tensorflow學習筆記七----------卷積神經網絡

卷積神經網絡比神經網絡稍微複雜一些，由於其多了一個卷積層(convolutional layer)和池化層(pooling layer)。

使用mnist數據集，n個數據，每一個數據的像素爲28*28*1=784。先讓這些數據經過第一個卷積層，在這個卷積上指定一個3*3*1的feature，這個feature的個數設爲64。接着通過一個池化層，讓這個池化層的窗口爲2*2。而後在通過一個卷積層，在這個卷積上指定一個3*3*64的feature，這個featurn的個數設置爲128,。接着通過一個池化層，讓這個池化層的窗口爲2*2。讓結果通過一個全鏈接層，這個全鏈接層大小設置爲1024，在通過第二個全鏈接層，大小設置爲10，進行分類。

import numpy as np import tensorflow as tf import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist 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")

#像素點爲784
n_input  = 784
#十分類
n_output = 10
#wc1，第一個卷積層參數，3*3*1，共有64個 #wc2，第二個卷積層參數，3*3*64，共有128個 #wd1，第一個全鏈接層參數，通過兩個池化層被壓縮到7*7 #wd2，第二個全鏈接層參數
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)) }

定義前向傳播函數。先將輸入數據預處理，變成tensorflow支持的四維圖像；進行第一層的卷積層處理，調用conv2d函數；將卷積結果用激活函數進行處理（relu函數）；將結果進行池化層處理，ksize表明窗口大小；將池化層的結果進行隨機刪除節點；進行第二層卷積和池化...；進行全鏈接層，先將數據進行reshape（此處爲7*7*128）；進行激活函數處理；得出結果。前向傳播結束。

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')
        _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')
        _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")

定義損失函數，定義優化器

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
save_step = 1 saver = tf.train.Saver(max_to_keep=3) print ("GRAPH READY")

進行迭代

do_train = 1 sess = tf.Session() sess.run(init) training_epochs = 15 batch_size = 16 display_step = 1
if do_train == 1: for epoch in range(training_epochs): avg_cost = 0. total_batch = int(mnist.train.num_examples/batch_size)
        # 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")