在Win7 + python3 + tf 1.0.0 alpha下执行ptb_Word_lm.py运行时出现以下问题：
1、无法找到rnn_cell；
2、无法找到seq2seq；
3、其他。
修正方法为：
from tensorflow.contrib import rnn
rnn.BasicLSTMCell
rnn.DropoutWrapper
rnn.MultiRNNCell
tf.contrib.legacy_seq2seq.sequence_loss_by_example
另，reader代码中出现错误：
TypeError: a bytes-like object is required, not 'str'
修正方法：
line30 修改为f.read().decode("utf-8").replace("/n", "<eos>").split()
贴ptb_word_lm修正后源码如下：
# -*- 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 Regularizationhttp://arxiv.org/abs/1409.2329There 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 | 78.29The 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 sizeThe data required for this example is in the data/ dir of thePTB dataset from Tomas Mikolov's webpage:$ wget http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz$ tar xvf simple-examples.tgzTo run:$ python ptb_word_lm.py --data_path=simple-examples/data/"""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport timeimport numpy as npimport tensorflow as tffrom tensorflow.contrib import rnnimport readerflags = tf.flagslogging = tf.loggingflags.DEFINE_string("model", "small", "A type of model. Possible options are: small, medium, large.")flags.DEFINE_string("data_path", "YOUR PATH TO LSTM DATA DIR", "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 = flags.FLAGSdef data_type(): return tf.float16 if FLAGS.use_fp16 else tf.float32class 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._input = input_ batch_size = input_.batch_size num_steps = input_.num_steps size = config.hidden_size vocab_size = config.vocab_size # 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. lstm_cell = rnn.BasicLSTMCell(size, forget_bias=0.0, state_is_tuple=True) if is_training and config.keep_prob < 1: lstm_cell = rnn.DropoutWrapper(lstm_cell, output_keep_prob=config.keep_prob) cell = rnn.MultiRNNCell([lstm_cell] * config.num_layers, state_is_tuple=True) self._initial_state = cell.zero_state(batch_size, data_type()) 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) # Simplified version of models/tutorials/rnn/rnn.py's rnn(). # This builds an unrolled LSTM for tutorial purposes only. # In general, use the rnn() or state_saving_rnn() from rnn.py. # # The alternative version of the code below is: # # inputs = tf.unstack(inputs, num=num_steps, axis=1) # outputs, state = tf.nn.rnn(cell, inputs, # initial_state=self._initial_state) outputs = [] state = self._initial_state with tf.variable_scope("RNN"): for time_step in range(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_v2(outputs, 1), [-1, size]) 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.matmul(output, softmax_w) + softmax_b loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example( [logits], [tf.reshape(input_.targets, [-1])], [tf.ones([batch_size * num_steps], dtype=data_type())]) self._cost = cost = tf.reduce_sum(loss) / batch_size 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(cost, tvars), config.max_grad_norm) optimizer = tf.train.GradientDescentOptimizer(self._lr) self._train_op = optimizer.apply_gradients( zip(grads, tvars), global_step=tf.contrib.framework.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 assign_lr(self, session, lr_value): session.run(self._lr_update, feed_dict={self._new_lr: lr_value}) @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_opclass 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 = 10000class 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 = 10000class 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 = 10000class RTestConfig(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 = 10000def 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 / (time.time() - start_time))) return np.exp(costs / iters)def get_config(): if FLAGS.model == "small": return SmallConfig() elif FLAGS.model == "medium": return MediumConfig() elif FLAGS.model == "large": return LargeConfig() elif FLAGS.model == "test": return RTestConfig() else: raise ValueError("Invalid model: %s", FLAGS.model)def main(_): if not FLAGS.data_path: raise ValueError("Must set --data_path to PTB data directory") 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) sv = tf.train.Supervisor(logdir=FLAGS.save_path) with sv.managed_session() 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()
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