reshape(-1,w,h,1)  ，

就可以正常運行了，輸出的結果如下：

...

[1 4 6 9 0 0 1 2 4]
第 1 個手勢預測:l
第 2 個手勢預測:thumb
第 3 個手勢預測:ok
第 4 個手勢預測:down
第 5 個手勢預測:palm
第 6 個手勢預測:palm
第 7 個手勢預測:l
第 8 個手勢預測:fist
第 9 個手勢預測:thumb