Upload mi_gru_cell.py

mi_gru_cell.py

@@ -0,0 +1,64 @@
+import tensorflow as tf
+import numpy as np
+
+class MiGRUCell(tf.nn.rnn_cell.RNNCell):
+    def __init__(self, num_units, input_size = None, activation = tf.tanh, reuse = None):
+        self.numUnits = num_units
+        self.activation = activation
+        self.reuse = reuse
+
+    @property
+    def state_size(self):
+        return 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, bInitial = 0, name = ""):
+        with tf.variable_scope("additiveBiases" + name):
+            b = tf.get_variable("biases", shape = (dim,), 
+                initializer = tf.zeros_initializer()) + bInitial
+        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):
+            inputSize = int(inputs.shape[1])
+            
+            Wxr = self.mulWeights(inputs, inputSize, self.numUnits, name = "Wxr")
+            Uhr = self.mulWeights(state, self.numUnits, self.numUnits, name = "Uhr")
+            
+            r = tf.nn.sigmoid(self.addBiases(Wxr, Uhr, self.numUnits, bInitial = 1, name = "r"))
+            
+            Wxu = self.mulWeights(inputs, inputSize, self.numUnits, name = "Wxu")
+            Uhu = self.mulWeights(state, self.numUnits, self.numUnits, name = "Uhu")
+            
+            u = tf.nn.sigmoid(self.addBiases(Wxu, Uhu, self.numUnits, bInitial = 1, name = "u"))
+            # r, u = tf.split(gates, num_or_size_splits = 2, axis = 1)
+
+            Wx = self.mulWeights(inputs, inputSize, self.numUnits, name = "Wxl")
+            Urh = self.mulWeights(r * state, self.numUnits, self.numUnits, name = "Uhl")
+            c = self.activation(self.addBiases(Wx, Urh, self.numUnits, name = "2"))
+
+            newH = u * state + (1 - u) * c # switch u and 1-u?
+        return newH, newH
+
+    def zero_state(self, batchSize, dtype = tf.float32):
+        return tf.zeros((batchSize, self.numUnits), dtype = dtype)
+