运行无提示，也没有输出数据，求大神帮助！
-*- coding: utf-8 -*-
"""
Created on Fri Oct 4 10:01:03 2019
@author: xxj
"""
import numpy as np
from sklearn import preprocessing
import tensorflow as tf
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
#读取CSV文件数据
从CSV文件中读取数据，返回DataFrame类型的数据集合。
def zc_func_read_csv():
zc_var_dataframe = pd.read_csv("highway.csv", sep=",")
# 打乱数据集合的顺序。有时候数据文件有可能是根据某种顺序排列的，会影响到我们对数据的处理。
zc_var_dataframe = zc_var_dataframe.reindex(np.random.permutation(zc_var_dataframe.index))
return zc_var_dataframe
预处理特征值
def preprocess_features(highway):
processed_features = highway[
["line1","line2","line3","line4","line5",
"brige1","brige2","brige3","brige4","brige5",
"tunnel1","tunnel2","tunnel3","tunnel4","tunnel5",
"inter1","inter2","inter3","inter4","inter5",
"econmic1","econmic2","econmic3","econmic4","econmic5"]
]
return processed_features
预处理标签
highway=zc_func_read_csv()
x= preprocess_features(highway)
outtarget=np.array(pd.read_csv("highway1.csv"))
y=np.array(outtarget[:,[0]])
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))
#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')
#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, 25]) #原始数据的维度：25
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, 5, 5, 1])#原始数据25变成二维图片5*5
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([3*3*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, 3*3*64])#把3*3，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(100):
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###########################################################################
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()
