tensorflow中slim高级库的应用

                                  tensorflow中slim库学习

        在阅读用tensorflow实现的深度学习网络结构的源码时，经常会看到作者使用TF中封装的slim高级库，看起来（实际上也是）比直接调用TF的API简洁好多。为了弄懂网络源码和学习slim库应用，特地查阅了一些资料，在这里做一下学习时的记录。

        tensorflow中关于slim库的介绍

        某位博主关于上面slim英文介绍的一些翻译

        下面直接贴出我实现的一个应用slim库的代码：

import tensorflow as tfimport tensorflow.contrib.slim as slimfrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('./data/MNIST',one_hot=True)def cal_loss(y_pre,y_label):    return tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_label, logits=y_pre))def cal_accuracy(y_pre,y_label):    return tf.reduce_mean(tf.cast(tf.equal(tf.arg_max(y_pre, dimension=1),tf.arg_max(y_label, dimension=1)),tf.float32))def network(inputs,y_label):    with slim.arg_scope([slim.conv2d],######可以在列表里添加其他要简化的操作，比如再添加全连接。函数中下面的参数是默认执行的操作                        activation_fn=tf.nn.relu,########可以应用自己编写的激活函数                        weights_initializer=slim.xavier_initializer(),####默认xavier_initializer初始化权值                        biases_initializer=tf.zeros_initializer(),                        weights_regularizer=slim.l2_regularizer(0.0005),                        padding='SAME'):        print inputs.get_shape()        net = slim.conv2d(inputs,num_outputs=32,kernel_size=[3,3],stride=1,scope='conv1')        print net.get_shape()        net = slim.max_pool2d(net, kernel_size=[2,2], stride=2, scope='pool1')        print net.get_shape()        net = slim.conv2d(net, num_outputs=64, kernel_size=[3,3], stride=1, scope='conv2')        print net.get_shape()        net = slim.max_pool2d(net, kernel_size=[2,2], stride=2, scope='pool2')        print net.get_shape()        net = slim.conv2d(net,num_outputs=64,kernel_size=[3,3],scope='conv3')        print net.get_shape()        fc_flat = slim.flatten(net)        print fc_flat.get_shape()        fc1 = slim.fully_connected(fc_flat, num_outputs=512, scope='fc1')        print fc1.get_shape()        y_out = slim.fully_connected(fc1, num_outputs=10, scope='y_out')        print y_out.get_shape()                accuracy = cal_accuracy(y_out, y_label)        l2_loss = tf.add_n(slim.losses.get_regularization_losses())        return cal_loss(y_out,y_label) + l2_loss , accuracy    def main():    x_data = tf.placeholder(dtype=tf.float32, shape=[None,784], name='x_data')    y_label = tf.placeholder(dtype=tf.float32, shape=[None,10], name='y_label')        x_input = tf.reshape(x_data, shape=[-1,28,28,1], name='x_input')    loss,accuracy = network(x_input,y_label)    train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)    with tf.Session() as sess:        sess.run(tf.initialize_all_variables())        for i in range(30001):            xs,ys = mnist.train.next_batch(64)            if i % 1000 == 0:                loss_op,ac = sess.run([loss,accuracy],feed_dict={x_data:xs,y_label:ys})                print 'the %dth iteration loss: %f'%(i,loss_op)                print 'the %dth iteration accuracy: %f'%(i,ac)            sess.run(train_op,feed_dict={x_data:xs,y_label:ys})        total_acc = sess.run(accuracy,feed_dict={x_data:mnist.validation.images,y_label:mnist.validation.labels})        print 'the total accuracy: %f'%(total_acc)                    if __name__ == '__main__':    main()

代码运行结果：

        在上面添加的链接中有关于slim的详细介绍，大家仔细看看即可，有不懂的或者我写错了的地方，可以交流~