KerasTTDense -> KerasDense and fixes w.r.t. new arg names

t3f/neural.py

@@ -7,7 +7,7 @@
 import tensorflow as tf
 
 
-class KerasTTDense(Layer):
+class KerasDense(Layer):
   counter = 0
 
   def __init__(self, input_dims, output_dims, tt_rank=2,
@@ -42,36 +42,36 @@ def __init__(self, input_dims, output_dims, tt_rank=2,
     self.use_bias = use_bias
     self.kernel_initializer = kernel_initializer
     self.bias_initializer = bias_initializer
-    super(KerasTTDense, self).__init__(**kwargs)
+    super(KerasDense, self).__init__(**kwargs)
 
   def build(self, input_shape):
-    if self.init == 'glorot':
+    if self.kernel_initializer == 'glorot':
       initializer = t3f.glorot_initializer(self.tt_shape,
                                            tt_rank=self.tt_rank)
-    elif self.init == 'he':
+    elif self.kernel_initializer == 'he':
       initializer = t3f.he_initializer(self.tt_shape,
                                        tt_rank=self.tt_rank)
-    elif self.init == 'lecun':
+    elif self.kernel_initializer == 'lecun':
       initializer = t3f.lecun_initializer(self.tt_shape,
                                           tt_rank=self.tt_rank)
     else:
       raise ValueError('Unknown kernel_initializer "%s", only "glorot",'
-                       '"he", and "lecun"  are supported' % self.init)
-    name = 'tt_dense_matrix_{}'.format(KerasTTDense.counter)
+                       '"he", and "lecun"  are supported' % self.kernel_initializer)
+    name = 'tt_dense_matrix_{}'.format(KerasDense.counter)
     self.W = t3f.get_variable(name, initializer=initializer)
     self.b = None
-    if self.bias:
-      b_name = 'tt_dense_b_{}'.format(KerasTTDense.counter)
-      b_init = tf.constant_initializer(self.bias_init)
+    if self.use_bias:
+      b_name = 'tt_dense_b_{}'.format(KerasDense.counter)
+      b_init = tf.constant_initializer(self.bias_initializer)
       self.b = tf.get_variable(b_name, shape=self.output_dim,
                                initializer=b_init)
-    KerasTTDense.counter += 1
+    KerasDense.counter += 1
     self.trainable_weights = list(self.W.tt_cores)
     if self.b is not None:
       self.trainable_weights.append(self.b)
 
   def call(self, x):
-    if self.bias:
+    if self.use_bias:
       h = t3f.matmul(x, self.W) + self.b
     else:
       h = t3f.matmul(x, self.W)