better handle params

examples/char-rnn.py

@@ -9,6 +9,8 @@
 import argparse
 from collections import Counter
 import operator
+import six
+from six.moves import map, range
 
 from tensorpack import *
 from tensorpack.models import  *
@@ -20,16 +22,26 @@
 from tensorflow.models.rnn import rnn_cell
 from tensorflow.models.rnn import seq2seq
 
-BATCH_SIZE = 128
-RNN_SIZE = 128  # hidden state size
-NUM_RNN_LAYER = 2
-SEQ_LEN = 50
-VOCAB_SIZE = None   # will be initialized by CharRNNData
-CORPUS = 'input.txt'
+if six.PY2:
+    class NS: pass  # this is a hack
+else:
+    import types
+    NS = types.SimpleNamespace  # this is what I wanted..
+param = NS()
+# some model hyperparams to set
+param.batch_size = 128
+param.rnn_size = 128
+param.num_rnn_layer = 2
+param.seq_len = 50
+param.grad_clip = 5.
+param.vocab_size = None
+param.softmax_temprature = 1
+param.corpus = 'input.txt'
+# Get corpus to play with at: http://cs.stanford.edu/people/karpathy/char-rnn/
 
 class CharRNNData(DataFlow):
     def __init__(self, input_file, size):
-        self.seq_length = SEQ_LEN
+        self.seq_length = param.seq_len
         self._size = size
         self.rng = get_rng(self)
 
@@ -40,8 +52,7 @@ def __init__(self, input_file, size):
         char_cnt = sorted(counter.items(), key=operator.itemgetter(1), reverse=True)
         self.chars = [x[0] for x in char_cnt]
         self.vocab_size = len(self.chars)
-        global VOCAB_SIZE
-        VOCAB_SIZE = self.vocab_size
+        param.vocab_size = self.vocab_size
         self.lut = LookUpTable(self.chars)
         self.whole_seq = np.array(list(map(self.lut.get_idx, data)), dtype='int32')
 
@@ -61,50 +72,48 @@ def get_data(self):
 
 class Model(ModelDesc):
     def _get_input_vars(self):
-        return [InputVar(tf.int32, (None, SEQ_LEN), 'input'),
-                InputVar(tf.int32, (None, SEQ_LEN), 'nextinput')
-               ]
+        return [InputVar(tf.int32, (None, param.seq_len), 'input'),
+                InputVar(tf.int32, (None, param.seq_len), 'nextinput') ]
 
     def _get_cost(self, input_vars, is_training):
         input, nextinput = input_vars
 
-        cell = rnn_cell.BasicLSTMCell(RNN_SIZE)
-        cell = rnn_cell.MultiRNNCell([cell] * NUM_RNN_LAYER)
+        cell = rnn_cell.BasicLSTMCell(num_units=param.rnn_size)
+        cell = rnn_cell.MultiRNNCell([cell] * param.num_rnn_layer)
 
         self.initial = initial = cell.zero_state(tf.shape(input)[0], tf.float32)
 
-        embeddingW = tf.get_variable('embedding', [VOCAB_SIZE, RNN_SIZE])
+        embeddingW = tf.get_variable('embedding', [param.vocab_size, param.rnn_size])
         input_feature = tf.nn.embedding_lookup(embeddingW, input) # B x seqlen x rnnsize
 
-        input_list = tf.split(1, SEQ_LEN, input_feature)    #seqlen x (Bx1xrnnsize)
+        input_list = tf.split(1, param.seq_len, input_feature)    #seqlen x (Bx1xrnnsize)
         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')
         self.last_state = tf.identity(last_state, 'last_state')
         # seqlen x (Bxrnnsize)
-        output = tf.reshape(tf.concat(1, outputs), [-1, RNN_SIZE])  # (seqlenxB) x rnnsize
-        logits = FullyConnected('fc', output, VOCAB_SIZE, nl=tf.identity)
-        self.prob = tf.nn.softmax(logits)
+        output = tf.reshape(tf.concat(1, outputs), [-1, param.rnn_size])  # (seqlenxB) x rnnsize
+        logits = FullyConnected('fc', output, param.vocab_size, nl=tf.identity)
+        self.prob = tf.nn.softmax(logits / param.softmax_temprature)
 
         xent_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
                 logits, symbolic_functions.flatten(nextinput))
         xent_loss = tf.reduce_mean(xent_loss, name='xent_loss')
-        tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, xent_loss)
-
         summary.add_param_summary([('.*/W', ['histogram'])])   # monitor histogram of all W
         return tf.add_n([xent_loss], name='cost')
 
     def get_gradient_processor(self):
-        return [MapGradient(lambda grad: tf.clip_by_global_norm([grad], 5.)[0][0])]
+        return [MapGradient(lambda grad: tf.clip_by_global_norm(
+            [grad], param.grad_clip)[0][0])]
 
 def get_config():
     basename = os.path.basename(__file__)
     logger.set_logger_dir(
         os.path.join('train_log', basename[:basename.rfind('.')]))
 
-    ds = CharRNNData(CORPUS, 100000)
-    ds = BatchData(ds, 128)
+    ds = CharRNNData(param.corpus, 100000)
+    ds = BatchData(ds, param.batch_size)
     step_per_epoch = ds.size()
 
     lr = tf.Variable(2e-3, trainable=False, name='learning_rate')
@@ -130,9 +139,8 @@ def sample(path, start, length):
     :param length: a `int`. the length of text to generate
     """
     # initialize vocabulary and sequence length
-    global SEQ_LEN
-    SEQ_LEN = 1
-    ds = CharRNNData(CORPUS, 100000)
+    param.seq_len = 1
+    ds = CharRNNData(param.corpus, 100000)
 
     model = Model()
     input_vars = model.get_input_vars()
@@ -170,17 +178,20 @@ def pick(prob):
     parser.add_argument('--load', help='load model')
     subparsers = parser.add_subparsers(title='command', dest='command')
     parser_sample = subparsers.add_parser('sample', help='sample a trained model')
-    parser_sample.add_argument('-n', '--num', type=int, default=300,
-            help='length of text to generate')
-    parser_sample.add_argument('-s', '--start', required=True, default='The ',
-            help='initial text sequence')
+    parser_sample.add_argument('-n', '--num', type=int,
+            default=300, help='length of text to generate')
+    parser_sample.add_argument('-s', '--start',
+            default='The ', help='initial text sequence')
+    parser_sample.add_argument('-t', '--temperature', type=float,
+            default=1, help='softmax temperature')
     args = parser.parse_args()
     if args.gpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
     else:
         os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 
     if args.command == 'sample':
+        param.softmax_temprature = args.temperature
         sample(args.load, args.start, args.num)
         sys.exit()
     else:

examples/mnist-convnet.py

@@ -8,7 +8,7 @@
 import os, sys
 import argparse
 
-import tensorpack as tp
+from tensorpack import *
 from tensorpack.models import  *
 from tensorpack.utils import  *
 from tensorpack.callbacks import *
@@ -56,7 +56,7 @@ def _get_cost(self, input_vars, is_training):
         tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, cost)
 
         # compute the number of failed samples, for ClassificationError to use at test time
-        wrong = tp.symbolic_functions.prediction_incorrect(logits, label)
+        wrong = symbolic_functions.prediction_incorrect(logits, label)
         nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
         tf.add_to_collection(
@@ -68,7 +68,7 @@ def _get_cost(self, input_vars, is_training):
                          name='regularize_loss')
         tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, wd_cost)
 
-        tp.summary.add_param_summary([('.*/W', ['histogram'])])   # monitor histogram of all W
+        summary.add_param_summary([('.*/W', ['histogram'])])   # monitor histogram of all W
         return tf.add_n([wd_cost, cost], name='cost')
 
 def get_config():
@@ -77,22 +77,18 @@ def get_config():
         os.path.join('train_log', basename[:basename.rfind('.')]))
 
     # prepare dataset
-    dataset_train = tp.BatchData(tp.dataset.Mnist('train'), 128)
-    dataset_test = tp.BatchData(tp.dataset.Mnist('test'), 256, remainder=True)
+    dataset_train = BatchData(dataset.Mnist('train'), 128)
+    dataset_test = BatchData(dataset.Mnist('test'), 256, remainder=True)
     step_per_epoch = dataset_train.size()
 
-    # prepare session
-    sess_config = tp.get_default_sess_config()
-    sess_config.gpu_options.per_process_gpu_memory_fraction = 0.5
-
     lr = tf.train.exponential_decay(
         learning_rate=1e-3,
-        global_step=tp.get_global_step_var(),
+        global_step=get_global_step_var(),
         decay_steps=dataset_train.size() * 10,
         decay_rate=0.3, staircase=True, name='learning_rate')
     tf.scalar_summary('learning_rate', lr)
 
-    return tp.TrainConfig(
+    return TrainConfig(
         dataset=dataset_train,
         optimizer=tf.train.AdamOptimizer(lr),
         callbacks=Callbacks([
@@ -101,7 +97,7 @@ def get_config():
             InferenceRunner(dataset_test,
                 [ScalarStats('cost'), ClassificationError() ])
         ]),
-        session_config=sess_config,
+        session_config=get_default_sess_config(0.5),
         model=Model(),
         step_per_epoch=step_per_epoch,
         max_epoch=100,
@@ -121,6 +117,5 @@ def get_config():
         config = get_config()
         if args.load:
             config.session_init = SaverRestore(args.load)
-        #tp.SimpleTrainer(config).train()
-        tp.QueueInputTrainer(config).train()
+        QueueInputTrainer(config).train()
 

tensorpack/tfutils/gradproc.py

@@ -96,7 +96,6 @@ def _process(self, grads):
         ret = []
         for grad, var in grads:
             if re.match(self.regex, var.op.name):
-                logger.info("DEBUG {}".format(var.op.name))
                 ret.append((self.func(grad), var))
             else:
                 ret.append((grad, var))