利用TensorFlow实现VGG16

上一篇文章，实现了网络的输入，这次继续完成网络的训练，网络采用VGG16的结构。其中为了方便，keep_prob无论是训练还是测试，都这成了1，大家应该根据需要feed进不同的值。网络的输入TFRecord.createBatch（），为上一篇文章中产生数据的方法。

1.定义网络参数

import tensorflow as tfimport numpy as npimport TFRecord#定义网络参数learning_rate = 0.001display_step = 5epochs = 10keep_prob = 0.5

2.定义各种类型的层

#定义卷积操作def conv_op(input_op, name, kh, kw, n_out, dh, dw):    input_op = tf.convert_to_tensor(input_op)    n_in = input_op.get_shape()[-1].value    with tf.name_scope(name) as scope:        kernel = tf.get_variable(scope+"w",                                shape = [kh, kw, n_in, n_out],                                dtype = tf.float32,                                initializer = tf.contrib.layers.xavier_initializer_conv2d())        conv = tf.nn.conv2d(input_op, kernel, (1, dh, dw, 1), padding = 'SAME')        bias_init_val = tf.constant(0.0, shape = [n_out], dtype = tf.float32)        biases = tf.Variable(bias_init_val, trainable = True, name = 'b')        z = tf.nn.bias_add(conv, biases)        activation = tf.nn.relu(z, name = scope)        return activation#定义全连接操作def fc_op(input_op, name, n_out):    n_in = input_op.get_shape()[-1].value    with tf.name_scope(name) as scope:        kernel = tf.get_variable(scope+'w',                                shape = [n_in, n_out],                                dtype = tf.float32,                                initializer = tf.contrib.layers.xavier_initializer())        biases = tf.Variable(tf.constant(0.1, shape = [n_out], dtype = tf.float32), name = 'b')        # tf.nn.relu_layer对输入变量input_op与kernel做矩阵乘法加上bias，再做RELU非线性变换得到activation        activation = tf.nn.relu_layer(input_op, kernel, biases, name = scope)         return activation    #定义池化层def mpool_op(input_op, name, kh, kw, dh, dw):    return  tf.nn.max_pool(input_op,                           ksize = [1, kh, kw, 1],                           strides = [1, dh, dw, 1],                           padding = 'SAME',                           name = name)

3. 定义网络结构

def inference_op(input_op, keep_prob):    # block 1 -- outputs 112x112x64    conv1_1 = conv_op(input_op, name="conv1_1", kh=3, kw=3, n_out=64, dh=1, dw=1)    conv1_2 = conv_op(conv1_1,  name="conv1_2", kh=3, kw=3, n_out=64, dh=1, dw=1)    pool1 = mpool_op(conv1_2,   name="pool1",   kh=2, kw=2, dw=2, dh=2)    # block 2 -- outputs 56x56x128    conv2_1 = conv_op(pool1,    name="conv2_1", kh=3, kw=3, n_out=128, dh=1, dw=1)    conv2_2 = conv_op(conv2_1,  name="conv2_2", kh=3, kw=3, n_out=128, dh=1, dw=1)    pool2 = mpool_op(conv2_2,   name="pool2",   kh=2, kw=2, dh=2, dw=2)    # # block 3 -- outputs 28x28x256    conv3_1 = conv_op(pool2,    name="conv3_1", kh=3, kw=3, n_out=256, dh=1, dw=1)    conv3_2 = conv_op(conv3_1,  name="conv3_2", kh=3, kw=3, n_out=256, dh=1, dw=1)    conv3_3 = conv_op(conv3_2,  name="conv3_3", kh=3, kw=3, n_out=256, dh=1, dw=1)        pool3 = mpool_op(conv3_3,   name="pool3",   kh=2, kw=2, dh=2, dw=2)    # block 4 -- outputs 14x14x512    conv4_1 = conv_op(pool3,    name="conv4_1", kh=3, kw=3, n_out=512, dh=1, dw=1)    conv4_2 = conv_op(conv4_1,  name="conv4_2", kh=3, kw=3, n_out=512, dh=1, dw=1)    conv4_3 = conv_op(conv4_2,  name="conv4_3", kh=3, kw=3, n_out=512, dh=1, dw=1)    pool4 = mpool_op(conv4_3,   name="pool4",   kh=2, kw=2, dh=2, dw=2)    # block 5 -- outputs 7x7x512    conv5_1 = conv_op(pool4,    name="conv5_1", kh=3, kw=3, n_out=512, dh=1, dw=1)    conv5_2 = conv_op(conv5_1,  name="conv5_2", kh=3, kw=3, n_out=512, dh=1, dw=1)    conv5_3 = conv_op(conv5_2,  name="conv5_3", kh=3, kw=3, n_out=512, dh=1, dw=1)    pool5 = mpool_op(conv5_3,   name="pool5",   kh=2, kw=2, dw=2, dh=2)    # flatten    shp = pool5.get_shape()    flattened_shape = shp[1].value * shp[2].value * shp[3].value    resh1 = tf.reshape(pool5, [-1, flattened_shape], name="resh1")    # fully connected    fc6 = fc_op(resh1, name="fc6", n_out=4096)    fc6_drop = tf.nn.dropout(fc6, keep_prob, name="fc6_drop")    fc7 = fc_op(fc6_drop, name="fc7", n_out=4096)    fc7_drop = tf.nn.dropout(fc7, keep_prob, name="fc7_drop")    logits = fc_op(fc7_drop, name="fc8", n_out=2)    return logits

4.开始训练

     cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))     optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)     correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(labels,1))     accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))     return optimizer, cost, accuracy

5. 主函数

if __name__ == "__main__":     train_filename = "/home/wc/DataSet/traffic/testTFRecord/train.tfrecords"     test_filename = "/home/wc/DataSet/traffic/testTFRecord/test.tfrecords"     image_batch, label_batch = TFRecord.createBatch(filename = train_filename, batchsize=2)     test_image, test_label = TFRecord.createBatch(filename = test_filename, batchsize=20)     pred = inference_op(input_op = image_batch, keep_prob = keep_prob)     test_pred = inference_op(input_op = test_image, keep_prob = keep_prob)     optimizer, cost, accuracy = train(logits = pred, labels = label_batch)     test_optimizer, test_cost, test_acc = train(logits = test_pred, labels = test_label)     initop = tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())     with tf.Session() as sess:           sess.run(initop)        coord = tf.train.Coordinator()        threads = tf.train.start_queue_runners(sess = sess, coord = coord)                step = 0        while step < epochs:            step += 1            print step            _, loss, acc = sess.run([optimizer,cost,accuracy])            if step % display_step ==0:                  print loss,acc        print "training finish!"        _, testLoss, testAcc = sess.run([test_optimizer,test_cost,test_acc])        print "Test acc = "+ str(testAcc)        print "Test Finish!"

其中的一些参数，都是随便设置的，我们的目的仅仅是将网络结构搭好，把数据feed进网络，将网络训练起来，其中epochs参数，是通过 总样本数 20 / batch_size 2 计算得来的，因为上篇文章中，读取tfrecord数据的方法中，创建了一个文件队列，

filename_queue = tf.train.string_input_producer([filename], shuffle=False,num_epochs = 1)

这里的num_epochs = 1，使得数据只能被读取一个周期，如果不设置，则可以重复读取。