﻿ python – 函数逼近Tensorflow - 代码日志

#### python – 函数逼近Tensorflow

``````import tensorflow as tf
import numpy as np
import math, random
import matplotlib.pyplot as plt

# Create the arrays x and y that contains the inputs and the outputs of the function to approximate
x = np.arange(0, 2*np.pi, 2*np.pi/1000).reshape((1000,1))
y = np.sin(x)
# plt.plot(x,y)
# plt.show()

# Define the number of nodes
n_nodes_hl1 = 100
n_nodes_hl2 = 100

# Define the number of outputs and the learn rate
n_classes = 1
learn_rate = 0.1

# Define input / output placeholders
x_ph = tf.placeholder('float', [None, 1])
y_ph = tf.placeholder('float')

# Routine to compute the neural network (2 hidden layers)
def neural_network_model(data):
hidden_1_layer = {'weights': tf.Variable(tf.random_normal([1, n_nodes_hl1])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}

hidden_2_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))}

output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2, n_classes])),
'biases': tf.Variable(tf.random_normal([n_classes]))}

# (input_data * weights) + biases
l1 = tf.nn.relu(l1)

l2 = tf.nn.relu(l2)

return output

# Routine to train the neural network
def train_neural_network(x_ph):
prediction = neural_network_model(x_ph)
cost = tf.reduce_mean(tf.square(prediction - y_ph))

# cycles feed forward + backprop
hm_epochs = 10

with tf.Session() as sess:
sess.run(tf.global_variables_initializer())

# Train in each epoch with the whole data
for epoch in range(hm_epochs):
epoch_loss = 0
_, c = sess.run([optimizer, cost], feed_dict = {x_ph: x, y_ph: y})
epoch_loss += c
print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', epoch_loss)

correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y_ph, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy;', accuracy.eval({x_ph: x, y_ph: x}))

# Train network
train_neural_network(x_ph)
``````

@AIdream对初始学习率收敛问题是正确的.但即使有一个lean_rate = 1.0e-9和10000个时期,错误仍然很大意味着问题是别的.

``````Epoch 0 completed out of 10 loss: 61437.30859375
Epoch 1 completed out of 10 loss: 1.2855042406744022e+21
Epoch 2 completed out of 10 loss: inf
Epoch 3 completed out of 10 loss: nan
``````

``````Epoch 0 completed out of 10 loss: 0.5000443458557129
Epoch 1 completed out of 10 loss: 0.4999848008155823
Epoch 2 completed out of 10 loss: 0.49993154406547546
Epoch 3 completed out of 10 loss: 0.4998819828033447
``````

``````Epoch 0 completed out of 100 loss: 456.28773515997455
Epoch 10 completed out of 100 loss: 6.713319106237066
Epoch 20 completed out of 100 loss: 0.24847120749460316
Epoch 30 completed out of 100 loss: 0.09907744570556076
``````