Tensorflow tensorfow tf.expand_dims错误

我是tensorflow的初学者，我使用了tf.expand_dims，我得到了错误，我不明白原因，所以我缺少了什么

这是密码

ML_OUTPUT = None
input_for_classification = None
def ConstructML( input_tensor, layers_count, node_for_each_layer):
    global   ML_OUTPUT 
    global input_for_classification 
    FeatureVector = np.array(input_tensor)
    FeatureVector = FeatureVector.flatten()
    print(FeatureVector.shape)                           
    ML_ModelINT(FeatureVector, layers_count, node_for_each_layer)

def ML_ModelINT(FeatureVector, layers_count, node_for_each_layer):
        hidden_layer = []
        Alloutputs = []
        hidden_layer.append({'weights': tf.Variable(tf.random_normal([FeatureVector.shape[0], node_for_each_layer[0]])),'biases': tf.Variable(tf.random_normal([node_for_each_layer[0]]))})
        for i in range(1, layers_count):
            hidden_layer.append({'weights': tf.Variable(tf.random_normal([node_for_each_layer[i - 1], node_for_each_layer[i]])),'biases': tf.Variable(tf.random_normal([node_for_each_layer[i]]))})
        FeatureVector = tf.expand_dims(FeatureVector,0)
        layers_output = tf.add(tf.matmul(FeatureVector, hidden_layer[0]['weights']), hidden_layer[0]['biases'])
        layers_output = tf.nn.relu(layers_output)
        Alloutputs.append(layers_output)
        for j in range(1, layers_count):
            layers_output = tf.add(tf.matmul(layers_output, hidden_layer[j]['weights']), hidden_layer[j]['biases'])
            layers_output = tf.nn.relu(layers_output)
            Alloutputs.append(layers_output)
        ML_OUTPUT = layers_output  
        input_for_classification = Alloutputs[1]             
        return ML_OUTPUT

 ML_Net = ConstructML(input,3,[1024,512,256])

这句话给了我一个错误

    FeatureVector = tf.expand_dims(FeatureVector,0)

错误是预期的二进制或unicode字符串，得到tf.Tensor'Relu_11:0'shape=（？，7，7，512）dtype=float32

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注意输入是另一个网络的输出张量，它工作得很好

好的，numpy部分是错误的，因为当第一次调用prediction函数时，它还没有输入imgs的馈送，numpy代码将无法正确执行，我用tensorflow ops替换了它，现在它可以工作了。

我想你的类型有些奇怪。。。在函数ConstructML中，FeatureVector是一个numpy数组。然后将其传递给ModelINT，并在其上运行tf张量操作，而不进行转换。。。