fix ddpg

architectures/tensorflow_components/architecture.py

@@ -13,13 +13,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+import time
+
+import six
+import numpy as np
+import tensorflow as tf
 
 from architectures.architecture import Architecture
-import tensorflow as tf
 from utils import force_list, squeeze_list
 from configurations import Preset, MiddlewareTypes
-import numpy as np
-import time
 
 def variable_summaries(var):
     """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
@@ -269,17 +271,29 @@ class TensorFlowArchitecture(Architecture):
     def _feed_dict(self, inputs):
         feed_dict = {}
         for input_name, input_value in inputs.items():
-            if input_name not in self.inputs:
+            if isinstance(input_name, six.string_types):
+                if input_name not in self.inputs:
+                    raise ValueError((
+                        'input name {input_name} was provided to create a feed '
+                        'dictionary, but there is no placeholder with that name. '
+                        'placeholder names available include: {placeholder_names}'
+                    ).format(
+                        input_name=input_name,
+                        placeholder_names=', '.join(self.inputs.keys())
+                    ))
+
+                feed_dict[self.inputs[input_name]] = input_value
+            elif isinstance(input_name, tf.Tensor) and input_name.op.type == 'Placeholder':
+                feed_dict[input_name] = input_value
+            else:
                 raise ValueError((
-                    'input name {input_name} was provided to create a feed '
-                    'dictionary, but there is no placeholder with that name. '
-                    'placeholder names available include: {placeholder_names}'
+                    'input dictionary expects strings or placeholders as keys, '
+                    'but found key {key} of type {type}'
                 ).format(
-                    input_name=input_name,
-                    placeholder_names=', '.join(self.inputs.keys())
+                    key=input_name,
+                    type=type(input_name),
                 ))
 
-            feed_dict[self.inputs[input_name]] = input_value
         return feed_dict
 
     def predict(self, inputs, outputs=None):

architectures/tensorflow_components/heads.py

@@ -23,7 +23,7 @@ from utils import force_list
 def normalized_columns_initializer(std=1.0):
     def _initializer(shape, dtype=None, partition_info=None):
         out = np.random.randn(*shape).astype(np.float32)
-        out *= std / np.sqrt(np.square(out).sum(axis=0, keep_dims=True))
+        out *= std / np.sqrt(np.square(out).sum(axis=0, keepdims=True))
         return tf.constant(out)
     return _initializer