将tensorflow 1的源代码转化成tensorflow 2

原地址 https://blog.csdn.net/baixiaozhe/article/details/54409966

使用keras

import numpy as np
from sklearn import preprocessing
import tensorflow.compat.v1 as tf
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

# 波士顿房价数据
boston = load_boston()
x = boston.data
y = boston.target
x_3 = x[:, 3:6]
x = np.column_stack([x, x_3])  # 随意给x增加了3列，x变为16列，可以reshape为4*4矩阵了 没啥用，就是凑个正方形

print('##################################################################')

# 随机挑选
train_x_disorder, test_x_disorder, train_y_disorder, test_y_disorder = train_test_split(x, y,
                                                                  train_size=0.8, random_state=33)
# 数据标准化
ss_x = preprocessing.StandardScaler()
train_x_disorder = ss_x.fit_transform(train_x_disorder)
test_x_disorder = ss_x.transform(test_x_disorder)

ss_y = preprocessing.StandardScaler()
train_y_disorder = ss_y.fit_transform(train_y_disorder.reshape(-1, 1))
test_y_disorder = ss_y.transform(test_y_disorder.reshape(-1, 1))


# 准确率计算
# def compute_accuracy(v_xs, v_ys):
#     global prediction
#     y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})
#     correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
#     accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#     result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})
#     return result

# 变厚矩阵
def weight_variable(shape):
   initial = tf.truncated_normal(shape, stddev=0.1)
   return tf.Variable(initial)


# 偏置
def bias_variable(shape):
   initial = tf.constant(0.1, shape=shape)
   return tf.Variable(initial)


# 卷积处理 变厚过程
def conv2d(x, W):
   # stride [1, x_movement, y_movement, 1] x_movement、y_movement就是步长
   # Must have strides[0] = strides[3] = 1 padding='SAME'表示卷积后长宽不变
   return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

tf.disable_v2_behavior()
# pool 长宽缩小一倍
def max_pool_2x2(x):
   # stride [1, x_movement, y_movement, 1]
   return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 16])  # 原始数据的维度：16
ys = tf.placeholder(tf.float32, [None, 1])  # 输出数据为维度：1

keep_prob = tf.placeholder(tf.float32)  # dropout的比例

x_image = tf.reshape(xs, [-1, 4, 4, 1])  # 原始数据16变成二维图片4*4
## conv1 layer ##第一卷积层
W_conv1 = weight_variable([2, 2, 1, 32])  # patch 2x2, in size 1, out size 32,每个像素变成32个像素，就是变厚的过程
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)  # output size 2x2x32，长宽不变，高度为32的三维图像
# h_pool1 = max_pool_2x2(h_conv1)     # output size 2x2x32 长宽缩小一倍

## conv2 layer ##第二卷积层

W_conv2 = weight_variable([2, 2, 32, 64])  # patch 2x2, in size 32, out size 64
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2) + b_conv2)  # 输入第一层的处理结果 输出shape 4*4*64

## fc1 layer ##  full connection 全连接层
W_fc1 = weight_variable([4 * 4 * 64, 512])  # 4x4 ，高度为64的三维图片，然后把它拉成512长的一维数组
b_fc1 = bias_variable([512])

h_pool2_flat = tf.reshape(h_conv2, [-1, 4 * 4 * 64])  # 把4*4，高度为64的三维图片拉成一维数组 降维处理
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)  # 把数组中扔掉比例为keep_prob的元素
## fc2 layer ## full connection
W_fc2 = weight_variable([512, 1])  # 512长的一维数组压缩为长度为1的数组
b_fc2 = bias_variable([1])  # 偏置
# 最后的计算结果
prediction = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
# prediction = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
# 计算 predition与y 差距 所用方法很简单就是用 suare()平方,sum()求和,mean()平均值
cross_entropy = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))
# 0.01学习效率,minimize(loss)减小loss误差
train_step = tf.train.AdamOptimizer(0.01).minimize(cross_entropy)

sess = tf.Session()
# important step
# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
sess.run(tf.global_variables_initializer())
# 训练500次
for i in range(3):
   sess.run(train_step, feed_dict={xs: train_x_disorder, ys: train_y_disorder, keep_prob: 0.7})
   print(i, '误差=', sess.run(cross_entropy, feed_dict={xs: train_x_disorder, ys: train_y_disorder, keep_prob: 1.0}))  # 输出loss值

# 可视化
prediction_value = sess.run(prediction, feed_dict={xs: test_x_disorder, ys: test_y_disorder, keep_prob: 1.0})
###画图###########################################################################
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(20, 3))  # dpi参数指定绘图对象的分辨率，即每英寸多少个像素，缺省值为80
axes = fig.add_subplot(1, 1, 1)
line1, = axes.plot(range(len(prediction_value)), prediction_value, 'b--', label='cnn', linewidth=2)
# line2,=axes.plot(range(len(gbr_pridict)), gbr_pridict, 'r--',label='优选参数')
line3, = axes.plot(range(len(test_y_disorder)), test_y_disorder, 'g', label='实际')

axes.grid()
fig.tight_layout()
# plt.legend(handles=[line1, line2,line3])
plt.legend(handles=[line1, line3])
plt.title('卷积神经网络')
plt.show()

人呢，tf2可以用【doge】

要使用keras搭建网络结构