Python Tensorflow错误''；无法为张量'；输入：0'；，其形状为'；（？，2）和#x27''；

此代码出现以下错误：

文件“C:/Users/lourd/Desktop/Arquivos/programmaïO/treino/IA”，第56行，模块中 错误，u=sess.run（[cost，optimizer]，feed_dict={输入：inp，目标：out}）

文件“C:\Python\Python36\lib\site packages\tensorflow\Python\client\session.py”，第900行，正在运行 运行_元数据_ptr）

文件“C:\Python\Python36\lib\site packages\tensorflow\Python\client\session.py”，第1111行，正在运行 str（subfeed\u t.get\u shape（）））

ValueError:无法为具有形状“（？，2）”的张量“输入：0”馈送形状（4，1）的值

我的代码：

 import tensorflow as tf

# Espaços reservados

inputs = tf.placeholder('float', [None, 2], name='input')
targets = tf.placeholder('float', name='Target')

# Variaveis

weight1 = tf.Variable(tf.random_normal(shape= [2, 3], stddev=0.02, name='Weight1'))
biases1 = tf.Variable(tf.random_normal(shape= [3], stddev=0.02), name='Biases1')

# Multiplicador

hlayer = tf.matmul(inputs, weight1)
hlayer += biases1

# Função de ativação

hlayer = tf.sigmoid(hlayer, name='hAtivador')

# Camada oculta crie camadas de saida e conclua a rede

weight2 = tf.Variable(tf.random_normal(shape=[3, 1], stddev=0.02), name='Weight2')
biases2 = tf.Variable(tf.random_normal(shape=[1], stddev=0.02), name='Biases2')

# Camada de saida

output = tf.matmul(hlayer, weight2)
output += biases2
output = tf.sigmoid(output, name='outActivation')

# Optimização para treinar

cost = tf.squared_difference(targets, output)
cost = tf.reduce_mean(cost)
optimizer = tf.train.AdamOptimizer().minimize(cost)

# sessao TensotFlow

import numpy as np

inp = [[0.1], [0.2], [1.0],[1.1]]
out = [[0], [1], [1], [0]]

inp = np.array(inp)
out = np.array(out)

# Começar a sessão

epochs = 4000

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    for i in range(epochs):
        error, _ =sess.run([cost,optimizer],feed_dict={inputs: inp,targets:out})
        print(i,error)

# Teste

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    for i in range(epochs):
        error, _ = sess.run([cost, optimizer], feed_dict={inputs: inp, targets: out})
        print(i, error)
    while True:
        a = input('Primeira entrada: ')
        b = input('Segunda entrada: ')
        inp = [[a, b]]
        inp = np.array(inp)
        prediction = sess.run([output], feed_dict={inputs: inp})
        print(prediction)

---

提供给占位符的数据的形状与占位符的形状不一致

inputs = tf.placeholder('float', [None, 2], name='input')

vs

---

要么将

输入

形状更改为

[None，1]

，要么为

inp

的每个条目添加第二个值，我最终将数据放错了位置。其中.0和.1分别为0.1和0.2，这没有区别-形状仍然不一致。

inp = [[0.1], [0.2], [1.0],[1.1]]