tensorflow25《TensorFlow实战Google深度学习框架》笔记-10-02 多GPU并行 code

# 《TensorFlow实战Google深度学习框架》10 TensorBoard计算加速# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:ts10.02.py # 多GPU并行# coding=utf-8from datetime import datetimeimport osimport timeimport tensorflow as tfimport mnist_inference# 定义训练神经网络时需要用到的参数。BATCH_SIZE = 100LEARNING_RATE_BASE = 0.001LEARNING_RATE_DECAY = 0.99REGULARAZTION_RATE = 0.0001TRAINING_STEPS = 1000MOVING_AVERAGE_DECAY = 0.99N_GPU = 4# 定义日志和模型输出的路径。MODEL_SAVE_PATH = "logs_and_models/"MODEL_NAME = "model.ckpt"DATA_PATH = "output.tfrecords"# 定义输入队列得到训练数据，具体细节可以参考第七章。def get_input():    filename_queue = tf.train.string_input_producer([DATA_PATH])    reader = tf.TFRecordReader()    _, serialized_example = reader.read(filename_queue)    # 定义数据解析格式。    features = tf.parse_single_example(        serialized_example,        features={            'image_raw': tf.FixedLenFeature([], tf.string),            'pixels': tf.FixedLenFeature([], tf.int64),            'label': tf.FixedLenFeature([], tf.int64),        })    # 解析图片和标签信息。    decoded_image = tf.decode_raw(features['image_raw'], tf.uint8)    reshaped_image = tf.reshape(decoded_image, [784])    retyped_image = tf.cast(reshaped_image, tf.float32)    label = tf.cast(features['label'], tf.int32)    # 定义输入队列并返回。    min_after_dequeue = 10000    capacity = min_after_dequeue + 3 * BATCH_SIZE    return tf.train.shuffle_batch(        [retyped_image, label],        batch_size=BATCH_SIZE,        capacity=capacity,        min_after_dequeue=min_after_dequeue)# 定义损失函数。def get_loss(x, y_, regularizer, scope, reuse_variables=None):    with tf.variable_scope(tf.get_variable_scope(), reuse=reuse_variables):        y = mnist_inference.inference(x, regularizer)    cross_entropy = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(y, y_))    regularization_loss = tf.add_n(tf.get_collection('losses', scope))    loss = cross_entropy + regularization_loss    return loss# 计算每一个变量梯度的平均值。def average_gradients(tower_grads):    average_grads = []    # 枚举所有的变量和变量在不同GPU上计算得出的梯度。    for grad_and_vars in zip(*tower_grads):        # 计算所有GPU上的梯度平均值。        grads = []        for g, _ in grad_and_vars:            expanded_g = tf.expand_dims(g, 0)            grads.append(expanded_g)        grad = tf.concat(grads, 0)        grad = tf.reduce_mean(grad, 0)        v = grad_and_vars[0][1]        grad_and_var = (grad, v)        # 将变量和它的平均梯度对应起来。        average_grads.append(grad_and_var)    # 返回所有变量的平均梯度，这个将被用于变量的更新。    return average_grads# 主训练过程。def main(argv=None):    # 将简单的运算放在CPU上，只有神经网络的训练过程放在GPU上。    with tf.Graph().as_default(), tf.device('/cpu:0'):        # 定义基本的训练过程        x, y_ = get_input()        regularizer = tf.contrib.layers.l2_regularizer(REGULARAZTION_RATE)        global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)        learning_rate = tf.train.exponential_decay(            LEARNING_RATE_BASE, global_step, 60000 / BATCH_SIZE, LEARNING_RATE_DECAY)        opt = tf.train.GradientDescentOptimizer(learning_rate)        tower_grads = []        reuse_variables = False        # 将神经网络的优化过程跑在不同的GPU上。        for i in range(N_GPU):            # 将优化过程指定在一个GPU上。            with tf.device('/gpu:%d' % i):                with tf.name_scope('GPU_%d' % i) as scope:                    cur_loss = get_loss(x, y_, regularizer, scope, reuse_variables)                    reuse_variables = True                    grads = opt.compute_gradients(cur_loss)                    tower_grads.append(grads)        # 计算变量的平均梯度。        grads = average_gradients(tower_grads)        for grad, var in grads:            if grad is not None:                tf.histogram_summary('gradients_on_average/%s' % var.op.name, grad)        # 使用平均梯度更新参数。        apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)        for var in tf.trainable_variables():            tf.histogram_summary(var.op.name, var)        # 计算变量的滑动平均值。        variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)        variables_to_average = (tf.trainable_variables() + tf.moving_average_variables())        variables_averages_op = variable_averages.apply(variables_to_average)        # 每一轮迭代需要更新变量的取值并更新变量的滑动平均值。        train_op = tf.group(apply_gradient_op, variables_averages_op)        saver = tf.train.Saver(tf.all_variables())        summary_op = tf.merge_all_summaries()        init = tf.initialize_all_variables()        with tf.Session(config=tf.ConfigProto(                allow_soft_placement=True, log_device_placement=True)) as sess:            # 初始化所有变量并启动队列。            init.run()            coord = tf.train.Coordinator()            threads = tf.train.start_queue_runners(sess=sess, coord=coord)            summary_writer = tf.train.SummaryWriter(MODEL_SAVE_PATH, sess.graph)            for step in range(TRAINING_STEPS):                # 执行神经网络训练操作，并记录训练操作的运行时间。                start_time = time.time()                _, loss_value = sess.run([train_op, cur_loss])                duration = time.time() - start_time                # 每隔一段时间数据当前的训练进度，并统计训练速度。                if step != 0 and step % 10 == 0:                    # 计算使用过的训练数据个数。                    num_examples_per_step = BATCH_SIZE * N_GPU                    examples_per_sec = num_examples_per_step / duration                    sec_per_batch = duration / N_GPU                    # 输出训练信息。                    format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)')                    print(format_str % (datetime.now(), step, loss_value, examples_per_sec, sec_per_batch))                    # 通过TensorBoard可视化训练过程。                    summary = sess.run(summary_op)                    summary_writer.add_summary(summary, step)                # 每隔一段时间保存当前的模型。                if step % 1000 == 0 or (step + 1) == TRAINING_STEPS:                    checkpoint_path = os.path.join(MODEL_SAVE_PATH, MODEL_NAME)                    saver.save(sess, checkpoint_path, global_step=step)            coord.request_stop()            coord.join(threads)if __name__ == '__main__':    tf.app.run()