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323 lines
12 KiB
323 lines
12 KiB
#!/usr/bin/env python
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from six.moves import xrange # pylint: disable=redefined-builtin
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import re
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import math
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import time
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import numpy as np
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from datetime import datetime
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import reader
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import tensorflow as tf
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from tensorflow.python.ops import rnn
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FLAGS = tf.app.flags.FLAGS
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tf.app.flags.DEFINE_integer('batch_size', 64, """Batch size.""")
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tf.app.flags.DEFINE_integer('num_batches', 100, """Number of batches to run.""")
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tf.app.flags.DEFINE_integer('num_layers', 1, """Number of batches to run.""")
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tf.app.flags.DEFINE_integer('max_len', 100, """Number of batches to run.""")
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tf.app.flags.DEFINE_integer('hidden_size', 128, """Number of batches to run.""")
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tf.app.flags.DEFINE_integer('emb_size', 64, """Number of batches to run.""")
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tf.app.flags.DEFINE_boolean('log_device_placement', False,
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"""Whether to log device placement.""")
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tf.app.flags.DEFINE_integer('num_gpus', 4, """How many GPUs to use.""")
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VOCAB_SIZE = 30000
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NUM_CLASS = 2
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NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
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NUM_EPOCHS_PER_DECAY = 50
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INITIAL_LEARNING_RATE = 0.1
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LEARNING_RATE_DECAY_FACTOR = 0.1
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TOWER_NAME = 'tower'
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train_dataset = reader.create_datasets("imdb.pkl", VOCAB_SIZE)
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def get_incoming_shape(incoming):
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""" Returns the incoming data shape """
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if isinstance(incoming, tf.Tensor):
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return incoming.get_shape().as_list()
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elif type(incoming) in [np.array, list, tuple]:
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return np.shape(incoming)
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else:
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raise Exception("Invalid incoming layer.")
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# Note input * W is done in LSTMCell,
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# which is different from PaddlePaddle
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def single_lstm(name,
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incoming,
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n_units,
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use_peepholes=True,
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return_seq=False,
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return_state=False):
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with tf.name_scope(name) as scope:
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cell = tf.nn.rnn_cell.LSTMCell(n_units, use_peepholes=use_peepholes)
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output, _cell_state = rnn.rnn(cell, incoming, dtype=tf.float32)
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out = output if return_seq else output[-1]
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return (out, _cell_state) if return_state else out
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def lstm(name,
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incoming,
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n_units,
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use_peepholes=True,
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return_seq=False,
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return_state=False,
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num_layers=1):
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with tf.name_scope(name) as scope:
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lstm_cell = tf.nn.rnn_cell.LSTMCell(
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n_units, use_peepholes=use_peepholes)
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cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * num_layers)
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initial_state = cell.zero_state(FLAGS.batch_size, dtype=tf.float32)
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if not isinstance(incoming, list):
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# if the input is embeding, the Tensor shape : [None, time_step, emb_size]
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incoming = [
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tf.squeeze(input_, [1])
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for input_ in tf.split(1, FLAGS.max_len, incoming)
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]
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outputs, state = tf.nn.rnn(cell,
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incoming,
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initial_state=initial_state,
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dtype=tf.float32)
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out = outputs if return_seq else outputs[-1]
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return (out, _cell_state) if return_state else out
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def embedding(name, incoming, vocab_size, emb_size):
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with tf.name_scope(name) as scope:
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#with tf.device("/cpu:0"):
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embedding = tf.get_variable(
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name + '_emb', [vocab_size, emb_size], dtype=tf.float32)
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out = tf.nn.embedding_lookup(embedding, incoming)
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return out
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def fc(name, inpOp, nIn, nOut, act=True):
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with tf.name_scope(name) as scope:
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kernel = tf.get_variable(
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name + '_w', [nIn, nOut],
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initializer=tf.truncated_normal_initializer(
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stddev=0.01, dtype=tf.float32),
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dtype=tf.float32)
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biases = tf.get_variable(
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name + '_b', [nOut],
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initializer=tf.constant_initializer(
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value=0.0, dtype=tf.float32),
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dtype=tf.float32,
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trainable=True)
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net = tf.nn.relu_layer(inpOp, kernel, biases, name=name) if act else \
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tf.matmul(inpOp, kernel) + biases
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return net
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def inference(seq):
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net = embedding('emb', seq, VOCAB_SIZE, FLAGS.emb_size)
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print "emb:", get_incoming_shape(net)
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net = lstm('lstm', net, FLAGS.hidden_size, num_layers=FLAGS.num_layers)
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print "lstm:", get_incoming_shape(net)
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net = fc('fc1', net, FLAGS.hidden_size, 2)
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return net
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def loss(logits, labels):
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# one label index for one sample
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#labels = tf.cast(labels, tf.int64)
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# cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
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# logits, labels, name='cross_entropy_per_example')
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labels = tf.cast(labels, tf.float32)
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cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
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logits, labels, name='cross_entropy_per_example')
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cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
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tf.add_to_collection('losses', cross_entropy_mean)
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return tf.add_n(tf.get_collection('losses'), name='total_loss')
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def tower_loss(scope):
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"""Calculate the total loss on a single tower running the model.
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Args:
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scope: unique prefix string identifying the tower, e.g. 'tower_0'
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Returns:
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Tensor of shape [] containing the total loss for a batch of data
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"""
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data, label = train_dataset.next_batch(FLAGS.batch_size)
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# Build a Graph that computes the logits predictions from the
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# inference model.
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last_layer = inference(data)
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# Build the portion of the Graph calculating the losses. Note that we will
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# assemble the total_loss using a custom function below.
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#_ = loss(last_layer, label)
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_ = loss(last_layer, label)
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# Assemble all of the losses for the current tower only.
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losses = tf.get_collection('losses', scope)
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# Calculate the total loss for the current tower.
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total_loss = tf.add_n(losses, name='total_loss')
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# Compute the moving average of all individual losses and the total loss.
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loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
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loss_averages_op = loss_averages.apply(losses + [total_loss])
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# Attach a scalar summary to all individual losses and the total loss; do the
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# same for the averaged version of the losses.
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for l in losses + [total_loss]:
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# Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
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# session. This helps the clarity of presentation on tensorboard.
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loss_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', l.op.name)
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# Name each loss as '(raw)' and name the moving average version of the loss
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# as the original loss name.
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tf.scalar_summary(loss_name + ' (raw)', l)
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#tf.scalar_summary(loss_name, loss_averages.average(l))
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with tf.control_dependencies([loss_averages_op]):
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total_loss = tf.identity(total_loss)
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return total_loss
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def average_gradients(tower_grads):
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"""Calculate the average gradient for each shared variable across all towers.
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Note that this function provides a synchronization point across all towers.
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Args:
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tower_grads: List of lists of (gradient, variable) tuples. The outer list
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is over individual gradients. The inner list is over the gradient
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calculation for each tower.
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Returns:
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List of pairs of (gradient, variable) where the gradient has been averaged
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across all towers.
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"""
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average_grads = []
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for grad_and_vars in zip(*tower_grads):
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# Note that each grad_and_vars looks like the following:
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# ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
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grads = []
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for g, _ in grad_and_vars:
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# Add 0 dimension to the gradients to represent the tower.
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expanded_g = tf.expand_dims(g, 0)
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# Append on a 'tower' dimension which we will average over below.
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grads.append(expanded_g)
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# Average over the 'tower' dimension.
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grad = tf.concat(0, grads)
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grad = tf.reduce_mean(grad, 0)
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# Keep in mind that the Variables are redundant because they are shared
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# across towers. So .. we will just return the first tower's pointer to
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# the Variable.
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v = grad_and_vars[0][1]
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grad_and_var = (grad, v)
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average_grads.append(grad_and_var)
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return average_grads
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def time_tensorflow_run(session, target):
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num_steps_burn_in = 80
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total_duration = 0.0
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total_duration_squared = 0.0
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for i in xrange(FLAGS.num_batches + num_steps_burn_in):
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start_time = time.time()
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_ = session.run(target, feed_dict={x_input: data, y_input: label})
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_, loss_value = session.run(target)
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duration = time.time() - start_time
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if i > num_steps_burn_in:
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if not i % 10:
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num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus
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examples_per_sec = num_examples_per_step / duration
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# sec_per_batch = duration / FLAGS.num_gpus
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sec_per_batch = duration
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format_str = (
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'%s: step %d, loss= %.2f (%.1f examples/sec; %.3f '
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'sec/batch batch_size= %d)')
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print(format_str %
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(datetime.now(), i - num_steps_burn_in, loss_value,
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duration, sec_per_batch, num_examples_per_step))
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total_duration += duration
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total_duration_squared += duration * duration
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mn = total_duration / FLAGS.num_batches
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vr = total_duration_squared / FLAGS.num_batches - mn * mn
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sd = math.sqrt(vr)
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print('%s: FwdBwd across %d steps, %.3f +/- %.3f sec / batch' %
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(datetime.now(), FLAGS.num_batches, mn, sd))
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def run_benchmark():
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with tf.Graph().as_default(), tf.device('/cpu:0'):
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# Create a variable to count the number of train() calls. This equals the
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# number of batches processed * FLAGS.num_gpus.
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global_step = tf.get_variable(
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'global_step', [],
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initializer=tf.constant_initializer(0),
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trainable=False)
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# Calculate the learning rate schedule.
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num_batches_per_epoch = (NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /
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FLAGS.batch_size)
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decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)
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# Create an optimizer that performs gradient descent.
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opt = tf.train.AdamOptimizer(0.001)
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#train_dataset = reader.create_datasets("imdb.pkl", VOCAB_SIZE)
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# Calculate the gradients for each model tower.
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tower_grads = []
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for i in xrange(FLAGS.num_gpus):
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with tf.device('/gpu:%d' % i):
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with tf.name_scope('%s_%d' % (TOWER_NAME, i)) as scope:
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# Calculate the loss for one tower of the model. This function
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# constructs the entire model but shares the variables across
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# all towers.
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loss = tower_loss(scope)
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# Reuse variables for the next tower.
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tf.get_variable_scope().reuse_variables()
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# Retain the summaries from the final tower.
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# summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)
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# Calculate the gradients for the batch of data on this tower.
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grads = opt.compute_gradients(loss)
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# Keep track of the gradients across all towers.
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tower_grads.append(grads)
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# We must calculate the mean of each gradient. Note that this is the
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# synchronization point across all towers.
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grads = average_gradients(tower_grads)
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# Apply the gradients to adjust the shared variables.
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apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
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# Group all updates to into a single train op.
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train_op = tf.group(apply_gradient_op)
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# Build an initialization operation.
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init = tf.initialize_all_variables()
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# Start running operations on the Graph. allow_soft_placement must be set to
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# True to build towers on GPU, as some of the ops do not have GPU
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# implementations.
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sess = tf.Session(config=tf.ConfigProto(
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allow_soft_placement=True,
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log_device_placement=FLAGS.log_device_placement))
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sess.run(init)
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time_tensorflow_run(sess, [train_op, loss])
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def main(_):
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run_benchmark()
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if __name__ == '__main__':
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tf.app.run()
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