def Convolution(img):
    kernel = tf.Variable(tf.truncated_normal(shape=[180,180,3,3],stddev=0.1))
    img = img.astype('float32')
    img = tf.nn.conv2d(np.expand_dims(img,0),kernel,strides=[ 1,15,1],padding='VALID')  # + Bias1
    return img

def Max_Pool(img):
    img = tf.nn.max_pool(img,ksize=[1,2,strides=[1,padding='VALID')
    return img

GmdMiss_Folder = os.path.join(os.getcwd(),'..','Photo','GMD Miss')
GmdMiss_List = os.listdir(GmdMiss_Folder)

GMD_Miss_Y = [0,1]
GMD_Miss_Y = np.tile(GMD_Miss_Y,(len(GmdMiss_List),1))
Img_Miss_List = []

for i in range(0,len(GmdMiss_List)):
    print(i)
    Img = os.path.join(os.getcwd(),GmdMiss_Folder,GmdMiss_List[i])
    Img = cv2.imread(Img)
    Img = cv2.cvtColor(Img,cv2.COLOR_BGR2RGB)
    Img = np.array(Img)
    Img = cv2.resize(Img,dsize=(1920,1080),interpolation=cv2.INTER_AREA)
    Img_Miss_List.append(Img)
i = 0

while True:
    print(i)
    Img = Img_Miss_List[i]
    print(Img)
    print(Img)
    with tf.Session() as sess:
        graph = tf.Graph()
        with graph.as_default():
            with tf.name_scope("Convolution"):
                Img = Convolution(Img)
            with tf.name_scope("Relu_Function"):
                Img = tf.nn.relu(Img)
            with tf.name_scope("MaxPool"):
                Img = Max_Pool(Img)
                print(Img.shape)
            with tf.name_scope("Img_Fatten"):
                Img_flatten = tf.reshape(Img,[-1,30*58*3])
            with tf.name_scope("Fully_Connected"):
                X = Img_flatten    # img is X
            with tf.name_scope("Output_layer"):
                Y = tf.placeholder(tf.float32,shape=[None,3])
                W = tf.Variable(tf.zeros(shape=[30*58*3,3]))
                B = tf.Variable(tf.zeros(shape=[3]))

                with tf.name_scope("Logits"):
                    Logits = tf.matmul(Img_flatten,W) + B
                with tf.name_scope("SoftMax"):
                    Y_Pred = tf.nn.softmax(Logits)

请注意，下面问题中的代码也包含在上方的While语句中。

with tf.name_scope("Learning"):
    with tf.name_scope("Reduce_Mean"):
        Loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=Logits,labels=GMD_Miss_Y))

错误是..

  

ValueError：无法使用输入形状为[3]，[1]和[3]，[1]和[3]的'Learning / Reduce_Mean / softmax_cross_entropy_with_logits / Reshape_2'（op：'Reshape'）重塑具有3个元素的张量以使其形状为[1]（1个元素）。输入张量计算为部分形状：input [1] = [1]。