直观的感受一下卷积操作，数据集还采用mnist手写字体数据。
首先读取一幅图片：
#读取mnist中的一个数据
from __future__ import division,print_function
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
#import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/",one_hot = True)
def train_size(num):
print('Total Training Image in Dataset = '+
str(mnist.train.images.shape))
print('-----------------------------------')
x_train = mnist.train.images[:num,:]
print('x_train Examples Loaded = ' + str(x_train.shape))
y_train = mnist.train.labels[:num,:]
print('y_train Examples Loaded = ' + str(y_train.shape))
print('')
return x_train,y_train
def test_size(num):
print('Total Test Examples in Dataset = '+
str(mnist.test.images.shape))
print('-----------------------------------')
x_test = mnist.test.images[:num,:]
print('x_test Examples Loaded =' + str(x_test.shape))
y_test = mnist.test.labels[:num,:]
print('y_test Examples Loaded =' + str(y_test.shape))
return x_test,y_test
x_train,y_train = train_size(55000)#抽取全部的训练集
image = x_train[1] #读取训练集的第一个样本
对它进行显示：
image = x_train[1] #读取训练集的第一个样本
image_label = y_train[1]
image_label_num = y_train[1].argmax(axis = 0)
image = image.reshape([28,28])#调整
plt.title("Raw Picture lable = %d" %image_label_num)
plt.imshow(image,cmap = plt.get_cmap('gray_r'))
plt.show()
卷积层和池化层的设置
输入层为输入的灰度图像尺寸: -1 x 28 x 28 x 1 第一个卷积层,卷积核的大小,深度和数量 (5, 5, 1, 16) 池化后的特征张量尺寸: -1 x 14 x 14 x 16 第二个卷积层,卷积核的大小,深度和数量 (5, 5, 16, 32) 池化后的特征张量尺寸: -1 x 7 x 7 x 32 全连接层权重矩阵 1568 x 512 输出层与全连接隐藏层之间, 512 x 10
