Tensorflow錯誤「unhashable類型：‘numpy.ndarray’」

Question

import tensorflow as tf 
    import numpy as np 

    layer1_weight = tf.Variable(tf.zeros([2 , 3])) 
    layer1_bias = tf.Variable(tf.zeros([3 , 1])) 
    layer2_weight = tf.Variable(tf.zeros([3, 1])) 
    layer2_bias = tf.Variable(tf.constant([[0.]])) 
    input = tf.placeholder(tf.float32 , [2 , 1]) 
    result = tf.placeholder(tf.float32 ,[1 , 1]) 

    data_input = [np.float32([[0.],[0.]]) , np.float32([[0.],[1.]]) , 
    np.float32([[1.],[0.]]) , np.float32([[1.],[1.]])] 
    data_output = [np.float32([[0.]]) , np.float32([[1.]]) , 
    np.float32([[1.]]) , np.float32([[0.]])] 
    layer1_output = tf.add(tf.matmul(tf.transpose(layer1_weight) , input) , 
    layer1_bias) 
    layer2_output = tf.add(tf.matmul(tf.transpose(layer2_weight) , 
    layer1_output) , layer2_bias) 
    print (data_input[0]) 
    loss = tf.square(tf.subtract(result , layer2_output)) 
    optimizer = tf.train.GradientDescentOptimizer(0.0001) 
    train_step = optimizer.minimize(loss) 


    sess = tf.Session() 
    init = tf.global_variables_initializer() 
    sess.run(init) 

    for i in range(30) : 
     j = int(i % 4) 
     result = data_output[j] 
     sess.run(train_step , feed_dict= {input : data_input[j] , result : 
     data_output[j]}) 
     print(str(layer2_output)) 

的代碼返回錯誤

TypeError: unhashable type: 'numpy.ndarray'

在這裏，我想實現與神經網絡的異或門，但無法找到錯誤。

Answer 1

首先您將result定義爲佔位符，但稍後將其重新定義爲 result = data_output[j]。這是錯誤的時候，因爲您不能再將值填入feed_dict。