八、tensorflow之结果可视化_tensorflow结果可视化-优快云博客

本文介绍了一个使用TensorFlow和NumPy实现简单神经网络的例子。该网络通过训练数据拟合一条曲线，并通过可视化展示了训练过程中的预测结果变化。

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import tensorflow as tf
import numpy as np
def add_layer(inputs, input_size, out_size, activation_function == None):
  Weights = tf.Variable(tf.random_normal([input_size,   
  out_size]))
  biases = tf.Variable(tf.zeros([1, out_size]))
  Wx_plus_b = tf.matmul(inputs,Weights) + biases
  if activation_function == None:
    outputs = Wx_plus_b
  else:
    outputs = activation_function(Wx_plus_b)
  return outputs
  x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
noise = np.random_normal(0, 0.005, x_data)
y_data = np.square(x_data)-0.5+noise
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32,[None, 1])

lay1 = add_layer(xs, 1, 10, activation_function = tf.nn.relu)
prediction = add_layer(layer1, 10, 1, activation_function = None)
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduce_indices=[1]))
optimizer = tf.train.GradientDescentOptimizer(0.1)
train_step = optimizer.minimize(loss)
init = tf.initialize_all_variables()

接下来是可视化结果：
先显示原始数据：

import matplotlib.pylot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x_data,y_data)
plt.show()

再显示预测数据

with tf.Session() as sess:
  sess.run(init)
  for i in range(1000):
    sess.run(train_step, feed_dict={xs: x_data, ys:
    y_data})
    if i % 50 == 0:
      try:
        ax.lines.remove(line[0])
      except Exception:
        pass
      prediction_value = sess.run(prediction, 
      feed_dict={xs:x_data})
      lines =ax.plot(x_data, prediction_value,'r-',lw=5)
      plt.pause(0.5)