DeepIoT_rnnDrop.py

import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops


def GenRNNMask(keep_prob=1.0, is_training=False, batch_size=64, inter_dim=1, dtype=None, seed=None):
    is_training = ops.convert_to_tensor(is_training, name='is_training')
    noise_shape = (batch_size, inter_dim)
    random_tensor = keep_prob
    random_tensor += random_ops.random_uniform(noise_shape,
                                               seed=seed,
                                               dtype=tf.float32)
    binary_tensor = math_ops.floor(random_tensor)
    ret = tf.cond(is_training, lambda: tf.identity(binary_tensor),
                  lambda: tf.identity(keep_prob))
    return ret


class DropoutWrapper(tf.contrib.rnn.RNNCell):

    def __init__(self, cell, binary_tensor=None, dtype=None, seed=None):
        self._cell = cell
        self._seed = seed
        self._binary_tensor = binary_tensor

    @property
    def state_size(self):
        return self._cell.state_size

    @property
    def output_size(self):
        return self._cell.output_size

    def zero_state(self, batch_size, dtype):
        with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
            return self._cell.zero_state(batch_size, dtype)

    def __call__(self, inputs, state, scope=None):
        output, new_state = self._cell(inputs, state, scope)
        output = output * self._binary_tensor
        new_state = new_state * self._binary_tensor

        return output, new_state