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import tensorflow as tf
import numpy as np
class MiLSTMCell(tf.nn.rnn_cell.RNNCell):
def __init__(self, num_units, forget_bias = 1.0, input_size = None,
state_is_tuple = True, activation = tf.tanh, reuse = None):
self.numUnits = num_units
self.forgetBias = forget_bias
self.activation = activation
self.reuse = reuse
@property
def state_size(self):
return tf.nn.rnn_cell.LSTMStateTuple(self.numUnits, self.numUnits)
@property
def output_size(self):
return self.numUnits
def mulWeights(self, inp, inDim, outDim, name = ""):
with tf.variable_scope("weights" + name):
W = tf.get_variable("weights", shape = (inDim, outDim),
initializer = tf.contrib.layers.xavier_initializer())
output = tf.matmul(inp, W)
return output
def addBiases(self, inp1, inp2, dim, name = ""):
with tf.variable_scope("additiveBiases" + name):
b = tf.get_variable("biases", shape = (dim,),
initializer = tf.zeros_initializer())
with tf.variable_scope("multiplicativeBias" + name):
beta = tf.get_variable("biases", shape = (3 * dim,),
initializer = tf.ones_initializer())
Wx, Uh, inter = tf.split(beta * tf.concat([inp1, inp2, inp1 * inp2], axis = 1),
num_or_size_splits = 3, axis = 1)
output = Wx + Uh + inter + b
return output
def __call__(self, inputs, state, scope = None):
scope = scope or type(self).__name__
with tf.variable_scope(scope, reuse = self.reuse):
c, h = state
inputSize = int(inputs.shape[1])
Wx = self.mulWeights(inputs, inputSize, self.numUnits, name = "Wxi")
Uh = self.mulWeights(h, self.numUnits, self.numUnits, name = "Uhi")
i = self.addBiases(Wx, Uh, self.numUnits, name = "i")
Wx = self.mulWeights(inputs, inputSize, self.numUnits, name = "Wxj")
Uh = self.mulWeights(h, self.numUnits, self.numUnits, name = "Uhj")
j = self.addBiases(Wx, Uh, self.numUnits, name = "l")
Wx = self.mulWeights(inputs, inputSize, self.numUnits, name = "Wxf")
Uh = self.mulWeights(h, self.numUnits, self.numUnits, name = "Uhf")
f = self.addBiases(Wx, Uh, self.numUnits, name = "f")
Wx = self.mulWeights(inputs, inputSize, self.numUnits, name = "Wxo")
Uh = self.mulWeights(h, self.numUnits, self.numUnits, name = "Uho")
o = self.addBiases(Wx, Uh, self.numUnits, name = "o")
# i, j, f, o = tf.split(value = concat, num_or_size_splits = 4, axis = 1)
newC = (c * tf.nn.sigmoid(f + self.forgetBias) + tf.nn.sigmoid(i) *
self.activation(j))
newH = self.activation(newC) * tf.nn.sigmoid(o)
newState = tf.nn.rnn_cell.LSTMStateTuple(newC, newH)
return newH, newState
def zero_state(self, batchSize, dtype = tf.float32):
return tf.nn.rnn_cell.LSTMStateTuple(tf.zeros((batchSize, self.numUnits), dtype = dtype),
tf.zeros((batchSize, self.numUnits), dtype = dtype))
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