tensorflow构建网络模型

  from __future__ import print_function import tensorflow as tf import numpy as np import matplotlib.pyplot as plt 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(-1, 1, 300)[:, np.newaxis] noise = np.random.normal(0, 0.05, x_data.shape) y_data = np.square(x_data) - 0.5 + noise ##plt.scatter(x_data, y_data) ##plt.show() # 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.initialize_all_variables() sess= tf.Session() sess.run(init) for i in range(1000): # 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}))

说明：

1、添加网络层的函数:

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 2、模型的输入和输出要用占位符表示

# define placeholder for inputs to network xs = tf.placeholder(tf.float32, [None, 1]) ys = tf.placeholder(tf.float32, [None, 1]) 3、堆叠层的方式

# 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)

4、定义好loss和优化器

# 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)

5、因为输出loss需要用到占位符。所以,要把占位符的取值通过dict传进去,在此处我们用 feed_dict={xs: x_data, ys: y_data},传进去的是全部的训练集 ，如果用batch size，可以传进去部分样本, 这里就体现出占位符的作用了

for i in range(1000): # 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}))