1 files changed, 49 insertions, 0 deletions
diff --git a/tensorflow/models/rnn/linear.py b/tensorflow/models/rnn/linear.py
new file mode 100644
index 0000000000..96278e73e4
--- /dev/null
+++ b/tensorflow/models/rnn/linear.py
@@ -0,0 +1,49 @@
+"""Basic linear combinations that implicitly generate variables."""
+
+import tensorflow as tf
+
+
+def linear(args, output_size, bias, bias_start=0.0, scope=None):
+  """Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
+
+  Args:
+    args: a 2D Tensor or a list of 2D, batch x n, Tensors.
+    output_size: int, second dimension of W[i].
+    bias: boolean, whether to add a bias term or not.
+    bias_start: starting value to initialize the bias; 0 by default.
+    scope: VariableScope for the created subgraph; defaults to "Linear".
+
+  Returns:
+    A 2D Tensor with shape [batch x output_size] equal to
+    sum_i(args[i] * W[i]), where W[i]s are newly created matrices.
+
+  Raises:
+    ValueError: if some of the arguments has unspecified or wrong shape.
+  """
+  assert args
+  if not isinstance(args, (list, tuple)):
+    args = [args]
+
+  # Calculate the total size of arguments on dimension 1.
+  total_arg_size = 0
+  shapes = [a.get_shape().as_list() for a in args]
+  for shape in shapes:
+    if len(shape) != 2:
+      raise ValueError("Linear is expecting 2D arguments: %s" % str(shapes))
+    if not shape[1]:
+      raise ValueError("Linear expects shape[1] of arguments: %s" % str(shapes))
+    else:
+      total_arg_size += shape[1]
+
+  # Now the computation.
+  with tf.variable_scope(scope or "Linear"):
+    matrix = tf.get_variable("Matrix", [total_arg_size, output_size])
+    if len(args) == 1:
+      res = tf.matmul(args[0], matrix)
+    else:
+      res = tf.matmul(tf.concat(1, args), matrix)
+    if not bias:
+      return res
+    bias_term = tf.get_variable("Bias", [output_size],
+                                initializer=tf.constant_initializer(bias_start))
+  return res + bias_term