temp commit

architectures/tensorflow_components/architecture.py

@@ -48,7 +48,7 @@ class TensorFlowArchitecture(Architecture):
         self.network_is_local = network_is_local
         assert tuning_parameters.agent.tensorflow_support, 'TensorFlow is not supported for this agent'
         self.sess = tuning_parameters.sess
-        self.inputs = []
+        self.inputs = {}
         self.outputs = []
         self.targets = []
         self.losses = []
@@ -106,7 +106,8 @@ class TensorFlowArchitecture(Architecture):
             # gradients of the outputs w.r.t. the inputs
             # at the moment, this is only used by ddpg
             if len(self.outputs) == 1:
-                self.gradients_wrt_inputs = [tf.gradients(self.outputs[0], input_ph) for input_ph in self.inputs]
+                # TODO: convert gradients_with_respect_to_inputs into dictionary?
+                self.gradients_wrt_inputs = [tf.gradients(self.outputs[0], input_ph) for input_ph in self.inputs.values()]
                 self.gradients_weights_ph = tf.placeholder('float32', self.outputs[0].shape, 'output_gradient_weights')
                 self.weighted_gradients = tf.gradients(self.outputs[0], self.trainable_weights, self.gradients_weights_ph)
 
@@ -169,9 +170,8 @@ class TensorFlowArchitecture(Architecture):
         # feed inputs
         if additional_fetches is None:
             additional_fetches = []
-        inputs = force_list(inputs)
 
-        feed_dict = dict(zip(self.inputs, inputs))
+        feed_dict = self._feed_dict(inputs)
 
         # feed targets
         targets = force_list(targets)
@@ -266,6 +266,12 @@ class TensorFlowArchitecture(Architecture):
                 while self.tp.sess.run(self.release_counter) % self.tp.num_threads != 0:
                     time.sleep(0.00001)
 
+    def _feed_dict(self, inputs):
+        return {
+            self.inputs[input_name]: input_value
+            for input_name, input_value in inputs.items()
+        }
+
     def predict(self, inputs, outputs=None):
         """
         Run a forward pass of the network using the given input
@@ -275,8 +281,8 @@ class TensorFlowArchitecture(Architecture):
 
         WARNING: must only call once per state since each call is assumed by LSTM to be a new time step.
         """
-
-        feed_dict = dict(zip(self.inputs, force_list(inputs)))
+        # TODO: rename self.inputs -> self.input_placeholders
+        feed_dict = self._feed_dict(inputs)
         if outputs is None:
             outputs = self.outputs
 
@@ -290,21 +296,21 @@ class TensorFlowArchitecture(Architecture):
 
         return squeeze_list(output)
 
-    def train_on_batch(self, inputs, targets, scaler=1., additional_fetches=None):
-        """
-        Given a batch of examples and targets, runs a forward pass & backward pass and then applies the gradients
-        :param additional_fetches: Optional tensors to fetch during the training process
-        :param inputs: The input for the network
-        :param targets: The targets corresponding to the input batch
-        :param scaler: A scaling factor that allows rescaling the gradients before applying them
-        :return: The loss of the network
-        """
-        if additional_fetches is None:
-            additional_fetches = []
-        force_list(additional_fetches)
-        loss = self.accumulate_gradients(inputs, targets, additional_fetches=additional_fetches)
-        self.apply_and_reset_gradients(self.accumulated_gradients, scaler)
-        return loss
+    # def train_on_batch(self, inputs, targets, scaler=1., additional_fetches=None):
+    #     """
+    #     Given a batch of examples and targets, runs a forward pass & backward pass and then applies the gradients
+    #     :param additional_fetches: Optional tensors to fetch during the training process
+    #     :param inputs: The input for the network
+    #     :param targets: The targets corresponding to the input batch
+    #     :param scaler: A scaling factor that allows rescaling the gradients before applying them
+    #     :return: The loss of the network
+    #     """
+    #     if additional_fetches is None:
+    #         additional_fetches = []
+    #     force_list(additional_fetches)
+    #     loss = self.accumulate_gradients(inputs, targets, additional_fetches=additional_fetches)
+    #     self.apply_and_reset_gradients(self.accumulated_gradients, scaler)
+    #     return loss
 
     def get_weights(self):
         """