学习进度笔记11

观看Tensorflow案例实战视频课程11 卷积神经网络模型

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import input_data

mnist=input_data.read_data_sets('data/',one_hot=True)
trainimg=mnist.train.images
trainlabel=mnist.train.lables
testimg=mnist.test.images
testlabel=mnist.test.labels
print("MNIST ready")

n_input=784
n_output=10
weights={
    'wc1':tf.Variable(tf.random_normal([3,3,1,64],stddev=0.1)),
    'wc2':tf.Variable(tf.random_normal([3,3,64,128],stddev=0.1)),
    'wd1':tf.Variable(tf.random_normal([7*7*128,1024],stddev=0.1)),
    'wd2':tf.Variable(tf.random_normal([1024,n_output],stddev=0.1))
    }
biases={
    'bc1':tf.Variable(tf.random_normal([64],stddev=0.1)),
    'bc2':tf.Variable(tf.random_normal([128],stddev=0.1)),
    'bd1':tf.Variable(tf.random_normal([1024],stddev=0.1)),
    'bd2':tf.Variable(tf.random_normal([n_output],stddev=0.1))
    }