tensorflow学习笔记七----------卷积神经网络
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tensorflow学习笔记七----------卷积神经网络
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卷積神經網絡比神經網絡稍微復雜一些，因為其多了一個卷積層(convolutional layer)和池化層(pooling layer)。
使用mnist數據集，n個數據，每個數據的像素為28*28*1=784。先讓這些數據通過第一個卷積層，在這個卷積上指定一個3*3*1的feature，這個feature的個數設為64。接著經過一個池化層，讓這個池化層的窗口為2*2。然后在經過一個卷積層，在這個卷積上指定一個3*3*64的feature，這個featurn的個數設置為128,。接著經過一個池化層，讓這個池化層的窗口為2*2。讓結果經過一個全連接層，這個全連接層大小設置為1024，在經過第二個全連接層，大小設置為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")
#像素點為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，第一個全連接層參數，經過兩個池化層被壓縮到7*7
#wd2，第二個全連接層參數
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代表窗口大小；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...；進行全連接層，先將數據進行reshape（此處為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")
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