Question

我是tensorflow的初学者，我使用 tf.expand_dims ，我得到错误，我无法理解原因，所以我错过了什么？

这是代码

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])

它在这一行中给我错误

    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'形状=（？，7,7,512）dtype = float32

注意输入是另一个网络的输出张量，它运行良好

Answer 1

Okey， numpy 部分是错误的，因为当首次调用预定函数时，它还没有用于input_imgs的提要，并且numpy代码将无法正确执行，并且我用tensorflow操作代替它，现在就可以了。

tensorfow tf.expand_dims错误

1 个答案: