use rnn.rnn

examples/char-rnn.py

@@ -19,8 +19,8 @@ from tensorpack.tfutils.gradproc import  *
 from tensorpack.utils.lut import LookUpTable
 from tensorpack.callbacks import *
 
-from tensorflow.models.rnn import rnn_cell
-from tensorflow.models.rnn import seq2seq
+from tensorflow.python.ops import rnn_cell
+from tensorflow.python.ops import rnn
 
 if six.PY2:
     class NS: pass  # this is a hack
@@ -30,7 +30,7 @@ else:
 param = NS()
 # some model hyperparams to set
 param.batch_size = 128
-param.rnn_size = 128
+param.rnn_size = 256
 param.num_rnn_layer = 2
 param.seq_len = 50
 param.grad_clip = 5.
@@ -90,7 +90,7 @@ class Model(ModelDesc):
         input_list = [tf.squeeze(x, [1]) for x in input_list]
 
         # seqlen is 1 in inference. don't need loop_function
-        outputs, last_state = seq2seq.rnn_decoder(input_list, initial, cell, scope='rnnlm')
+        outputs, last_state = rnn.rnn(cell, input_list, initial, scope='rnnlm')
         self.last_state = tf.identity(last_state, 'last_state')
         # seqlen x (Bxrnnsize)
         output = tf.reshape(tf.concat(1, outputs), [-1, param.rnn_size])  # (seqlenxB) x rnnsize
@@ -125,7 +125,8 @@ def get_config():
         callbacks=Callbacks([
             StatPrinter(),
             ModelSaver(),
-            HumanHyperParamSetter('learning_rate', 'hyper.txt')
+            #HumanHyperParamSetter('learning_rate', 'hyper.txt')
+            SeduledHyperParamSetter('learning_rate', [(25, 2e-4)])
         ]),
         model=Model(),
         step_per_epoch=step_per_epoch,
@@ -184,6 +185,7 @@ if __name__ == '__main__':
             default='The ', help='initial text sequence')
     parser_sample.add_argument('-t', '--temperature', type=float,
             default=1, help='softmax temperature')
+    parser_train = subparsers.add_parser('train', help='train')
     args = parser.parse_args()
     if args.gpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu