TensorFlow-Models/namignizer/model.py

# 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_cell = tf.nn.rnn_cell.BasicLSTMCell(size, forget_bias=0.0)
        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)

        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(1, 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.nn.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.all_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