在Win7 + python3 + tf 1.0.0 alpha下執行ptb_Word_lm.py運行時出現以下問題：

1、無法找到rnn_cell；

2、無法找到seq2seq；

3、其他。

修正方法為：

from tensorflow.contrib import rnn

rnn.BasicLSTMCell

rnn.DropoutWrapper

rnn.MultiRNNCell

tf.contrib.legacy_seq2seq.sequence_loss_by_example

另，reader代碼中出現錯誤：

TypeError: a bytes-like object is required, not 'str'

修正方法：

line30 修改為f.read().decode("utf-8").replace("/n", "<eos>").split()

貼ptb_word_lm修正后源碼如下：

# -*- 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()