Introduce a work-around for constructing TensorFlow constants by hand

Author: brandonwillard

symbolic_pymc/tensorflow/meta.py

@@ -65,6 +65,20 @@ class MetaOpDefLibrary(object):
     }
     opdef_signatures = {}
 
+    def __init__(self):
+        #
+        # We need this in order to construct "Const" tensors directly, since
+        # the "value" attr in a meta `NodeDef` is just a NumPy array and not
+        # the `TensorProto` expected by `raw_ops.Const`.
+        #
+        def mt_const(value, dtype, name=None):
+            return tf.raw_ops.Const(
+                value=tensor_util.make_tensor_proto(value), dtype=dtype, name=name
+            )
+
+        opdef = op_def_registry.get("Const")
+        self.opdef_signatures[opdef.name] = self.make_opdef_sig(opdef, mt_const)
+
     @classmethod
     def get_op_info(cls, opdef):
         """Return the TF Python API function signature for a given `OpDef`.

tests/tensorflow/test_meta.py

@@ -677,6 +677,21 @@ def test_global_options():
         assert isvar(a_mt.name)
 
 
+@run_in_graph_mode
+def test_meta_const():
+    """Make sure we can create a Const tensor by hand."""
+
+    with tf.Graph().as_default():
+        one_mt = mt.const(1, 'int32', 'Const')
+
+    with tf.Graph().as_default():
+        another_one_mt = mt(1)
+
+    assert one_mt == another_one_mt
+    assert isinstance(one_mt.reify(), tf.Tensor)
+    assert one_mt.reify().op.type == 'Const'
+
+
 @run_in_graph_mode
 def test_meta_existing_names():