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专业: 理论和计算化学

[求助] tensorflow神经网络问题请教已有1人参与

请教下，我这段tensorflow代码误差一直收敛不了是为什么

import tensorflow as tf
import numpy as np

def add_layer(inputs, in_size, out_size, activation_function=None):
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
Wx_plus_b = tf.matmul(inputs, Weights) + biases
if activation_function is None:
      outputs = Wx_plus_b
else:
      outputs = activation_function(Wx_plus_b)
return outputs

# Make up some real data
x_data = np.linspace(0.2, 13, 33)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = 2 * x_data + 2.2 + noise

# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])
# add hidden layer
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
# add output layer
prediction = add_layer(l1, 10, 1, activation_function=None)

# the error between prediciton and real data
loss = tf.reduce_mean(tf.reduce_sum(tf.square( ys -prediction), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
# important step
init = tf.global_variables_initializer()
sess= tf.Session()
sess.run(init)

for i in range(10000):
# training
sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
if i % 50 == 0:
      # to see the step improvement
      print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))

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