七、tensorflow之构建网络。

在前面的学习中，我们学会了如何通过tensorflow添加新层，具体代码如下：

import tensorflow as tfimport numpy as npdef 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

上段代码定义了增加层的方法，下面产生数据模拟这一过程。

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+noisexs = tf.placeholder(tf.float32, [None, 1])ys = tf.placeholder(tf.float32,[None, 1])

构建一个两层的神经网络。

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]))

下面是训练：

optimizer = tf.train.GradientDescentOptimizer(0.1)train_step = optimizer.minimize(loss)init = tf.initialize_all_variables()

接下来就是激活框架：

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:      print(sess.run(i, sess.run(loss, feed_dict={xs:      x_data, ys: y_data})))

完整的过程就是这样咯。