实例1:从一组看似混乱的数据中找出?≈2?的规律
使用神经网络找出其中的规律
1. 准备数据
y=2x + noise
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
plotdata = { "batchsize":[], "loss":[] }
def moving_average(a, w=10):
if len(a) < w:
return a[:]
return [val if idx < w else sum(a[(idx-w):idx])/w for idx, val in enumerate(a)]
#生成模拟数据
train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.3 # y=2x,但是加入了噪声
#显示模拟数据点
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.legend()
plt.show()
2. 搭建模型
- placeholder占位符X,Y
- W,b初始化随机[-1,1]参数
# 创建模型
# 占位符
X = tf.placeholder("float")
Y = tf.placeholder("float")
# 模型参数
W = tf.Variable(tf.random_normal([1]), name="weight")
b = tf.Variable(tf.zeros([1]), name="bias")
# 前向结构
z = tf.multiply(X, W)+ b
- 反向传播
#反向优化
cost =tf.reduce_mean( tf.square(Y - z))
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent
- 训练模型
# 初始化变量
init = tf.global_variables_initializer()
# 训练参数
training_epochs = 20
display_step = 2
# 启动session
with tf.Session() as sess:
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={X: x, Y: y})
#显示训练中的详细信息
if epoch % display_step == 0:
loss = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
print ("Epoch:", epoch+1, "cost=", loss,"W=", sess.run(W), "b=", sess.run(b))
if not (loss == "NA" ):
plotdata["batchsize"].append(epoch)
plotdata["loss"].append(loss)
print (" Finished!")
print ("cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), "W=", sess.run(W), "b=", sess.run(b))
#print ("cost:",cost.eval({X: train_X, Y: train_Y}))
- 可视化
#图形显示
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
plotdata["avgloss"] = moving_average(plotdata["loss"])
plt.figure(1)
plt.subplot(211)
plt.plot(plotdata["batchsize"], plotdata["avgloss"], 'b--')
plt.xlabel('Minibatch number')
plt.ylabel('Loss')
plt.title('Minibatch run vs. Training loss')
plt.show()
Epoch: 1 cost= 0.7557078 W= [0.7214475] b= [0.35542127] Epoch: 3 cost= 0.1474243 W= [1.6094706] b= [0.11747434] Epoch: 5 cost= 0.09202065 W= [1.8502718] b= [0.02679843] Epoch: 7 cost= 0.08676976 W= [1.9127239] b= [0.00286189] Epoch: 9 cost= 0.08602997 W= [1.9288754] b= [-0.00333569] Epoch: 11 cost= 0.08588005 W= [1.9330516] b= [-0.00493819] Epoch: 13 cost= 0.08584406 W= [1.9341311] b= [-0.00535254] Epoch: 15 cost= 0.08583492 W= [1.9344107] b= [-0.00545979] Epoch: 17 cost= 0.08583256 W= [1.9344833] b= [-0.00548763] Epoch: 19 cost= 0.08583197 W= [1.9345018] b= [-0.00549479] FInished! cost= 0.08583186 W= [1.934505] b= [-0.00549596]
print ("x=0.2,z=", sess.run(z, feed_dict={X: 0.2}))
x=0.2, z= [0.38140503]
完整代码
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
plotdata = { "batchsize":[], "loss":[] }
def moving_average(a, w=10):
if len(a) < w:
return a[:]
return [val if idx < w else sum(a[(idx-w):idx])/w for idx, val in enumerate(a)]
#生成模拟数据
train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.3 # y=2x,但是加入了噪声
#显示模拟数据点
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.legend()
plt.show()
# 创建模型
# 占位符
X = tf.placeholder("float")
Y = tf.placeholder("float")
# 模型参数
W = tf.Variable(tf.random_normal([1]), name="weight")
b = tf.Variable(tf.zeros([1]), name="bias")
# 前向结构
z = tf.multiply(X, W)+ b
#反向优化
cost =tf.reduce_mean( tf.square(Y - z))
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent
# 初始化变量
init = tf.global_variables_initializer()
# 训练参数
training_epochs = 20
display_step = 2
# 启动session
with tf.Session() as sess:
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={X: x, Y: y})
#显示训练中的详细信息
if epoch % display_step == 0:
loss = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
print ("Epoch:", epoch+1, "cost=", loss,"W=", sess.run(W), "b=", sess.run(b))
if not (loss == "NA" ):
plotdata["batchsize"].append(epoch)
plotdata["loss"].append(loss)
print (" Finished!")
print ("cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), "W=", sess.run(W), "b=", sess.run(b))
#print ("cost:",cost.eval({X: train_X, Y: train_Y}))
#图形显示
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
plotdata["avgloss"] = moving_average(plotdata["loss"])
plt.figure(1)
plt.subplot(211)
plt.plot(plotdata["batchsize"], plotdata["avgloss"], 'b--')
plt.xlabel('Minibatch number')
plt.ylabel('Loss')
plt.title('Minibatch run vs. Training loss')
plt.show()
print ("x=0.2,z=", sess.run(z, feed_dict={X: 0.2}))
实例2: 通过字典类型定义输入节点
# 创建模型
# 占位符
inputdict = {
'x': tf.placeholder("float"),
'y': tf.placeholder("float")
}
完整代码
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
plotdata = { "batchsize":[], "loss":[] }
def moving_average(a, w=10):
if len(a) < w:
return a[:]
return [val if idx < w else sum(a[(idx-w):idx])/w for idx, val in enumerate(a)]
#生成模拟数据
train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.3 # y=2x,但是加入了噪声
#图形显示
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.legend()
plt.show()
# 创建模型
# 占位符
inputdict = {
'x': tf.placeholder("float"),
'y': tf.placeholder("float")
}
# 模型参数
W = tf.Variable(tf.random_normal([1]), name="weight")
b = tf.Variable(tf.zeros([1]), name="bias")
# 前向结构
z = tf.multiply(inputdict['x'], W)+ b
#反向优化
cost =tf.reduce_mean( tf.square(inputdict['y'] - z))
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent
# 初始化变量
init = tf.global_variables_initializer()
#参数设置
training_epochs = 20
display_step = 2
# 启动session
with tf.Session() as sess:
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={inputdict['x']: x, inputdict['y']: y})
#显示训练中的详细信息
if epoch % display_step == 0:
loss = sess.run(cost, feed_dict={inputdict['x']: train_X, inputdict['y']:train_Y})
print ("Epoch:", epoch+1, "cost=", loss,"W=", sess.run(W), "b=", sess.run(b))
if not (loss == "NA" ):
plotdata["batchsize"].append(epoch)
plotdata["loss"].append(loss)
print (" Finished!")
print ("cost=", sess.run(cost, feed_dict={inputdict['x']: train_X, inputdict['y']: train_Y}), "W=", sess.run(W), "b=", sess.run(b))
#图形显示
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
plotdata["avgloss"] = moving_average(plotdata["loss"])
plt.figure(1)
plt.subplot(211)
plt.plot(plotdata["batchsize"], plotdata["avgloss"], 'b--')
plt.xlabel('Minibatch number')
plt.ylabel('Loss')
plt.title('Minibatch run vs. Training loss')
plt.show()
print ("x=0.2,z=", sess.run(z, feed_dict={inputdict['x']: 0.2}))
实例3:直接定义输入节点
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
#生成模拟数据
train_X =np.float32( np.linspace(-1, 1, 100))
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.3 # y=2x,但是加入了噪声
#图形显示
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.legend()
plt.show()
# 创建模型
# 模型参数
W = tf.Variable(tf.random_normal([1]), name="weight")
b = tf.Variable(tf.zeros([1]), name="bias")
# 前向结构
z = tf.multiply(W, train_X)+ b
#反向优化
cost =tf.reduce_mean( tf.square(train_Y - z))
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent
# 初始化变量
init = tf.global_variables_initializer()
#参数设置
training_epochs = 20
display_step = 2
# 启动session
with tf.Session() as sess:
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer)
#显示训练中的详细信息
if epoch % display_step == 0:
loss = sess.run(cost)
print ("Epoch:", epoch+1, "cost=", loss,"W=", sess.run(W), "b=", sess.run(b))
print (" Finished!")
print ("cost=", sess.run(cost), "W=", sess.run(W), "b=", sess.run(b))
实例4:通过字典类型定义学习参数
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
plotdata = { "batchsize":[], "loss":[] }
def moving_average(a, w=10):
if len(a) < w:
return a[:]
return [val if idx < w else sum(a[(idx-w):idx])/w for idx, val in enumerate(a)]
#生成模拟数据
train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.3 # y=2x,但是加入了噪声
#图形显示
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.legend()
plt.show()
# 创建模型
# 占位符
X = tf.placeholder("float")
Y = tf.placeholder("float")
# 模型参数
paradict = {
'w': tf.Variable(tf.random_normal([1])),
'b': tf.Variable(tf.zeros([1]))
}
# 前向结构
z = tf.multiply(X, paradict['w'])+ paradict['b']
#反向优化
cost =tf.reduce_mean( tf.square(Y - z))
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) #Gradient descent
# 初始化变量
init = tf.global_variables_initializer()
#参数设置
training_epochs = 20
display_step = 2
# 启动session
with tf.Session() as sess:
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={X: x, Y: y})
#显示训练中的详细信息
if epoch % display_step == 0:
loss = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
print ("Epoch:", epoch+1, "cost=", loss,"W=", sess.run(paradict['w']), "b=", sess.run(paradict['b']))
if not (loss == "NA" ):
plotdata["batchsize"].append(epoch)
plotdata["loss"].append(loss)
print (" Finished!")
print ("cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), "W=", sess.run(paradict['w']), "b=", sess.run(paradict['b']))
#图形显示
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(paradict['w']) * train_X + sess.run(paradict['b']), label='Fitted line')
plt.legend()
plt.show()
plotdata["avgloss"] = moving_average(plotdata["loss"])
plt.figure(1)
plt.subplot(211)
plt.plot(plotdata["batchsize"], plotdata["avgloss"], 'b--')
plt.xlabel('Minibatch number')
plt.ylabel('Loss')
plt.title('Minibatch run vs. Training loss')
plt.show()
print ("x=0.2,z=", sess.run(z, feed_dict={X: 0.2}))