Add data_dir to load_mnist to avoid downloading data all the time

bob/learn/tensorflow/utils/util.py

 #!/usr/bin/env python
 # vim: set fileencoding=utf-8 :
 # @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# @date: Wed 11 May 2016 09:39:36 CEST 
+# @date: Wed 11 May 2016 09:39:36 CEST
 
 import numpy
 import tensorflow as tf
@@ -18,10 +18,10 @@ def compute_euclidean_distance(x, y):
         return d
 
 
-def load_mnist(perc_train=0.9):
+def load_mnist(perc_train=0.9, data_dir=None):
 
     import bob.db.mnist
-    db = bob.db.mnist.Database()
+    db = bob.db.mnist.Database(data_dir=data_dir)
     raw_data = db.data()
 
     # data  = raw_data[0].astype(numpy.float64)
@@ -189,15 +189,15 @@ def compute_accuracy(data_train, labels_train, data_validation, labels_validatio
             tp += 1
 
     return (float(tp) / data_validation.shape[0]) * 100
-    
-    
+
+
 def debug_embbeding(image, architecture, embbeding_dim=2, feature_layer="fc3"):
     """
     """
     import tensorflow as tf
     from bob.learn.tensorflow.utils.session import Session
-    
-    session = Session.instance(new=False).session    
+
+    session = Session.instance(new=False).session
     inference_graph = architecture.compute_graph(architecture.inference_placeholder, feature_layer=feature_layer, training=False)
 
     embeddings = numpy.zeros(shape=(image.shape[0], embbeding_dim))
@@ -208,4 +208,3 @@ def debug_embbeding(image, architecture, embbeding_dim=2, feature_layer="fc3"):
         embeddings[i] = embedding
 
     return embeddings
-