Updates

• config file from train.py accepts tensors as input
• TFRecord, add keyword argument to flag the shuffling mechanism
• Added InceptionResnetV1 in our basket of networks
• Added an option to sum the regularization losses in the CrossEntropy loss
• Implemented validation with embeddings (officially appending to the tensorboard)
• Created method Trainer.load_variables_from_external_model that load variables from another model in the current graph
• Implemented MeanSoftMaxLossCenterLoss (http://ydwen.github.io/papers/WenECCV16.pdf)
• Patched InceptionV2 (Added the flag trainable for each variable in the model)
• Implemented TFRecordImage (read, data augmentation, scaling and casting to float32)
• Added data type as input in the TFRecord datashufflers

bob/learn/tensorflow/datashuffler/TFRecord.py

@@ -12,13 +12,19 @@ from bob.learn.tensorflow.datashuffler.Normalizer import Linear
 
 class TFRecord(object):
 
+    """
+    Datashuffler that wraps tfrecords    
+    """
+
+
     def __init__(self,filename_queue,
                          input_shape=[None, 28, 28, 1],
                          input_dtype="float32",
                          batch_size=32,
                          seed=10,
                          prefetch_capacity=50,
-                         prefetch_threads=5):
+                         prefetch_threads=5, 
+                         shuffle=True):
 
         # Setting the seed for the pseudo random number generator
         self.seed = seed
@@ -40,6 +46,8 @@ class TFRecord(object):
         
         self.data_ph = None
         self.label_ph = None
+        
+        self.shuffle = shuffle
 
     def __call__(self, element, from_queue=False):
         """
@@ -83,10 +91,14 @@ class TFRecord(object):
         # Reshape image data into the original shape
         image = tf.reshape(image, self.input_shape[1:])
         
+        if self.shuffle:
+            data_ph, label_ph = tf.train.shuffle_batch([image, label], batch_size=self.batch_size,
+                             capacity=self.prefetch_capacity, num_threads=self.prefetch_threads,
+                             min_after_dequeue=1, name="shuffle_batch")
+        else:
+            data_ph, label_ph = tf.train.batch([image, label], batch_size=self.batch_size,
+                             capacity=self.prefetch_capacity, num_threads=self.prefetch_threads, name="batch")
         
-        data_ph, label_ph = tf.train.shuffle_batch([image, label], batch_size=self.batch_size,
-                         capacity=self.prefetch_capacity, num_threads=self.prefetch_threads,
-                         min_after_dequeue=1, name="shuffle_batch")
         
         
         self.data_ph = data_ph