[test_lstm] Use slim fully connected

bob/learn/tensorflow/test/test_lstm.py

@@ -76,14 +76,18 @@ def test_dnn_trainer():
         graph = architecture(input_pl)
 
     elif version == "lstm":
-        W = tf.Variable(tf.random_normal([n_hidden, n_classes]))
-        b = tf.Variable(tf.random_normal([n_classes]))
+        slim = tf.contrib.slim
+        # W = tf.Variable(tf.random_normal([n_hidden, n_classes]))
+        # b = tf.Variable(tf.random_normal([n_classes]))
         graph = input_pl[:, n_input:]
         graph = tf.reshape(graph, (-1, n_steps, n_input))
         graph = tf.unstack(graph, n_steps, 1)
         lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0)
         outputs, states = tf.nn.static_rnn(lstm_cell, graph, dtype=tf.float32)
-        graph = tf.matmul(outputs[-1], W) + b
+        # graph = tf.matmul(outputs[-1], W) + b
+        graph = outputs[-1]
+        graph = slim.fully_connected(graph, n_classes, activation_fn=None)
+
 
     # Loss for the softmax
     loss = BaseLoss(tf.nn.sparse_softmax_cross_entropy_with_logits, tf.reduce_mean)