"""The PTB model."""

def __init__(self, is_training, config):

self.batch_size = batch_size = config.batch_size

self.num_steps = num_steps = config.num_steps

size = config.hidden_size

vocab_size = config.vocab_size

self._input_data = tf.placeholder(tf.int32, [batch_size, num_steps])

self._targets = tf.placeholder(tf.int32, [batch_size, num_steps])

lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(size, forget_bias=1.0, state_is_tuple=True)

if is_training and config.keep_prob < 1:

lstm_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_cell, output_keep_prob=config.keep_prob)

cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * config.num_layers, state_is_tuple=True)

self._initial_state = cell.zero_state(batch_size, data_type())

with tf.device("/gpu:0"):

embedding = tf.get_variable( "embedding", [vocab_size, size], dtype=data_type())

inputs = tf.nn.embedding_lookup(embedding, self._input_data)

if is_training and config.keep_prob < 1:

inputs = tf.nn.dropout(inputs, config.keep_prob)

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(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

#logits = tf.nn.xw_plus_b(output, softmax_w, softmax_b)

# loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example(

# [logits],

# [tf.reshape(self._targets, [-1])],

# [tf.ones([batch_size * num_steps], dtype=data_type())])

logits_ = tf.reshape(logits, [self.batch_size, self.num_steps, vocab_size])

loss = tf.contrib.seq2seq.sequence_loss(

logits_,

self.targets,

tf.ones([self.batch_size, self.num_steps], dtype=data_type()),

average_across_timesteps=False,

average_across_batch=True)

self._cost = cost = tf.reduce_sum(loss)

self._final_state = state

# RANI

self.logits = logits_

if not is_training:

self._out_probs = tf.nn.softmax(logits_)

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))

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_data(self):

return self._input_data

@property

def targets(self):

return self._targets

@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 out_probs(self):

"""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 = 15

keep_prob = 1.0

lr_decay = 0.5

batch_size = 20

"""Medium config."""

init_scale = 0.05

learning_rate = 1.0

max_grad_norm = 5

num_layers = 2

num_steps = 30

hidden_size = 650

max_epoch = 6

max_max_epoch = 30

keep_prob = 1.0

lr_decay = 0.8

batch_size = 50

"""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 = 200

"""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

"""Runs the model on the given data."""

epoch_size = ((len(data) // model.batch_size) - 1) // model.num_steps

start_time = time.time()

costs = 0.0

iters = 0

state = session.run(model.initial_state)

for step, (x, y) in enumerate(Reader.ptb_iterator(data, model.batch_size, model.num_steps)):

fetches = [model.cost, model.final_state, model.logits, eval_op]

feed_dict = {}

feed_dict[model.input_data] = x

feed_dict[model.targets] = y

for i, (c, h) in enumerate(model.initial_state):

feed_dict[c] = state[i].c

feed_dict[h] = state[i].h

cost, state, logits, _ = session.run(fetches, feed_dict)

costs += cost

iters += model.num_steps

# Rani: show the actual prediction

# decodedWordId = int(np.argmax(logits))

# print(" ".join([inverseDictionary[int(x1)] for x1 in np.nditer(x)]) + \

# " got:" + inverseDictionary[decodedWordId] + " expected:" + inverseDictionary[int(y)])

#if verbose and step % (epoch_size // 10) == 10:

if step % 10 == 0:

print("epoch: %d\t%.3f perplexity: %.3f speed: %.0f wps" % (epoch_num, step * 1.0 / epoch_size, np.exp(costs / iters), iters * model.batch_size / (time.time() - start_time)))

print("costs is %d and iters is %d" % (costs,iters) )

if FLAGS.model == "small":

return SmallConfig()

elif FLAGS.model == "medium":

return MediumConfig()

elif FLAGS.model == "large":

return LargeConfig()

elif FLAGS.model == "test":

return TestConfig()

else:

if not FLAGS.data_path:

raise ValueError("Must set --data_path to PTB data directory")

config = get_config()

eval_config = get_config()

eval_config.batch_size = 1

eval_config.num_steps = 1

# raw_data = reader.ptb_raw_data(FLAGS.data_path)

# train_data, valid_data, test_data, vocabulary, word_to_id = raw_data

#Rani: added inverseDictionary

word_to_id = Reader._build_vocab(FLAGS.train_dataset, cut_vocab_size=config.vocab_size)

train_data = Reader._file_to_word_ids('../corpus/corpus_beibei_train.data',word_to_id)

valid_data = Reader._file_to_word_ids('../corpus/corpus_beibei_valid.data',word_to_id)

test_data = Reader._file_to_word_ids('../corpus/corpus_beibei_test.data', word_to_id)

# inverseDictionary = dict(zip(word_to_id.values(), word_to_id.keys()))

#config_gpu = tf.ConfigProto()

#config_gpu.gpu_options.allocator_type = 'BFC' #动态分配显存

with tf.Graph().as_default(), tf.Session(config = tf.ConfigProto(log_device_placement=True)) as session:

initializer = tf.random_uniform_initializer(-config.init_scale, config.init_scale)

with tf.variable_scope("model", reuse=None, initializer=initializer):

m = PTBModel(is_training=True, config=config)

with tf.variable_scope("model", reuse=True, initializer=initializer):

mvalid = PTBModel(is_training=False, config=config)

mtest = PTBModel(is_training=False, config=eval_config)

saver = tf.train.Saver()

session.run(tf.global_variables_initializer())

if FLAGS.train:

print('training')

for i in range(config.max_max_epoch):

lr_decay = config.lr_decay ** max(i - config.max_epoch, 0.0)

m.assign_lr(session, config.learning_rate * lr_decay)

print("Epoch: %f Learning rate: %.3f" % (i + 1, session.run(m.lr)))

train_perplexity = run_epoch(session, m, train_data, m.train_op,i + 1)

print("Epoch: %f Train Perplexity: %.3f" % (i + 1, train_perplexity))

valid_perplexity = run_epoch(session, mvalid, valid_data, tf.no_op(), i + 1)

print("Epoch: %f Valid Perplexity: %.3f" % (i + 1, valid_perplexity))

saver.save(session, FLAGS.checkpoint_dir + '/model.ckpt', global_step=i + 1)

else:

print('testing')

ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)

print(ckpt)

if ckpt and ckpt.model_checkpoint_path:

saver.restore(session, ckpt.model_checkpoint_path)

else:

print("No checkpoint file found")

test_perplexity = run_epoch(session, mtest, test_data, tf.no_op())