吴裕雄 python深度学习与实践(10)

import tensorflow as tf

input1 = tf.constant(1)
print(input1)

input2 = tf.Variable(2,tf.int32)
print(input2)

input2 = input1
sess = tf.Session()
print(sess.run(input2))

import tensorflow as tf

input1 = tf.placeholder(tf.int32)
input2 = tf.placeholder(tf.int32)

output = tf.add(input1, input2)

sess = tf.Session()
print(sess.run(output, feed_dict={input1:[1], input2:[2]}))

import numpy as np
import tensorflow as tf

"""
这里是一个非常好的大数据验证结果,随着数据量的上升,集合的结果也越来越接近真实值,
这也是反馈神经网络的一个比较好的应用
这里不是很需要各种激励函数
而对于dropout,这里可以看到加上dropout,loss的值更快。
随着数据量的上升,结果就更加接近于真实值。
"""

inputX = np.random.rand(3000,1)
noise = np.random.normal(0, 0.05, inputX.shape)
outputY = inputX * 4 + 1 + noise

#这里是第一层
weight1 = tf.Variable(np.random.rand(inputX.shape[1],4))
bias1 = tf.Variable(np.random.rand(inputX.shape[1],4))
x1 = tf.placeholder(tf.float64, [None, 1])
y1_ = tf.matmul(x1, weight1) + bias1

y = tf.placeholder(tf.float64, [None, 1])
loss = tf.reduce_mean(tf.reduce_sum(tf.square((y1_ - y)), reduction_indices=[1]))
train = tf.train.GradientDescentOptimizer(0.25).minimize(loss)  # 选择梯度下降法

init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

for i in range(1000):
    sess.run(train, feed_dict={x1: inputX, y: outputY})

print(weight1.eval(sess))
print("---------------------")
print(bias1.eval(sess))
print("------------------结果是------------------")

x_data = np.matrix([[1.],[2.],[3.]])
print(sess.run(y1_,feed_dict={x1: x_data}))

import numpy as np

aa = np.random.rand(5,4)
print(aa)
print(np.shape(aa))

import tensorflow as tf
import numpy as np

"""
这里是一个非常好的大数据验证结果,随着数据量的上升,集合的结果也越来越接近真实值,
这也是反馈神经网络的一个比较好的应用
这里不是很需要各种激励函数
而对于dropout,这里可以看到加上dropout,loss的值更快。
随着数据量的上升,结果就更加接近于真实值。
"""

inputX = np.random.rand(3000,1)
noise = np.random.normal(0, 0.05, inputX.shape)
outputY = inputX * 4 + 1 + noise

#这里是第一层
weight1 = tf.Variable(np.random.rand(inputX.shape[1],4))
bias1 = tf.Variable(np.random.rand(inputX.shape[1],4))
x1 = tf.placeholder(tf.float64, [None, 1])
y1_ = tf.matmul(x1, weight1) + bias1
#这里是第二层
weight2 = tf.Variable(np.random.rand(4,1))
bias2 = tf.Variable(np.random.rand(inputX.shape[1],1))
y2_ = tf.matmul(y1_, weight2) + bias2

y = tf.placeholder(tf.float64, [None, 1])

loss = tf.reduce_mean(tf.reduce_sum(tf.square((y2_ - y)), reduction_indices=[1]))
train = tf.train.GradientDescentOptimizer(0.25).minimize(loss)  # 选择梯度下降法

init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

for i in range(1000):
    sess.run(train, feed_dict={x1: inputX, y: outputY})

print(weight1.eval(sess))
print("---------------------")
print(weight2.eval(sess))
print("---------------------")
print(bias1.eval(sess))
print("---------------------")
print(bias2.eval(sess))
print("------------------结果是------------------")

x_data = np.matrix([[1.],[2.],[3.]])
print(sess.run(y2_,feed_dict={x1: x_data}))

原文地址:https://www.cnblogs.com/tszr/p/10356471.html

时间: 2024-11-06 10:01:27

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