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 tf

FLAGS = tf.app.flags.FLAGS
tf.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_data
mnist = input_data.read_data_sets("./", one_hot=True)

# Parameters
learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10

# Network Parameters
n_input = 28 # MNIST data input (img shape: 28*28)
n_steps = 28 # timesteps
n_hidden = 128 # hidden layer num of features
n_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 graph
with 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()