#导入相关的库import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataimport osimport timefrom tensorflow.contrib.tensorboard.plugins import projectorimport matplotlib.pyplot as pltimport numpy as np#这里用slim这个API来进行卷积网络构建slim = tf.contrib.slim
#定义卷积神经网络模型#网络架构是卷积网络--最大池化--卷积网络--最大池化---flatten---MLP-softmax的全连接MLPdef model(inputs, is_training, dropout_rate, num_classes, scope='Net'):    inputs = tf.reshape(inputs, [-1, 28, 28, 1])    with tf.variable_scope(scope):        with slim.arg_scope([slim.conv2d, slim.fully_connected],                            normalizer_fn=slim.batch_norm):            net = slim.conv2d(inputs, 32, [5, 5], padding='SAME', scope='conv1')            net = slim.max_pool2d(net, 2, stride=2, scope='maxpool1')            tf.summary.histogram("conv1", net)
            net = slim.conv2d(net, 64, [5, 5], padding='SAME', scope='conv2')            net = slim.max_pool2d(net, 2, stride=2, scope='maxpool2')            tf.summary.histogram("conv2", net)
            net = slim.flatten(net, scope='flatten')            fc1 = slim.fully_connected(net, 1024, scope='fc1')            tf.summary.histogram("fc1", fc1)
            net = slim.dropout(fc1, dropout_rate, is_training=is_training, scope='fc1-dropout')            net = slim.fully_connected(net, num_classes, scope='fc2')
            return net, fc1

def create_sprite_image(images):    """更改图片的shape"""    if isinstance(images, list):        images = np.array(images)    img_h = images.shape[1]    img_w = images.shape[2]    n_plots = int(np.ceil(np.sqrt(images.shape[0])))
    sprite_image = np.ones((img_h * n_plots, img_w * n_plots))
    for i in range(n_plots):        for j in range(n_plots):            this_filter = i * n_plots + j            if this_filter < images.shape[0]:                this_img = images[this_filter]                sprite_image[i * img_h:(i + 1) * img_h,                j * img_w:(j + 1) * img_w] = this_img
    return sprite_image

def vector_to_matrix_mnist(mnist_digits):    """把正常的mnist数字图片(batch,28*28)这个格式，转换为新的张量形状(batch,28,28)"""    return np.reshape(mnist_digits, (-1, 28, 28))

def invert_grayscale(mnist_digits):    """处理下图片颜色，黑色变白，白色边黑"""    return 1 - mnist_digits

if __name__ == "__main__":    # 定义参数    #学习率    learning_rate = 1e-4    #定义迭代参数    total_epoch = 600    #定义批量    batch_size = 200    #程序运行中打印频率    display_step = 20    #程序运行中保存结果的频率    save_step = 100    load_checkpoint = False    checkpoint_dir = "checkpoint"    checkpoint_name = 'model.ckpt'    #结果存放的路径    logs_path = "logs"    #定义我们使用多少个图片    test_size = 10000    #定义第二层路径    projector_path = 'projector'
    # 网络参数    n_input = 28 * 28   # 每个图片是28*28个像素，也就是784个特征    n_classes = 10  # MNIST数据集有0-9是个类别的结果    dropout_rate = 0.5  # Dropout的比率
    mnist = input_data.read_data_sets('MNIST-data', one_hot=True)
    # 定义计算图    x = tf.placeholder(tf.float32, [None, n_input], name='InputData')    y = tf.placeholder(tf.float32, [None, n_classes], name='LabelData')    is_training = tf.placeholder(tf.bool, name='IsTraining')    keep_prob = dropout_rate
    logits, fc1 = model(x, is_training, keep_prob, n_classes)
    with tf.name_scope('Loss'):        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))    tf.summary.scalar("loss", loss)
    with tf.name_scope('Accuracy'):        correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))        accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))    tf.summary.scalar("accuracy", accuracy)
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
    projector_dir = os.path.join(logs_path, projector_path)    path_metadata = os.path.join(projector_dir,'metadata.tsv')    path_sprites = os.path.join(projector_dir, 'mnistdigits.png')    # 检查结果目录的状态    if not os.path.exists(projector_dir):        os.makedirs(projector_dir)
    # 这里进行嵌入    mnist_test = input_data.read_data_sets('MNIST-data', one_hot=False)    batch_x_test = mnist_test.test.images[:test_size]    batch_y_test = mnist_test.test.labels[:test_size]
    embedding_var = tf.Variable(tf.zeros([test_size, 1024]), name='embedding')    assignment = embedding_var.assign(fc1)
    config = projector.ProjectorConfig()    embedding = config.embeddings.add()    embedding.tensor_name = embedding_var.name    embedding.metadata_path = os.path.join(projector_path,'metadata.tsv')    embedding.sprite.image_path = os.path.join(projector_path, 'mnistdigits.png')    embedding.sprite.single_image_dim.extend([28,28])
    # 初始化变量    init = tf.global_variables_initializer()
    # 'Saver' op to save and restore all the variables    saver = tf.train.Saver()    merged_summary_op = tf.summary.merge_all()
    # 运行计算图    with tf.Session() as sess:        sess.run(init)        # Restore model weights from previously saved model        prev_model = tf.train.get_checkpoint_state(checkpoint_dir)        if load_checkpoint:            if prev_model:                saver.restore(sess, prev_model.model_checkpoint_path)                print('Checkpoint found, {}'.format(prev_model))            else:                print('No checkpoint found')
        summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())        projector.visualize_embeddings(summary_writer, config)        start_time = time.time()        # 开始训练        for epoch in range(total_epoch):            batch_x, batch_y = mnist.train.next_batch(batch_size)            # reshapeX = np.reshape(batch_x, [-1, 28, 28, 1])            # 开始反向传播算法            sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,                                           is_training: True})            if epoch % display_step == 0:                # 计算损失和精度                cost, acc, summary = sess.run([loss, accuracy, merged_summary_op],                                              feed_dict={x: batch_x,                                                         y: batch_y,                                                         is_training: False})                elapsed_time = time.time() - start_time                start_time = time.time()                print('epoch {}, training accuracy: {:.4f}, loss: {:.5f}, time: {}'                      .format(epoch, acc, cost, elapsed_time))                summary_writer.add_summary(summary, epoch)            if epoch % save_step == 0:                # 保存训练的结果                sess.run(assignment, feed_dict={x: mnist.test.images[:test_size],                                                y: mnist.test.labels[:test_size], is_training: False})                checkpoint_path = os.path.join(checkpoint_dir, checkpoint_name)                save_path = saver.save(sess, checkpoint_path)                print("Model saved in file: {}".format(save_path))
        # 保存结果        saver.save(sess, os.path.join(logs_path, "model.ckpt"), 1)        # 创建可视化的图片        to_visualise = batch_x_test        to_visualise = vector_to_matrix_mnist(to_visualise)        to_visualise = invert_grayscale(to_visualise)        sprite_image = create_sprite_image(to_visualise)        # 保存可视化的图片        plt.imsave(path_sprites, sprite_image, cmap='gray')        # 写文件        with open(path_metadata, 'w') as f:            f.write("Index\tLabel\n")            for index, label in enumerate(batch_y_test):                f.write("%d\t%d\n" % (index, label))
        print("训练完成")