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# Copyright 2016 Google Inc. 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.
"""RNN model with embeddings"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class NamignizerModel(object):
"""The Namignizer model ~ strongly based on PTB"""
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
# will always be 27
vocab_size = config.vocab_size
# placeholders for inputs
self._input_data = tf.placeholder(tf.int32, [batch_size, num_steps])
self._targets = tf.placeholder(tf.int32, [batch_size, num_steps])
# weights for the loss function
self._weights = tf.placeholder(tf.float32, [batch_size * num_steps])
# lstm for our RNN cell (GRU supported too)
lstm_cells = []
for layer in range(config.num_layers):
lstm_cell = tf.contrib.rnn.BasicLSTMCell(size, forget_bias=0.0)
if is_training and config.keep_prob < 1:
lstm_cell = tf.contrib.rnn.DropoutWrapper(
lstm_cell, output_keep_prob=config.keep_prob)
lstm_cells.append(lstm_cell)
cell = tf.contrib.rnn.MultiRNNCell(lstm_cells)
self._initial_state = cell.zero_state(batch_size, tf.float32)
with tf.device("/cpu:0"):
embedding = tf.get_variable("embedding", [vocab_size, size])
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(axis=1, values=outputs), [-1, size])
softmax_w = tf.get_variable("softmax_w", [size, vocab_size])
softmax_b = tf.get_variable("softmax_b", [vocab_size])
logits = tf.matmul(output, softmax_w) + softmax_b
loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example(
[logits],
[tf.reshape(self._targets, [-1])],
[self._weights])
self._loss = loss
self._cost = cost = tf.reduce_sum(loss) / batch_size
self._final_state = state
# probabilities of each letter
self._activations = tf.nn.softmax(logits)
# ability to save the model
self.saver = tf.train.Saver(tf.global_variables())
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))
def assign_lr(self, session, lr_value):
session.run(tf.assign(self.lr, lr_value))
@property
def input_data(self):
return self._input_data
@property
def targets(self):
return self._targets
@property
def activations(self):
return self._activations
@property
def weights(self):
return self._weights
@property
def initial_state(self):
return self._initial_state
@property
def cost(self):
return self._cost
@property
def loss(self):
return self._loss
@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
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