python之TensorFlow 神经网络接线错误，但缺少什么

我已经创建了一个非常简单的 TensorFlow 神经网络，但显然我一定是在某处跳过了一个步骤或者混淆了来自不同教程的示例代码，因为结果是荒谬的，而且训练误差只会随着每个时期的增加而增加。

这是一个完全独立的示例 (MVCE)，尝试训练网络计算平方函数:

import tensorflow as tf 
import numpy as np 
 
# hard-coded input and labels for demonstration 
training_x = np.array([[1.], [2.],[3.],[4.],[5.]]).T 
labels_training = np.array([[1.],[4.],[9.],[16.],[25.]]).T 
 
# Hyperparameters 
num_epochs = 1000 
learning_rate = 0.001 
LAYERS = 3 
 
# setup the Neural Network 
INPUT = len(training_x) 
OUTPUT = len(labels_training) 
X = tf.placeholder(tf.float32, shape=[INPUT,None]) 
Y = tf.placeholder(tf.float32, shape=[OUTPUT, None]) 
parameters = { 
    'W1': tf.Variable(np.random.randn(LAYERS,INPUT), dtype=tf.float32), 
    'b1': tf.Variable(np.zeros([LAYERS,1]), dtype=tf.float32), 
    'W2': tf.Variable(np.random.randn(OUTPUT,LAYERS), dtype=tf.float32), 
    'b2': tf.Variable(np.zeros([OUTPUT,1]), dtype=tf.float32) 
} 
Z1 = tf.add(tf.matmul(parameters['W1'], X), parameters['b1']) # W1*X + b 
A2 = tf.nn.relu(Z1) 
Z2 = tf.add(tf.matmul(parameters['W2'], A2), parameters['b2'])  
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=Z2,  labels=Y))  
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 
init = tf.global_variables_initializer() 
 
with tf.Session() as sess: 
    sess.run(init) 
    for epoch in range(num_epochs): 
        _ , c = sess.run([optimizer, cost], feed_dict={X: training_x, Y: labels_training})  
 
        if epoch % 200 == 0: 
            print ("Cost after epoch %i: %f" % (epoch, c)) 
 
    # Test predictions by computing the output using training set as input  
    output = sess.run(Z2, feed_dict={X: training_x}) 
    print(np.array2string(output, precision=3)) 

示例输出(由于随机初始化向量的 YMMV):

Cost after epoch 0: 158.512558 
Cost after epoch 200: 227.178513 
Cost after epoch 400: 319.617218 
Cost after epoch 600: 436.471069 
Cost after epoch 800: 577.651733 
[[23.437 38.291 53.145 67.999 82.852]] 

请您参考如下方法:

我试过你的代码，我认为你应该改变成本函数。如果我将其更改为 cost = tf.reduce_mean(tf.losses.mean_squared_error(labels = Y, predictions = Z2)) 那么效果会更好。

编辑: 当我没有转置您的输入和输出数据时，它会在不到 200 个时期内将成本降低到 0。