char-rnn

examples/char-rnn.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# File: char-rnn.py
+# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+
+import tensorflow as tf
+import numpy as np
+import os, sys
+import argparse
+from collections import Counter
+import operator
+
+from tensorpack import *
+from tensorpack.models import  *
+from tensorpack.utils import  *
+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
+
+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'
+
+class CharRNNData(DataFlow):
+    def __init__(self, input_file, size):
+        self.seq_length = SEQ_LEN
+        self._size = size
+        self.rng = get_rng(self)
+
+        # preprocess data
+        with open(input_file) as f:
+            data = f.read()
+        counter = Counter(data)
+        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
+        self.lut = LookUpTable(self.chars)
+        self.whole_seq = np.array(list(map(self.lut.get_idx, data)), dtype='int32')
+
+    def reset_state(self):
+        self.rng = get_rng(self)
+
+    def size(self):
+        return self._size
+
+    def get_data(self):
+        random_starts = self.rng.randint(0,
+                self.whole_seq.shape[0] - self.seq_length - 1, (self._size,))
+        for st in random_starts:
+            seq = self.whole_seq[st:st + self.seq_length + 1]
+            yield [seq[:-1], seq[1:]]
+
+
+class Model(ModelDesc):
+    def _get_input_vars(self):
+        return [InputVar(tf.int32, (None, SEQ_LEN), 'input'),
+                InputVar(tf.int32, (None, 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)
+
+        self.initial = initial = cell.zero_state(tf.shape(input)[0], tf.float32)
+
+        embeddingW = tf.get_variable('embedding', [VOCAB_SIZE, 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.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)
+
+        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])]
+
+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)
+    step_per_epoch = ds.size()
+
+    lr = tf.Variable(2e-3, trainable=False, name='learning_rate')
+    tf.scalar_summary('learning_rate', lr)
+
+    return TrainConfig(
+        dataset=ds,
+        optimizer=tf.train.AdamOptimizer(lr),
+        callbacks=Callbacks([
+            StatPrinter(),
+            ModelSaver(),
+            HumanHyperParamSetter('learning_rate', 'hyper.txt')
+        ]),
+        model=Model(),
+        step_per_epoch=step_per_epoch,
+        max_epoch=50,
+    )
+
+def sample(path, start, length):
+    """
+    :param path: path to the model
+    :param start: a `str`. the starting characters
+    :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)
+
+    model = Model()
+    input_vars = model.get_input_vars()
+    model.get_cost(input_vars, False)
+    sess = tf.Session()
+    tfutils.SaverRestore(path).init(sess)
+
+    dummy_input = np.zeros((1,1), dtype='int32')
+    with sess.as_default():
+        # feed the starting sentence
+        state = model.initial.eval({input_vars[0]: dummy_input})
+        for c in start[:-1]:
+            x = np.array([[ds.lut.get_idx(c)]], dtype='int32')
+            state = model.last_state.eval({input_vars[0]: x, model.initial: state})
+
+        def pick(prob):
+            t = np.cumsum(prob)
+            s = np.sum(prob)
+            return(int(np.searchsorted(t, np.random.rand(1) * s)))
+
+        # generate more
+        ret = start
+        c = start[-1]
+        for k in range(length):
+            x = np.array([[ds.lut.get_idx(c)]], dtype='int32')
+            [prob, state] = sess.run([model.prob, model.last_state],
+                    {input_vars[0]: x, model.initial: state})
+            c = ds.lut.get_obj(pick(prob[0]))
+            ret += c
+        print(ret)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.') # nargs='*' in multi mode
+    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')
+    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':
+        sample(args.load, args.start, args.num)
+        sys.exit()
+    else:
+        with tf.Graph().as_default():
+            config = get_config()
+            if args.load:
+                config.session_init = SaverRestore(args.load)
+            QueueInputTrainer(config).train()
+

tensorpack/callbacks/summary.py

@@ -59,7 +59,7 @@ class StatHolder(object):
     def _print_stat(self):
         for k, v in sorted(self.stat_now.items(), key=operator.itemgetter(0)):
             if self.print_tag is None or k in self.print_tag:
-                logger.info('{}: {:.5f}'.format(k, v))
+                logger.info('{}: {:.5g}'.format(k, v))
 
     def _write_stat(self):
         tmp_filename = self.filename + '.tmp'

tensorpack/tfutils/common.py

@@ -6,6 +6,11 @@
 from ..utils.naming import *
 import tensorflow as tf
 
+__all__ = ['get_default_sess_config',
+            'get_global_step',
+            'get_global_step_var',
+            'get_op_var_name']
+
 def get_default_sess_config(mem_fraction=0.9):
     """
     Return a better session config to use as default.

tensorpack/tfutils/gradproc.py

@@ -9,7 +9,7 @@ import re
 from ..utils import logger
 
 __all__ = ['GradientProcessor', 'SummaryGradient', 'CheckGradient',
-           'ScaleGradient']
+           'ScaleGradient', 'MapGradient']
 
 class GradientProcessor(object):
     __metaclass__ = ABCMeta
@@ -77,3 +77,27 @@ class ScaleGradient(GradientProcessor):
             else:
                 ret.append((grad, var))
         return ret
+
+class MapGradient(GradientProcessor):
+    """
+    Apply a function on all gradient if the name matches regex.
+    """
+    def __init__(self, func, regex='.*'):
+        """
+        :param func: takes a tensor and returns a tensor
+        ;param regex: used to match variables. default to match all variables.
+        """
+        self.func = func
+        if not regex.endswith('$'):
+            regex = regex + '$'
+        self.regex = regex
+
+    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))
+        return ret

tensorpack/tfutils/sessinit.py

@@ -73,7 +73,7 @@ class SaverRestore(SessionInit):
         """
         while len(vars_multimap):
             ret = {}
-            for k in vars_multimap.keys():
+            for k in list(vars_multimap.keys()):
                 v = vars_multimap[k]
                 ret[k] = v[-1]
                 del v[-1]

tensorpack/utils/lut.py

@@ -11,3 +11,12 @@ class LookUpTable(object):
     def __init__(self, objlist):
         self.idx2obj = dict(enumerate(objlist))
         self.obj2idx = {v : k for k, v in six.iteritems(self.idx2obj)}
+
+    def size(self):
+        return len(self.idx2obj)
+
+    def get_obj(self, idx):
+        return self.idx2obj[idx]
+
+    def get_idx(self, obj):
+        return self.obj2idx[obj]