Restructured repository with new YOLO model

Removed and added neccessary files

agents/rl_drone.py

@@ -31,7 +31,7 @@ def __init__(self, env):
         self.actor.add(Dense(16))
         self.actor.add(Activation('relu'))
         self.actor.add(Dense(nb_actions,activation='tanh',kernel_initializer=RandomUniform()))
-        self.actor.add(Lambda(lambda x: x * 30.0))
+        self.actor.add(Lambda(lambda x: x * 60.0))
         print(self.actor.summary())
 
         action_input = Input(shape=(nb_actions,), name='action_input')

callbacks.py

@@ -0,0 +1,70 @@
+from keras.callbacks import TensorBoard, ModelCheckpoint
+import tensorflow as tf
+import numpy as np
+
+class CustomTensorBoard(TensorBoard):
+    """ to log the loss after each batch
+    """    
+    def __init__(self, log_every=1, **kwargs):
+        super(CustomTensorBoard, self).__init__(**kwargs)
+        self.log_every = log_every
+        self.counter = 0
+
+    def on_batch_end(self, batch, logs=None):
+        self.counter+=1
+        if self.counter%self.log_every==0:
+            for name, value in logs.items():
+                if name in ['batch', 'size']:
+                    continue
+                summary = tf.Summary()
+                summary_value = summary.value.add()
+                summary_value.simple_value = value.item()
+                summary_value.tag = name
+                self.writer.add_summary(summary, self.counter)
+            self.writer.flush()
+
+        super(CustomTensorBoard, self).on_batch_end(batch, logs)
+
+class CustomModelCheckpoint(ModelCheckpoint):
+    """ to save the template model, not the multi-GPU model
+    """
+    def __init__(self, model_to_save, **kwargs):
+        super(CustomModelCheckpoint, self).__init__(**kwargs)
+        self.model_to_save = model_to_save
+
+    def on_epoch_end(self, epoch, logs=None):
+        logs = logs or {}
+        self.epochs_since_last_save += 1
+        if self.epochs_since_last_save >= self.period:
+            self.epochs_since_last_save = 0
+            filepath = self.filepath.format(epoch=epoch + 1, **logs)
+            if self.save_best_only:
+                current = logs.get(self.monitor)
+                if current is None:
+                    warnings.warn('Can save best model only with %s available, '
+                                  'skipping.' % (self.monitor), RuntimeWarning)
+                else:
+                    if self.monitor_op(current, self.best):
+                        if self.verbose > 0:
+                            print('\nEpoch %05d: %s improved from %0.5f to %0.5f,'
+                                  ' saving model to %s'
+                                  % (epoch + 1, self.monitor, self.best,
+                                     current, filepath))
+                        self.best = current
+                        if self.save_weights_only:
+                            self.model_to_save.save_weights(filepath, overwrite=True)
+                        else:
+                            self.model_to_save.save(filepath, overwrite=True)
+                    else:
+                        if self.verbose > 0:
+                            print('\nEpoch %05d: %s did not improve from %0.5f' %
+                                  (epoch + 1, self.monitor, self.best))
+            else:
+                if self.verbose > 0:
+                    print('\nEpoch %05d: saving model to %s' % (epoch + 1, filepath))
+                if self.save_weights_only:
+                    self.model_to_save.save_weights(filepath, overwrite=True)
+                else:
+                    self.model_to_save.save(filepath, overwrite=True)
+
+        super(CustomModelCheckpoint, self).on_batch_end(epoch, logs)