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#!/usr/bin/python2.7
#coding:utf-8
from __future__ import print_function
import tensorflow as tf
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("../Mnist_data/", one_hot=True)
print(mnist)
# Parameters setting
learning_rate = 0.01
training_epochs = 25 # 训练迭代的次数
batch_size = 100 # 一次输入的样本
display_step = 1
# set the tf Graph Input & set the model weights
x = tf.placeholder(dtype=tf.float32, shape=[None,784], name="input_x")
y = tf.placeholder(dtype=tf.float32, shape=[None,10], name="input_y")
#set models weights,bias
W=tf.Variable(tf.zeros([784,10]))
b=tf.Variable(tf.zeros([10]))
# Construct the model
pred=tf.nn.softmax(tf.matmul(x,W)+b) # 归一化,the possibility of getting the right value
# Minimize error using cross entropy & set the gradient descent
cost=tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred),reduction_indices=1)) #交叉熵，reducion_indices=1横向求和
optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
# Start training
with tf.Session() as sess:
# Run the initializer
sess.run(init)
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(mnist.train.num_examples/batch_size)
# Loop over all batches
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,
y: batch_ys})
# Compute average loss
avg_cost += c / total_batch
# Display logs per epoch step
if (epoch+1) % display_step == 0:
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
print("Optimization Finished!")
# Test model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
linear regression
from __future__ import print_function
import tensorflow as tf
import numpy as np
def add_layer(inputs, in_size, out_size, activation_function=None):
# add one more layer and return the output of this layer
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
# 1.训练的数据 # 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
# 2.定义节点准备接收数据
# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])
# 3.定义神经层：隐藏层和预测层
# add hidden layer 输入值是 xs，在隐藏层有 10 个神经元
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
# add output layer 输入值是隐藏层 l1，在预测层输出 1 个结果
prediction = add_layer(l1, 10, 1, activation_function=None)
# 4.定义 loss 表达式
# the error between prediciton and real data
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))
# 5.选择 optimizer 使 loss 达到最小
# 这一行定义了用什么方式去减少 loss，学习率是 0.1
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
# important step 对所有变量进行初始化
init = tf.initialize_all_variables()
with tf.Session() as sess:
# 上面定义的都没有运算，直到 sess.run 才会开始运算
sess.run(init)
# 迭代 1000 次学习，sess.run optimizer
for epoch in range(1000):
# training train_step 和 loss 都是由 placeholder 定义的运算，所以这里要用 feed 传入参数
_, cost = sess.run([train_step, loss], feed_dict={xs: x_data, ys: y_data})
if (epoch+1) % 50 == 0:
# to see the step improvement
print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(cost))
