mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 11:10:20 +01:00
fix ddpg
This commit is contained in:
@@ -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):
|
||||
|
||||
Reference in New Issue
Block a user