今天來實現tensorflow架構下的卷積神經網路，首先了解清楚我們要構建的網路架構，資料集還是用mnist，所以輸入還是28X28。

建立的卷積神經網路架構是：卷積層->pooling層->卷積層->pooling層->全連線層1->全連線層2。

全連線層的尺寸公式：N = (W − F + 2P )/S+1

引數解釋：

                 輸入圖片大小 W×W    

                    Filter大小 F×F

                    步長 S

                    padding的畫素數 

因為定義的filter所以卷積層=(28-3+2X1)/1+1=28，池化選擇2X2,所以經過兩次卷積和pooling層，特徵圖的尺寸大小=7X7,

程式碼如下：

一、匯入好工具包和資料集

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.labels
testimg    = mnist.test.images
testlabel  = mnist.test.labels
print ("MNIST ready")
 

二、定義各層引數

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))
    }

：卷積層的高斯初始化，分別對應h,w,輸入深度（灰度就1彩色就3）,得出多少特徵圖；

：卷積層的高斯初始化，h,w,深度是對應的特徵圖，輸出深度特徵圖。

：全連線層的高斯初始化，h*w*上一層的特徵圖，輸出的特徵圖。

三、定義卷積架構

def conv_basic(_input, _w, _b, _keepratio):
        # INPUT
        _input_r = tf.reshape(_input, shape=[-1, 28, 28, 1])
        # CONV LAYER 1
        _conv1 = tf.nn.conv2d(_input_r, _w['wc1'], strides=[1, 1, 1, 1], padding='SAME')
        #_mean, _var = tf.nn.moments(_conv1, [0, 1, 2])
        #_conv1 = tf.nn.batch_normalization(_conv1, _mean, _var, 0, 1, 0.0001)
        _conv1 = tf.nn.relu(tf.nn.bias_add(_conv1, _b['bc1']))
        _pool1 = tf.nn.max_pool(_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
        _pool_dr1 = tf.nn.dropout(_pool1, _keepratio)
        # CONV LAYER 2
        _conv2 = tf.nn.conv2d(_pool_dr1, _w['wc2'], strides=[1, 1, 1, 1], padding='SAME')
        #_mean, _var = tf.nn.moments(_conv2, [0, 1, 2])
        #_conv2 = tf.nn.batch_normalization(_conv2, _mean, _var, 0, 1, 0.0001)
        _conv2 = tf.nn.relu(tf.nn.bias_add(_conv2, _b['bc2']))
        _pool2 = tf.nn.max_pool(_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
        _pool_dr2 = tf.nn.dropout(_pool2, _keepratio)
        # VECTORIZE
        _dense1 = tf.reshape(_pool_dr2, [-1, _w['wd1'].get_shape().as_list()[0]])
        # FULLY CONNECTED LAYER 1
        _fc1 = tf.nn.relu(tf.add(tf.matmul(_dense1, _w['wd1']), _b['bd1']))
        _fc_dr1 = tf.nn.dropout(_fc1, _keepratio)
        # FULLY CONNECTED LAYER 2
        _out = tf.add(tf.matmul(_fc_dr1, _w['wd2']), _b['bd2'])
        # RETURN
        out = { 'input_r': _input_r, 'conv1': _conv1, 'pool1': _pool1, 'pool1_dr1': _pool_dr1,
            'conv2': _conv2, 'pool2': _pool2, 'pool_dr2': _pool_dr2, 'dense1': _dense1,
            'fc1': _fc1, 'fc_dr1': _fc_dr1, 'out': _out
        }
        return out
print ("CNN READY")

tf.nn.max_pool(value, ksize, strides, padding, data_format='NHWC', name=None)中四個數值的解釋：

value是一個四維的輸入：batch, height, width, channels。輸入經過卷積和relu層的_conv1,
_fc_dr1 = tf.nn.dropout(_fc1, _keepratio):是隨機殺死一些節點，_keepratio是比例的意思

ksize[1,2,2,1]:batchsize維度，height,width,步行strides

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_output])
keepratio = tf.placeholder(tf.float32)

# FUNCTIONS

_pred = conv_basic(x, weights, biases, keepratio)['out']
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(_pred, y))
optm = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
_corr = tf.equal(tf.argmax(_pred,1), tf.argmax(y,1)) 
accr = tf.reduce_mean(tf.cast(_corr, tf.float32)) 
init = tf.global_variables_initializer()
    
# SAVER
print ("GRAPH READY")
sess = tf.Session()
sess.run(init)

training_epochs = 15
batch_size      = 16
display_step    = 1
for epoch in range(training_epochs):
    avg_cost = 0.
    #total_batch = int(mnist.train.num_examples/batch_size)
    total_batch = 10
    # Loop over all batches
    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        # Fit training using batch data
        sess.run(optm, feed_dict={x: batch_xs, y: batch_ys, keepratio:0.7})
        # Compute average loss
        avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keepratio:1.})/total_batch

    # Display logs per epoch step
    if epoch % display_step == 0: 
        print ("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))
        train_acc = sess.run(accr, feed_dict={x: batch_xs, y: batch_ys, keepratio:1.})
        print (" Training accuracy: %.3f" % (train_acc))
        #test_acc = sess.run(accr, feed_dict={x: testimg, y: testlabel, keepratio:1.})
        #print (" Test accuracy: %.3f" % (test_acc))

print ("OPTIMIZATION FINISHED")

得出結果：

Epoch: 000/015 cost: 30.928401661
 Training accuracy: 0.500
Epoch: 001/015 cost: 12.954609606
 Training accuracy: 0.700
Epoch: 002/015 cost: 10.392489696
 Training accuracy: 0.700
Epoch: 003/015 cost: 7.254891634
 Training accuracy: 0.800
Epoch: 004/015 cost: 4.977767670
 Training accuracy: 0.900
Epoch: 005/015 cost: 5.414173813
 Training accuracy: 0.600
Epoch: 006/015 cost: 3.057567777
 Training accuracy: 0.700
Epoch: 007/015 cost: 4.929724103
 Training accuracy: 0.600
Epoch: 008/015 cost: 3.192437538
 Training accuracy: 0.600
Epoch: 009/015 cost: 3.224479928
 Training accuracy: 0.800
Epoch: 010/015 cost: 2.720530389
 Training accuracy: 0.400
Epoch: 011/015 cost: 3.000342276
 Training accuracy: 0.800
Epoch: 012/015 cost: 0.639763238
 Training accuracy: 1.000
Epoch: 013/015 cost: 1.897303332
 Training accuracy: 0.900
Epoch: 014/015 cost: 2.295500937
 Training accuracy: 0.800
OPTIMIZATION FINISHED