用于回归检验的张量流神经网络多层感知器

Question

我正试图用TensorFlow编写一个MLP（我刚刚开始学习，所以对代码表示歉意！）对于多元回归（请不要列出）。这是我的MWE，在这里我选择使用来自sklearn的datasets.load_linnerud.html#sklearn.datasets.load_linnerud" rel="noreferrer">linnerud数据集。（实际上，我使用的是一个更大的数据集，这里我只使用一个层，因为我想使MWE更小，但如果需要，我可以添加）。顺便说一句，我之所以在train_test_split中使用shuffle = False，是因为实际上我使用的是时间序列数据集。

MWE

######################### import stuff ##########################
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.datasets import load_linnerud
from sklearn.model_selection import train_test_split


######################## prepare the data ########################
X, y = load_linnerud(return_X_y = True)
X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle = False, test_size = 0.33)


######################## set learning variables ##################
learning_rate = 0.0001
epochs = 100
batch_size = 3


######################## set some variables #######################
x = tf.placeholder(tf.float32, [None, 3], name = 'x')   # 3 features
y = tf.placeholder(tf.float32, [None, 3], name = 'y')   # 3 outputs

# input-to-hidden layer1
W1 = tf.Variable(tf.truncated_normal([3,300], stddev = 0.03), name = 'W1')
b1 = tf.Variable(tf.truncated_normal([300]), name = 'b1')  

# hidden layer1-to-output
W2 = tf.Variable(tf.truncated_normal([300,3], stddev = 0.03), name=  'W2')    
b2 = tf.Variable(tf.truncated_normal([3]), name = 'b2')   


######################## Activations, outputs ######################
# output hidden layer 1
hidden_out = tf.nn.relu(tf.add(tf.matmul(x, W1), b1))   

# total output
y_ = tf.nn.relu(tf.add(tf.matmul(hidden_out, W2), b2)) 


####################### Loss Function  #########################
mse = tf.losses.mean_squared_error(y, y_)


####################### Optimizer      #########################
optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(mse)  


###################### Initialize, Accuracy and Run #################
# initialize variables
init_op = tf.global_variables_initializer()

# accuracy for the test set
accuracy = tf.reduce_mean(tf.square(tf.subtract(y, y_))) # or could use tf.losses.mean_squared_error

#run
with tf.Session() as sess:
     sess.run(init_op)
     total_batch = int(len(y_train) / batch_size)  
     for epoch in range(epochs):
         avg_cost = 0
         for i in range(total_batch):
              batch_x, batch_y =  X_train[i*batch_size:min(i*batch_size + batch_size, len(X_train)), :], y_train[i*batch_size:min(i*batch_size + batch_size, len(y_train)), :] 
              _, c = sess.run([optimizer, mse], feed_dict = {x: batch_x, y: batch_y}) 
              avg_cost += c / total_batch
         print('Epoch:', (epoch+1), 'cost =', '{:.3f}'.format(avg_cost))
     print(sess.run(mse, feed_dict = {x: X_test, y:y_test})) 

这个印出来的东西是这样的

...
Epoch: 98 cost = 10992.617
Epoch: 99 cost = 10992.592
Epoch: 100 cost = 10992.566
11815.1

很明显是出了什么问题。我怀疑问题出在成本函数/准确性或我使用批量的方式上，但我不太明白。。