Tensorflow學習筆記（第四天）—遞迴神經網路
阿新 • • 發佈：2018-11-19
一、首先下載來源於 Tomas Mikolov 網站上的 PTB 資料集
"""Utilities for parsing PTB text files.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function
import collections import os import sys
import tensorflow as tf
Py3 = sys.version_info[0] == 3
def _read_words(filename): with tf.gfile.GFile(filename, "r") as f: if Py3: return f.read().replace("\n", "<eos>").split() else: return f.read().decode("utf-8").replace("\n", "<eos>").split()
def _build_vocab(filename): data = _read_words(filename)
counter = collections.Counter(data) count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))
words, _ = list(zip(*count_pairs)) word_to_id = dict(zip(words, range(len(words))))
return word_to_id
def _file_to_word_ids(filename, word_to_id): data = _read_words(filename) return [word_to_id[word] for word in data if word in word_to_id]
def ptb_raw_data(data_path=None): """Load PTB raw data from data directory "data_path".
Reads PTB text files, converts strings to integer ids, and performs mini-batching of the inputs.
The PTB dataset comes from Tomas Mikolov's webpage:
http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz
Args: data_path: string path to the directory where simple-examples.tgz has been extracted.
Returns: tuple (train_data, valid_data, test_data, vocabulary) where each of the data objects can be passed to PTBIterator. """
train_path = os.path.join(data_path, "ptb.train.txt") valid_path = os.path.join(data_path, "ptb.valid.txt") test_path = os.path.join(data_path, "ptb.test.txt")
word_to_id = _build_vocab(train_path) train_data = _file_to_word_ids(train_path, word_to_id) valid_data = _file_to_word_ids(valid_path, word_to_id) test_data = _file_to_word_ids(test_path, word_to_id) vocabulary = len(word_to_id) return train_data, valid_data, test_data, vocabulary
def ptb_producer(raw_data, batch_size, num_steps, name=None): """Iterate on the raw PTB data.
This chunks up raw_data into batches of examples and returns Tensors that are drawn from these batches.
Args: raw_data: one of the raw data outputs from ptb_raw_data. batch_size: int, the batch size. num_steps: int, the number of unrolls. name: the name of this operation (optional).
Returns: A pair of Tensors, each shaped [batch_size, num_steps]. The second element of the tuple is the same data time-shifted to the right by one.
Raises: tf.errors.InvalidArgumentError: if batch_size or num_steps are too high. """ with tf.name_scope(name, "PTBProducer", [raw_data, batch_size, num_steps]): raw_data = tf.convert_to_tensor(raw_data, name="raw_data", dtype=tf.int32)
data_len = tf.size(raw_data) batch_len = data_len // batch_size data = tf.reshape(raw_data[0 : batch_size * batch_len], [batch_size, batch_len])
epoch_size = (batch_len - 1) // num_steps assertion = tf.assert_positive( epoch_size, message="epoch_size == 0, decrease batch_size or num_steps") with tf.control_dependencies([assertion]): epoch_size = tf.identity(epoch_size, name="epoch_size")
i = tf.train.range_input_producer(epoch_size, shuffle=False).dequeue() x = tf.strided_slice(data, [0, i * num_steps], [batch_size, (i + 1) * num_steps]) x.set_shape([batch_size, num_steps]) y = tf.strided_slice(data, [0, i * num_steps + 1], [batch_size, (i + 1) * num_steps + 1]) y.set_shape([batch_size, num_steps]) return x, y
ptb_word_lm.py程式碼： # -*- coding: utf-8 -*-
# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ==============================================================================
"""Example / benchmark for building a PTB LSTM model.
Trains the model described in: (Zaremba, et. al.) Recurrent Neural Network Regularization http://arxiv.org/abs/1409.2329
There are 3 supported model configurations: =========================================== | config | epochs | train | valid | test =========================================== | small | 13 | 37.99 | 121.39 | 115.91 | medium | 39 | 48.45 | 86.16 | 82.07 | large | 55 | 37.87 | 82.62 | 78.29 The exact results may vary depending on the random initialization.
The hyperparameters used in the model: - init_scale - the initial scale of the weights - learning_rate - the initial value of the learning rate - max_grad_norm - the maximum permissible norm of the gradient - num_layers - the number of LSTM layers - num_steps - the number of unrolled steps of LSTM - hidden_size - the number of LSTM units - max_epoch - the number of epochs trained with the initial learning rate - max_max_epoch - the total number of epochs for training - keep_prob - the probability of keeping weights in the dropout layer - lr_decay - the decay of the learning rate for each epoch after "max_epoch" - batch_size - the batch size - rnn_mode - the low level implementation of lstm cell: one of CUDNN, BASIC, or BLOCK, representing cudnn_lstm, basic_lstm, and lstm_block_cell classes.
The data required for this example is in the data/ dir of the PTB dataset from Tomas Mikolov's webpage:
$ wget http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz $ tar xvf simple-examples.tgz
To run:
$ python ptb_word_lm.py --data_path=simple-examples/data/
""" from __future__ import absolute_import from __future__ import division from __future__ import print_function
import time
import numpy as np import tensorflow as tf
import reader import util
from tensorflow.python.client import device_lib
flags = tf.flags logging = tf.logging
flags.DEFINE_string( "model", "small", "A type of model. Possible options are: small, medium, large.") flags.DEFINE_string("data_path", None, "Where the training/test data is stored.") flags.DEFINE_string("save_path", None, "Model output directory.") flags.DEFINE_bool("use_fp16", False, "Train using 16-bit floats instead of 32bit floats") flags.DEFINE_integer("num_gpus", 1, "If larger than 1, Grappler AutoParallel optimizer " "will create multiple training replicas with each GPU " "running one replica.") flags.DEFINE_string("rnn_mode", None, "The low level implementation of lstm cell: one of CUDNN, " "BASIC, and BLOCK, representing cudnn_lstm, basic_lstm, " "and lstm_block_cell classes.") FLAGS = flags.FLAGS BASIC = "basic" CUDNN = "cudnn" BLOCK = "block"
def data_type(): return tf.float16 if FLAGS.use_fp16 else tf.float32
class PTBInput(object): """The input data."""
def __init__(self, config, data, name=None): self.batch_size = batch_size = config.batch_size self.num_steps = num_steps = config.num_steps self.epoch_size = ((len(data) // batch_size) - 1) // num_steps self.input_data, self.targets = reader.ptb_producer( data, batch_size, num_steps, name=name)
class PTBModel(object): """The PTB model."""
def __init__(self, is_training, config, input_): self._is_training = is_training self._input = input_ self._rnn_params = None self._cell = None self.batch_size = input_.batch_size self.num_steps = input_.num_steps size = config.hidden_size vocab_size = config.vocab_size
with tf.device("/cpu:0"): embedding = tf.get_variable( "embedding", [vocab_size, size], dtype=data_type()) inputs = tf.nn.embedding_lookup(embedding, input_.input_data)
if is_training and config.keep_prob < 1: inputs = tf.nn.dropout(inputs, config.keep_prob)
output, state = self._build_rnn_graph(inputs, config, is_training)
softmax_w = tf.get_variable( "softmax_w", [size, vocab_size], dtype=data_type()) softmax_b = tf.get_variable("softmax_b", [vocab_size], dtype=data_type()) logits = tf.nn.xw_plus_b(output, softmax_w, softmax_b) # Reshape logits to be a 3-D tensor for sequence loss logits = tf.reshape(logits, [self.batch_size, self.num_steps, vocab_size])
# Use the contrib sequence loss and average over the batches loss = tf.contrib.seq2seq.sequence_loss( logits, input_.targets, tf.ones([self.batch_size, self.num_steps], dtype=data_type()), average_across_timesteps=False, average_across_batch=True)
# Update the cost self._cost = tf.reduce_sum(loss) self._final_state = state
if not is_training: return
self._lr = tf.Variable(0.0, trainable=False) tvars = tf.trainable_variables() grads, _ = tf.clip_by_global_norm(tf.gradients(self._cost, tvars), config.max_grad_norm) optimizer = tf.train.GradientDescentOptimizer(self._lr) self._train_op = optimizer.apply_gradients( zip(grads, tvars), global_step=tf.train.get_or_create_global_step())
self._new_lr = tf.placeholder( tf.float32, shape=[], name="new_learning_rate") self._lr_update = tf.assign(self._lr, self._new_lr)
def _build_rnn_graph(self, inputs, config, is_training): if config.rnn_mode == CUDNN: return self._build_rnn_graph_cudnn(inputs, config, is_training) else: return self._build_rnn_graph_lstm(inputs, config, is_training)
def _build_rnn_graph_cudnn(self, inputs, config, is_training): """Build the inference graph using CUDNN cell.""" inputs = tf.transpose(inputs, [1, 0, 2]) self._cell = tf.contrib.cudnn_rnn.CudnnLSTM( num_layers=config.num_layers, num_units=config.hidden_size, input_size=config.hidden_size, dropout=1 - config.keep_prob if is_training else 0) params_size_t = self._cell.params_size() self._rnn_params = tf.get_variable( "lstm_params", initializer=tf.random_uniform( [params_size_t], -config.init_scale, config.init_scale), validate_shape=False) c = tf.zeros([config.num_layers, self.batch_size, config.hidden_size], tf.float32) h = tf.zeros([config.num_layers, self.batch_size, config.hidden_size], tf.float32) self._initial_state = (tf.contrib.rnn.LSTMStateTuple(h=h, c=c),) outputs, h, c = self._cell(inputs, h, c, self._rnn_params, is_training) outputs = tf.transpose(outputs, [1, 0, 2]) outputs = tf.reshape(outputs, [-1, config.hidden_size]) return outputs, (tf.contrib.rnn.LSTMStateTuple(h=h, c=c),)
def _get_lstm_cell(self, config, is_training): if config.rnn_mode == BASIC: return tf.contrib.rnn.BasicLSTMCell( config.hidden_size, forget_bias=0.0, state_is_tuple=True, reuse=not is_training) if config.rnn_mode == BLOCK: return tf.contrib.rnn.LSTMBlockCell( config.hidden_size, forget_bias=0.0) raise ValueError("rnn_mode %s not supported" % config.rnn_mode)
def _build_rnn_graph_lstm(self, inputs, config, is_training): """Build the inference graph using canonical LSTM cells.""" # Slightly better results can be obtained with forget gate biases # initialized to 1 but the hyperparameters of the model would need to be # different than reported in the paper. def make_cell(): cell = self._get_lstm_cell(config, is_training) if is_training and config.keep_prob < 1: cell = tf.contrib.rnn.DropoutWrapper( cell, output_keep_prob=config.keep_prob) return cell
cell = tf.contrib.rnn.MultiRNNCell( [make_cell() for _ in range(config.num_layers)], state_is_tuple=True)
self._initial_state = cell.zero_state(config.batch_size, data_type()) state = self._initial_state # Simplified version of tf.nn.static_rnn(). # This builds an unrolled LSTM for tutorial purposes only. # In general, use tf.nn.static_rnn() or tf.nn.static_state_saving_rnn(). # # The alternative version of the code below is: # # inputs = tf.unstack(inputs, num=self.num_steps, axis=1) # outputs, state = tf.nn.static_rnn(cell, inputs, # initial_state=self._initial_state) outputs = [] with tf.variable_scope("RNN"): for time_step in range(self.num_steps): if time_step > 0: tf.get_variable_scope().reuse_variables() (cell_output, state) = cell(inputs[:, time_step, :], state) outputs.append(cell_output) output = tf.reshape(tf.concat(outputs, 1), [-1, config.hidden_size]) return output, state
def assign_lr(self, session, lr_value): session.run(self._lr_update, feed_dict={self._new_lr: lr_value})
def export_ops(self, name): """Exports ops to collections.""" self._name = name ops = {util.with_prefix(self._name, "cost"): self._cost} if self._is_training: ops.update(lr=self._lr, new_lr=self._new_lr, lr_update=self._lr_update) if self._rnn_params: ops.update(rnn_params=self._rnn_params) for name, op in ops.items(): tf.add_to_collection(name, op) self._initial_state_name = util.with_prefix(self._name, "initial") self._final_state_name = util.with_prefix(self._name, "final") util.export_state_tuples(self._initial_state, self._initial_state_name) util.export_state_tuples(self._final_state, self._final_state_name)
def import_ops(self): """Imports ops from collections.""" if self._is_training: self._train_op = tf.get_collection_ref("train_op")[0] self._lr = tf.get_collection_ref("lr")[0] self._new_lr = tf.get_collection_ref("new_lr")[0] self._lr_update = tf.get_collection_ref("lr_update")[0] rnn_params = tf.get_collection_ref("rnn_params") if self._cell and rnn_params: params_saveable = tf.contrib.cudnn_rnn.RNNParamsSaveable( self._cell, self._cell.params_to_canonical, self._cell.canonical_to_params, rnn_params, base_variable_scope="Model/RNN") tf.add_to_collection(tf.GraphKeys.SAVEABLE_OBJECTS, params_saveable) self._cost = tf.get_collection_ref(util.with_prefix(self._name, "cost"))[0] num_replicas = FLAGS.num_gpus if self._name == "Train" else 1 self._initial_state = util.import_state_tuples( self._initial_state, self._initial_state_name, num_replicas) self._final_state = util.import_state_tuples( self._final_state, self._final_state_name, num_replicas)
@property def input(self): return self._input
@property def initial_state(self): return self._initial_state
@property def cost(self): return self._cost
@property def final_state(self): return self._final_state
@property def lr(self): return self._lr
@property def train_op(self): return self._train_op
@property def initial_state_name(self): return self._initial_state_name
@property def final_state_name(self): return self._final_state_name
class SmallConfig(object): """Small config.""" init_scale = 0.1 learning_rate = 1.0 max_grad_norm = 5 num_layers = 2 num_steps = 20 hidden_size = 200 max_epoch = 4 max_max_epoch = 13 keep_prob = 1.0 lr_decay = 0.5 batch_size = 20 vocab_size = 10000 rnn_mode = BLOCK
class MediumConfig(object): """Medium config.""" init_scale = 0.05 learning_rate = 1.0 max_grad_norm = 5 num_layers = 2 num_steps = 35 hidden_size = 650 max_epoch = 6 max_max_epoch = 39 keep_prob = 0.5 lr_decay = 0.8 batch_size = 20 vocab_size = 10000 rnn_mode = BLOCK
class LargeConfig(object): """Large config.""" init_scale = 0.04 learning_rate = 1.0 max_grad_norm = 10 num_layers = 2 num_steps = 35 hidden_size = 1500 max_epoch = 14 max_max_epoch = 55 keep_prob = 0.35 lr_decay = 1 / 1.15 batch_size = 20 vocab_size = 10000 rnn_mode = BLOCK
class TestConfig(object): """Tiny config, for testing.""" init_scale = 0.1 learning_rate = 1.0 max_grad_norm = 1 num_layers = 1 num_steps = 2 hidden_size = 2 max_epoch = 1 max_max_epoch = 1 keep_prob = 1.0 lr_decay = 0.5 batch_size = 20 vocab_size = 10000 rnn_mode = BLOCK
def run_epoch(session, model, eval_op=None, verbose=False): """Runs the model on the given data.""" start_time = time.time() costs = 0.0 iters = 0 state = session.run(model.initial_state)
fetches = { "cost": model.cost, "final_state": model.final_state, } if eval_op is not None: fetches["eval_op"] = eval_op
for step in range(model.input.epoch_size): feed_dict = {} for i, (c, h) in enumerate(model.initial_state): feed_dict[c] = state[i].c feed_dict[h] = state[i].h
vals = session.run(fetches, feed_dict) cost = vals["cost"] state = vals["final_state"]
costs += cost iters += model.input.num_steps
if verbose and step % (model.input.epoch_size // 10) == 10: print("%.3f perplexity: %.3f speed: %.0f wps" % (step * 1.0 / model.input.epoch_size, np.exp(costs / iters), iters * model.input.batch_size * max(1, FLAGS.num_gpus) / (time.time() - start_time)))
return np.exp(costs / iters)
def get_config(): """Get model config.""" config = None if FLAGS.model == "small": config = SmallConfig() elif FLAGS.model == "medium": config = MediumConfig() elif FLAGS.model == "large": config = LargeConfig() elif FLAGS.model == "test": config = TestConfig() else: raise ValueError("Invalid model: %s", FLAGS.model) if FLAGS.rnn_mode: config.rnn_mode = FLAGS.rnn_mode if FLAGS.num_gpus != 1 or tf.__version__ < "1.3.0" : config.rnn_mode = BASIC return config
def main(_): if not FLAGS.data_path: raise ValueError("Must set --data_path to PTB data directory") gpus = [ x.name for x in device_lib.list_local_devices() if x.device_type == "GPU" ] if FLAGS.num_gpus > len(gpus): raise ValueError( "Your machine has only %d gpus " "which is less than the requested --num_gpus=%d." % (len(gpus), FLAGS.num_gpus))
raw_data = reader.ptb_raw_data(FLAGS.data_path) train_data, valid_data, test_data, _ = raw_data
config = get_config() eval_config = get_config() eval_config.batch_size = 1 eval_config.num_steps = 1
with tf.Graph().as_default(): initializer = tf.random_uniform_initializer(-config.init_scale, config.init_scale)
with tf.name_scope("Train"): train_input = PTBInput(config=config, data=train_data, name="TrainInput") with tf.variable_scope("Model", reuse=None, initializer=initializer): m = PTBModel(is_training=True, config=config, input_=train_input) tf.summary.scalar("Training Loss", m.cost) tf.summary.scalar("Learning Rate", m.lr)
with tf.name_scope("Valid"): valid_input = PTBInput(config=config, data=valid_data, name="ValidInput") with tf.variable_scope("Model", reuse=True, initializer=initializer): mvalid = PTBModel(is_training=False, config=config, input_=valid_input) tf.summary.scalar("Validation Loss", mvalid.cost)
with tf.name_scope("Test"): test_input = PTBInput( config=eval_config, data=test_data, name="TestInput") with tf.variable_scope("Model", reuse=True, initializer=initializer): mtest = PTBModel(is_training=False, config=eval_config, input_=test_input)
models = {"Train": m, "Valid": mvalid, "Test": mtest} for name, model in models.items(): model.export_ops(name) metagraph = tf.train.export_meta_graph() if tf.__version__ < "1.1.0" and FLAGS.num_gpus > 1: raise ValueError("num_gpus > 1 is not supported for TensorFlow versions " "below 1.1.0") soft_placement = False if FLAGS.num_gpus > 1: soft_placement = True util.auto_parallel(metagraph, m)
with tf.Graph().as_default(): tf.train.import_meta_graph(metagraph) for model in models.values(): model.import_ops() sv = tf.train.Supervisor(logdir=FLAGS.save_path) config_proto = tf.ConfigProto(allow_soft_placement=soft_placement) with sv.managed_session(config=config_proto) as session: for i in range(config.max_max_epoch): lr_decay = config.lr_decay ** max(i + 1 - config.max_epoch, 0.0) m.assign_lr(session, config.learning_rate * lr_decay)
print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr))) train_perplexity = run_epoch(session, m, eval_op=m.train_op, verbose=True) print("Epoch: %d Train Perplexity: %.3f" % (i + 1, train_perplexity)) valid_perplexity = run_epoch(session, mvalid) print("Epoch: %d Valid Perplexity: %.3f" % (i + 1, valid_perplexity))
test_perplexity = run_epoch(session, mtest) print("Test Perplexity: %.3f" % test_perplexity)
if FLAGS.save_path: print("Saving model to %s." % FLAGS.save_path) sv.saver.save(session, FLAGS.save_path, global_step=sv.global_step)
if __name__ == "__main__": tf.app.run() 三、執行 python E:\tensorflow_data\ptb_word_lm\ptb_word_lm.py --data_path=E:\tensorflow_data\ptb_word_lm\simple-examples\simple-examples\data --model small 這裡只運行了一個小模型 執行結果：
在13個epoch時，我們的測試perplexity為113.984
http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz
reader.py程式碼：
# -*- coding: utf-8 -*-
"""Utilities for parsing PTB text files.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function
import collections import os import sys
import tensorflow as tf
Py3 = sys.version_info[0] == 3
def _read_words(filename): with tf.gfile.GFile(filename, "r") as f: if Py3: return f.read().replace("\n", "<eos>").split() else: return f.read().decode("utf-8").replace("\n", "<eos>").split()
def _build_vocab(filename): data = _read_words(filename)
counter = collections.Counter(data) count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))
words, _ = list(zip(*count_pairs)) word_to_id = dict(zip(words, range(len(words))))
return word_to_id
def _file_to_word_ids(filename, word_to_id): data = _read_words(filename) return [word_to_id[word] for word in data if word in word_to_id]
def ptb_raw_data(data_path=None): """Load PTB raw data from data directory "data_path".
Reads PTB text files, converts strings to integer ids, and performs mini-batching of the inputs.
The PTB dataset comes from Tomas Mikolov's webpage:
http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz
Args: data_path: string path to the directory where simple-examples.tgz has been extracted.
Returns: tuple (train_data, valid_data, test_data, vocabulary) where each of the data objects can be passed to PTBIterator. """
train_path = os.path.join(data_path, "ptb.train.txt") valid_path = os.path.join(data_path, "ptb.valid.txt") test_path = os.path.join(data_path, "ptb.test.txt")
word_to_id = _build_vocab(train_path) train_data = _file_to_word_ids(train_path, word_to_id) valid_data = _file_to_word_ids(valid_path, word_to_id) test_data = _file_to_word_ids(test_path, word_to_id) vocabulary = len(word_to_id) return train_data, valid_data, test_data, vocabulary
def ptb_producer(raw_data, batch_size, num_steps, name=None): """Iterate on the raw PTB data.
This chunks up raw_data into batches of examples and returns Tensors that are drawn from these batches.
Args: raw_data: one of the raw data outputs from ptb_raw_data. batch_size: int, the batch size. num_steps: int, the number of unrolls. name: the name of this operation (optional).
Returns: A pair of Tensors, each shaped [batch_size, num_steps]. The second element of the tuple is the same data time-shifted to the right by one.
Raises: tf.errors.InvalidArgumentError: if batch_size or num_steps are too high. """ with tf.name_scope(name, "PTBProducer", [raw_data, batch_size, num_steps]): raw_data = tf.convert_to_tensor(raw_data, name="raw_data", dtype=tf.int32)
data_len = tf.size(raw_data) batch_len = data_len // batch_size data = tf.reshape(raw_data[0 : batch_size * batch_len], [batch_size, batch_len])
epoch_size = (batch_len - 1) // num_steps assertion = tf.assert_positive( epoch_size, message="epoch_size == 0, decrease batch_size or num_steps") with tf.control_dependencies([assertion]): epoch_size = tf.identity(epoch_size, name="epoch_size")
i = tf.train.range_input_producer(epoch_size, shuffle=False).dequeue() x = tf.strided_slice(data, [0, i * num_steps], [batch_size, (i + 1) * num_steps]) x.set_shape([batch_size, num_steps]) y = tf.strided_slice(data, [0, i * num_steps + 1], [batch_size, (i + 1) * num_steps + 1]) y.set_shape([batch_size, num_steps]) return x, y
ptb_word_lm.py程式碼： # -*- coding: utf-8 -*-
# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ==============================================================================
"""Example / benchmark for building a PTB LSTM model.
Trains the model described in: (Zaremba, et. al.) Recurrent Neural Network Regularization http://arxiv.org/abs/1409.2329
There are 3 supported model configurations: =========================================== | config | epochs | train | valid | test =========================================== | small | 13 | 37.99 | 121.39 | 115.91 | medium | 39 | 48.45 | 86.16 | 82.07 | large | 55 | 37.87 | 82.62 | 78.29 The exact results may vary depending on the random initialization.
The hyperparameters used in the model: - init_scale - the initial scale of the weights - learning_rate - the initial value of the learning rate - max_grad_norm - the maximum permissible norm of the gradient - num_layers - the number of LSTM layers - num_steps - the number of unrolled steps of LSTM - hidden_size - the number of LSTM units - max_epoch - the number of epochs trained with the initial learning rate - max_max_epoch - the total number of epochs for training - keep_prob - the probability of keeping weights in the dropout layer - lr_decay - the decay of the learning rate for each epoch after "max_epoch" - batch_size - the batch size - rnn_mode - the low level implementation of lstm cell: one of CUDNN, BASIC, or BLOCK, representing cudnn_lstm, basic_lstm, and lstm_block_cell classes.
The data required for this example is in the data/ dir of the PTB dataset from Tomas Mikolov's webpage:
$ wget http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz $ tar xvf simple-examples.tgz
To run:
$ python ptb_word_lm.py --data_path=simple-examples/data/
""" from __future__ import absolute_import from __future__ import division from __future__ import print_function
import time
import numpy as np import tensorflow as tf
import reader import util
from tensorflow.python.client import device_lib
flags = tf.flags logging = tf.logging
flags.DEFINE_string( "model", "small", "A type of model. Possible options are: small, medium, large.") flags.DEFINE_string("data_path", None, "Where the training/test data is stored.") flags.DEFINE_string("save_path", None, "Model output directory.") flags.DEFINE_bool("use_fp16", False, "Train using 16-bit floats instead of 32bit floats") flags.DEFINE_integer("num_gpus", 1, "If larger than 1, Grappler AutoParallel optimizer " "will create multiple training replicas with each GPU " "running one replica.") flags.DEFINE_string("rnn_mode", None, "The low level implementation of lstm cell: one of CUDNN, " "BASIC, and BLOCK, representing cudnn_lstm, basic_lstm, " "and lstm_block_cell classes.") FLAGS = flags.FLAGS BASIC = "basic" CUDNN = "cudnn" BLOCK = "block"
def data_type(): return tf.float16 if FLAGS.use_fp16 else tf.float32
class PTBInput(object): """The input data."""
def __init__(self, config, data, name=None): self.batch_size = batch_size = config.batch_size self.num_steps = num_steps = config.num_steps self.epoch_size = ((len(data) // batch_size) - 1) // num_steps self.input_data, self.targets = reader.ptb_producer( data, batch_size, num_steps, name=name)
class PTBModel(object): """The PTB model."""
def __init__(self, is_training, config, input_): self._is_training = is_training self._input = input_ self._rnn_params = None self._cell = None self.batch_size = input_.batch_size self.num_steps = input_.num_steps size = config.hidden_size vocab_size = config.vocab_size
with tf.device("/cpu:0"): embedding = tf.get_variable( "embedding", [vocab_size, size], dtype=data_type()) inputs = tf.nn.embedding_lookup(embedding, input_.input_data)
if is_training and config.keep_prob < 1: inputs = tf.nn.dropout(inputs, config.keep_prob)
output, state = self._build_rnn_graph(inputs, config, is_training)
softmax_w = tf.get_variable( "softmax_w", [size, vocab_size], dtype=data_type()) softmax_b = tf.get_variable("softmax_b", [vocab_size], dtype=data_type()) logits = tf.nn.xw_plus_b(output, softmax_w, softmax_b) # Reshape logits to be a 3-D tensor for sequence loss logits = tf.reshape(logits, [self.batch_size, self.num_steps, vocab_size])
# Use the contrib sequence loss and average over the batches loss = tf.contrib.seq2seq.sequence_loss( logits, input_.targets, tf.ones([self.batch_size, self.num_steps], dtype=data_type()), average_across_timesteps=False, average_across_batch=True)
# Update the cost self._cost = tf.reduce_sum(loss) self._final_state = state
if not is_training: return
self._lr = tf.Variable(0.0, trainable=False) tvars = tf.trainable_variables() grads, _ = tf.clip_by_global_norm(tf.gradients(self._cost, tvars), config.max_grad_norm) optimizer = tf.train.GradientDescentOptimizer(self._lr) self._train_op = optimizer.apply_gradients( zip(grads, tvars), global_step=tf.train.get_or_create_global_step())
self._new_lr = tf.placeholder( tf.float32, shape=[], name="new_learning_rate") self._lr_update = tf.assign(self._lr, self._new_lr)
def _build_rnn_graph(self, inputs, config, is_training): if config.rnn_mode == CUDNN: return self._build_rnn_graph_cudnn(inputs, config, is_training) else: return self._build_rnn_graph_lstm(inputs, config, is_training)
def _build_rnn_graph_cudnn(self, inputs, config, is_training): """Build the inference graph using CUDNN cell.""" inputs = tf.transpose(inputs, [1, 0, 2]) self._cell = tf.contrib.cudnn_rnn.CudnnLSTM( num_layers=config.num_layers, num_units=config.hidden_size, input_size=config.hidden_size, dropout=1 - config.keep_prob if is_training else 0) params_size_t = self._cell.params_size() self._rnn_params = tf.get_variable( "lstm_params", initializer=tf.random_uniform( [params_size_t], -config.init_scale, config.init_scale), validate_shape=False) c = tf.zeros([config.num_layers, self.batch_size, config.hidden_size], tf.float32) h = tf.zeros([config.num_layers, self.batch_size, config.hidden_size], tf.float32) self._initial_state = (tf.contrib.rnn.LSTMStateTuple(h=h, c=c),) outputs, h, c = self._cell(inputs, h, c, self._rnn_params, is_training) outputs = tf.transpose(outputs, [1, 0, 2]) outputs = tf.reshape(outputs, [-1, config.hidden_size]) return outputs, (tf.contrib.rnn.LSTMStateTuple(h=h, c=c),)
def _get_lstm_cell(self, config, is_training): if config.rnn_mode == BASIC: return tf.contrib.rnn.BasicLSTMCell( config.hidden_size, forget_bias=0.0, state_is_tuple=True, reuse=not is_training) if config.rnn_mode == BLOCK: return tf.contrib.rnn.LSTMBlockCell( config.hidden_size, forget_bias=0.0) raise ValueError("rnn_mode %s not supported" % config.rnn_mode)
def _build_rnn_graph_lstm(self, inputs, config, is_training): """Build the inference graph using canonical LSTM cells.""" # Slightly better results can be obtained with forget gate biases # initialized to 1 but the hyperparameters of the model would need to be # different than reported in the paper. def make_cell(): cell = self._get_lstm_cell(config, is_training) if is_training and config.keep_prob < 1: cell = tf.contrib.rnn.DropoutWrapper( cell, output_keep_prob=config.keep_prob) return cell
cell = tf.contrib.rnn.MultiRNNCell( [make_cell() for _ in range(config.num_layers)], state_is_tuple=True)
self._initial_state = cell.zero_state(config.batch_size, data_type()) state = self._initial_state # Simplified version of tf.nn.static_rnn(). # This builds an unrolled LSTM for tutorial purposes only. # In general, use tf.nn.static_rnn() or tf.nn.static_state_saving_rnn(). # # The alternative version of the code below is: # # inputs = tf.unstack(inputs, num=self.num_steps, axis=1) # outputs, state = tf.nn.static_rnn(cell, inputs, # initial_state=self._initial_state) outputs = [] with tf.variable_scope("RNN"): for time_step in range(self.num_steps): if time_step > 0: tf.get_variable_scope().reuse_variables() (cell_output, state) = cell(inputs[:, time_step, :], state) outputs.append(cell_output) output = tf.reshape(tf.concat(outputs, 1), [-1, config.hidden_size]) return output, state
def assign_lr(self, session, lr_value): session.run(self._lr_update, feed_dict={self._new_lr: lr_value})
def export_ops(self, name): """Exports ops to collections.""" self._name = name ops = {util.with_prefix(self._name, "cost"): self._cost} if self._is_training: ops.update(lr=self._lr, new_lr=self._new_lr, lr_update=self._lr_update) if self._rnn_params: ops.update(rnn_params=self._rnn_params) for name, op in ops.items(): tf.add_to_collection(name, op) self._initial_state_name = util.with_prefix(self._name, "initial") self._final_state_name = util.with_prefix(self._name, "final") util.export_state_tuples(self._initial_state, self._initial_state_name) util.export_state_tuples(self._final_state, self._final_state_name)
def import_ops(self): """Imports ops from collections.""" if self._is_training: self._train_op = tf.get_collection_ref("train_op")[0] self._lr = tf.get_collection_ref("lr")[0] self._new_lr = tf.get_collection_ref("new_lr")[0] self._lr_update = tf.get_collection_ref("lr_update")[0] rnn_params = tf.get_collection_ref("rnn_params") if self._cell and rnn_params: params_saveable = tf.contrib.cudnn_rnn.RNNParamsSaveable( self._cell, self._cell.params_to_canonical, self._cell.canonical_to_params, rnn_params, base_variable_scope="Model/RNN") tf.add_to_collection(tf.GraphKeys.SAVEABLE_OBJECTS, params_saveable) self._cost = tf.get_collection_ref(util.with_prefix(self._name, "cost"))[0] num_replicas = FLAGS.num_gpus if self._name == "Train" else 1 self._initial_state = util.import_state_tuples( self._initial_state, self._initial_state_name, num_replicas) self._final_state = util.import_state_tuples( self._final_state, self._final_state_name, num_replicas)
@property def input(self): return self._input
@property def initial_state(self): return self._initial_state
@property def cost(self): return self._cost
@property def final_state(self): return self._final_state
@property def lr(self): return self._lr
@property def train_op(self): return self._train_op
@property def initial_state_name(self): return self._initial_state_name
@property def final_state_name(self): return self._final_state_name
class SmallConfig(object): """Small config.""" init_scale = 0.1 learning_rate = 1.0 max_grad_norm = 5 num_layers = 2 num_steps = 20 hidden_size = 200 max_epoch = 4 max_max_epoch = 13 keep_prob = 1.0 lr_decay = 0.5 batch_size = 20 vocab_size = 10000 rnn_mode = BLOCK
class MediumConfig(object): """Medium config.""" init_scale = 0.05 learning_rate = 1.0 max_grad_norm = 5 num_layers = 2 num_steps = 35 hidden_size = 650 max_epoch = 6 max_max_epoch = 39 keep_prob = 0.5 lr_decay = 0.8 batch_size = 20 vocab_size = 10000 rnn_mode = BLOCK
class LargeConfig(object): """Large config.""" init_scale = 0.04 learning_rate = 1.0 max_grad_norm = 10 num_layers = 2 num_steps = 35 hidden_size = 1500 max_epoch = 14 max_max_epoch = 55 keep_prob = 0.35 lr_decay = 1 / 1.15 batch_size = 20 vocab_size = 10000 rnn_mode = BLOCK
class TestConfig(object): """Tiny config, for testing.""" init_scale = 0.1 learning_rate = 1.0 max_grad_norm = 1 num_layers = 1 num_steps = 2 hidden_size = 2 max_epoch = 1 max_max_epoch = 1 keep_prob = 1.0 lr_decay = 0.5 batch_size = 20 vocab_size = 10000 rnn_mode = BLOCK
def run_epoch(session, model, eval_op=None, verbose=False): """Runs the model on the given data.""" start_time = time.time() costs = 0.0 iters = 0 state = session.run(model.initial_state)
fetches = { "cost": model.cost, "final_state": model.final_state, } if eval_op is not None: fetches["eval_op"] = eval_op
for step in range(model.input.epoch_size): feed_dict = {} for i, (c, h) in enumerate(model.initial_state): feed_dict[c] = state[i].c feed_dict[h] = state[i].h
vals = session.run(fetches, feed_dict) cost = vals["cost"] state = vals["final_state"]
costs += cost iters += model.input.num_steps
if verbose and step % (model.input.epoch_size // 10) == 10: print("%.3f perplexity: %.3f speed: %.0f wps" % (step * 1.0 / model.input.epoch_size, np.exp(costs / iters), iters * model.input.batch_size * max(1, FLAGS.num_gpus) / (time.time() - start_time)))
return np.exp(costs / iters)
def get_config(): """Get model config.""" config = None if FLAGS.model == "small": config = SmallConfig() elif FLAGS.model == "medium": config = MediumConfig() elif FLAGS.model == "large": config = LargeConfig() elif FLAGS.model == "test": config = TestConfig() else: raise ValueError("Invalid model: %s", FLAGS.model) if FLAGS.rnn_mode: config.rnn_mode = FLAGS.rnn_mode if FLAGS.num_gpus != 1 or tf.__version__ < "1.3.0" : config.rnn_mode = BASIC return config
def main(_): if not FLAGS.data_path: raise ValueError("Must set --data_path to PTB data directory") gpus = [ x.name for x in device_lib.list_local_devices() if x.device_type == "GPU" ] if FLAGS.num_gpus > len(gpus): raise ValueError( "Your machine has only %d gpus " "which is less than the requested --num_gpus=%d." % (len(gpus), FLAGS.num_gpus))
raw_data = reader.ptb_raw_data(FLAGS.data_path) train_data, valid_data, test_data, _ = raw_data
config = get_config() eval_config = get_config() eval_config.batch_size = 1 eval_config.num_steps = 1
with tf.Graph().as_default(): initializer = tf.random_uniform_initializer(-config.init_scale, config.init_scale)
with tf.name_scope("Train"): train_input = PTBInput(config=config, data=train_data, name="TrainInput") with tf.variable_scope("Model", reuse=None, initializer=initializer): m = PTBModel(is_training=True, config=config, input_=train_input) tf.summary.scalar("Training Loss", m.cost) tf.summary.scalar("Learning Rate", m.lr)
with tf.name_scope("Valid"): valid_input = PTBInput(config=config, data=valid_data, name="ValidInput") with tf.variable_scope("Model", reuse=True, initializer=initializer): mvalid = PTBModel(is_training=False, config=config, input_=valid_input) tf.summary.scalar("Validation Loss", mvalid.cost)
with tf.name_scope("Test"): test_input = PTBInput( config=eval_config, data=test_data, name="TestInput") with tf.variable_scope("Model", reuse=True, initializer=initializer): mtest = PTBModel(is_training=False, config=eval_config, input_=test_input)
models = {"Train": m, "Valid": mvalid, "Test": mtest} for name, model in models.items(): model.export_ops(name) metagraph = tf.train.export_meta_graph() if tf.__version__ < "1.1.0" and FLAGS.num_gpus > 1: raise ValueError("num_gpus > 1 is not supported for TensorFlow versions " "below 1.1.0") soft_placement = False if FLAGS.num_gpus > 1: soft_placement = True util.auto_parallel(metagraph, m)
with tf.Graph().as_default(): tf.train.import_meta_graph(metagraph) for model in models.values(): model.import_ops() sv = tf.train.Supervisor(logdir=FLAGS.save_path) config_proto = tf.ConfigProto(allow_soft_placement=soft_placement) with sv.managed_session(config=config_proto) as session: for i in range(config.max_max_epoch): lr_decay = config.lr_decay ** max(i + 1 - config.max_epoch, 0.0) m.assign_lr(session, config.learning_rate * lr_decay)
print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr))) train_perplexity = run_epoch(session, m, eval_op=m.train_op, verbose=True) print("Epoch: %d Train Perplexity: %.3f" % (i + 1, train_perplexity)) valid_perplexity = run_epoch(session, mvalid) print("Epoch: %d Valid Perplexity: %.3f" % (i + 1, valid_perplexity))
test_perplexity = run_epoch(session, mtest) print("Test Perplexity: %.3f" % test_perplexity)
if FLAGS.save_path: print("Saving model to %s." % FLAGS.save_path) sv.saver.save(session, FLAGS.save_path, global_step=sv.global_step)
if __name__ == "__main__": tf.app.run() 三、執行 python E:\tensorflow_data\ptb_word_lm\ptb_word_lm.py --data_path=E:\tensorflow_data\ptb_word_lm\simple-examples\simple-examples\data --model small 這裡只運行了一個小模型 執行結果：
在13個epoch時，我們的測試perplexity為113.984