[test_lstm] Use rnn from bob correcly

bob/learn/tensorflow/layers/RNN.py

 #!/usr/bin/env python
 
 import tensorflow as tf
-
 from tensorflow.python.layers import base
 
-# def lstm(inputs, n_hidden, name=None):
-#     """
-#     """
-#     return LSTM(n_hidden=n_hidden, name=name)(inputs)
-
-def rnn(inputs, n_hidden, cell_fn, cell_args, name=None):
+def rnn(inputs, n_hidden,
+        cell_fn = tf.nn.rnn_cell.BasicLSTMCell,
+        cell_args = { "forget_bias": 1.0, },
+        name = None):
     """
     """
-    return RNN(n_hidden=n_hidden,
+    return RNN(n_hidden = n_hidden,
                cell_fn = cell_fn,
                cell_args = cell_args,
                name=name)(inputs)

bob/learn/tensorflow/test/test_lstm.py

@@ -7,7 +7,7 @@ from bob.learn.tensorflow.loss import BaseLoss
 from bob.learn.tensorflow.trainers import Trainer, constant
 from bob.learn.tensorflow.utils import load_real_mnist, load_mnist
 from bob.learn.tensorflow.utils.session import Session
-from bob.learn.tensorflow.layers import lstm
+from bob.learn.tensorflow.layers import rnn
 
 import tensorflow as tf
 import shutil
@@ -95,7 +95,7 @@ def test_dnn_trainer():
         graph = input_pl[:, n_input:]
         graph = tf.reshape(graph, (-1, n_steps, n_input))
         graph = tf.unstack(graph, n_steps, 1)
-        graph = lstm(graph, n_hidden)
+        graph = rnn(graph, n_hidden)
         graph = slim.fully_connected(graph, n_classes, activation_fn=None)
 
     # Loss for the softmax