Upload mi_lstm_cell.py

mi_lstm_cell.py

@@ -0,0 +1,77 @@
+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))
+