Hdf5 loading

Hi,

This MR allows the usage of bob.io.base.load for the batching using hdf5 files in the Bob format. For that I introduced this function from_file_name_to_tensor.

The implementation is not super clean; I had to introduce the a keyword argument called extension in the batching functions (https://gitlab.idiap.ch/bob/bob.learn.tensorflow/blob/3dcbd6afec902432152c17142dbd55ac0ca13f41/bob/learn/tensorflow/dataset/image.py#L10); but so far the thing does the job nicelly.

Mind to review that one ?

bob/learn/tensorflow/dataset/__init__.py

@@ -2,7 +2,6 @@ import tensorflow as tf
 import numpy
 import os
 import bob.io.base
-import bob.io.image
 
 DEFAULT_FEATURE = {'data': tf.FixedLenFeature([], tf.string),
                    'label': tf.FixedLenFeature([], tf.int64),
@@ -10,13 +9,13 @@ DEFAULT_FEATURE = {'data': tf.FixedLenFeature([], tf.string),
 
 
 def from_hdf5file_to_tensor(filename):
+    import bob.io.image
     data = bob.io.image.to_matplotlib(bob.io.base.load(filename))
     
-    #reshaping to ndim == 3
+    #reshaping to ndim == 3    
     if data.ndim == 2:
         data = numpy.reshape(data, (data.shape[0], data.shape[1], 1))
     data = data.astype("float32")
-    data = numpy.zeros((160, 160, 1), dtype="float32")
     
     return data