tensorflow 分布式 数据并行 异步训练 between-graph 自己写的实例 RNN

#运行方法见上两篇文章import tensorflow as tfFLAGS = tf.app.flags.FLAGStf.app.flags.DEFINE_string('job_name', '', 'One of "ps", "worker"')tf.app.flags.DEFINE_string('ps_hosts', '',                           """Comma-separated list of hostname:port for the """                           """parameter server jobs. e.g. """                           """'machine1:2222,machine2:1111,machine2:2222'""")tf.app.flags.DEFINE_string('worker_hosts', '',                           """Comma-separated list of hostname:port for the """                           """worker jobs. e.g. """                           """'machine1:2222,machine2:1111,machine2:2222'""")tf.app.flags.DEFINE_integer(        'task_id', 0, 'Task id of the replica running the training.')ps_hosts = FLAGS.ps_hosts.split(',')worker_hosts = FLAGS.worker_hosts.split(',')cluster_spec = tf.train.ClusterSpec({'ps': ps_hosts,'worker': worker_hosts})server = tf.train.Server(                    {'ps': ps_hosts,'worker': worker_hosts},                    job_name=FLAGS.job_name,                    task_index=FLAGS.task_id)print("!!!!")if FLAGS.job_name == 'ps':  server.join()print("!!!!")       from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("./", one_hot=True)# Parameterslearning_rate = 0.001training_iters = 100000batch_size = 128display_step = 10# Network Parametersn_input = 28 # MNIST data input (img shape: 28*28)n_steps = 28 # timestepsn_hidden = 128 # hidden layer num of featuresn_classes = 10 # MNIST total classes (0-9 digits)def RNN(x, weights, biases):    # Prepare data shape to match `rnn` function requirements    # Current data input shape: (batch_size, n_steps, n_input)    # Required shape: 'n_steps' tensors list of shape (batch_size, n_input)    # Permuting batch_size and n_steps    x = tf.transpose(x, [1, 0, 2])    # Reshaping to (n_steps*batch_size, n_input)    x = tf.reshape(x, [-1, n_input])    # Split to get a list of 'n_steps' tensors of shape (batch_size, n_input)    x = tf.split(0, n_steps, x)    # Define a lstm cell with tensorflow    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)    # Get lstm cell output    outputs, states = tf.nn.rnn(lstm_cell, x, dtype=tf.float32)    # Linear activation, using rnn inner loop last output    return tf.matmul(outputs[-1], weights['out']) + biases['out']with tf.device(tf.train.replica_device_setter(               worker_device="/job:worker/task:%d" % FLAGS.task_id,               cluster=cluster_spec)):  # tf Graph input  x = tf.placeholder("float", [None, n_steps, n_input])  y = tf.placeholder("float", [None, n_classes])# Define weights  weights = {    'out': tf.Variable(tf.random_normal([n_hidden, n_classes]))  }  biases = {    'out': tf.Variable(tf.random_normal([n_classes]))  }              pred = RNN(x, weights, biases)# Define loss and optimizer  cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))  optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# Evaluate model  correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))  accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# Initializing the variables  # Initializing the variables  global_step = tf.Variable(0, name='global_step', trainable=False)  init = tf.global_variables_initializer()  saver = tf.train.Saver()  tf.scalar_summary('cost', cost)  summary_op = tf.merge_all_summaries()sv = tf.train.Supervisor(is_chief=(FLAGS.task_id == 0),                            logdir="C:\\Users\\guotong1\\Desktop\\checkpoint",                            init_op=init,                            summary_op=None,                            saver=saver,                            global_step=global_step,                            save_model_secs=60)# Launch the graphwith sv.managed_session(server.target) as sess:    sess.run(init)    step = 1    # Keep training until reach max iterations    while step * batch_size < training_iters:        batch_x, batch_y = mnist.train.next_batch(batch_size)        # Reshape data to get 28 seq of 28 elements        batch_x = batch_x.reshape((batch_size, n_steps, n_input))        # Run optimization op (backprop)        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})        if step % display_step == 0:            # Calculate batch accuracy            acc = sess.run(accuracy, feed_dict={x: batch_x, y: batch_y})            # Calculate batch loss            loss = sess.run(cost, feed_dict={x: batch_x, y: batch_y})            print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \                  "{:.6f}".format(loss) + ", Training Accuracy= " + \                  "{:.5f}".format(acc))        step += 1    print("Optimization Finished!")    # Calculate accuracy for 128 mnist test images    test_len = 128    test_data = mnist.test.images[:test_len].reshape((-1, n_steps, n_input))    test_label = mnist.test.labels[:test_len]    print("Testing Accuracy:", \        sess.run(accuracy, feed_dict={x: test_data, y: test_label}))sv.stop()