update

demo/mnist/api_train_v2.py

@@ -20,26 +20,29 @@ def main():
 
     adam_optimizer = paddle.optimizer.Adam(learning_rate=0.01)
 
+    trainer = paddle.trainer.SGD(cost=cost,
+                                 parameters=parameters,
+                                 update_equation=adam_optimizer)
+
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
-            if event.batch_id % 100 == 0:
-                print "Pass %d, Batch %d, Cost %f, %s" % (
-                    event.pass_id, event.batch_id, event.cost, event.metrics)
+            if event.batch_id % 1000 == 0:
+                result = trainer.test(reader=paddle.reader.batched(
+                    paddle.dataset.mnist.test(), batch_size=256))
+
+                print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics,
+                    result.metrics)
+
         else:
             pass
 
-    trainer = paddle.trainer.SGD(update_equation=adam_optimizer)
-
     trainer.train(
         reader=paddle.reader.batched(
             paddle.reader.shuffle(
                 paddle.dataset.mnist.train(), buf_size=8192),
             batch_size=32),
-        cost=cost,
-        parameters=parameters,
-        event_handler=event_handler,
-        reader_dict={images.name: 0,
-                     label.name: 1})
+        event_handler=event_handler)
 
 
 if __name__ == '__main__':

doc/howto/usage/cluster/cluster_train_cn.md

@@ -6,7 +6,7 @@
 
 在本文中，我们将阐释如何在集群上运行分布式 Paddle 训练作业。我们将以[推荐系统](https://github.com/baidu/Paddle/tree/develop/demo/recommendation)为例创建分布式的单进程训练。
 
-在本文中使用的[脚本](https://github.com/baidu/Paddle/tree/develop/paddle/scripts/cluster_train)通过 SSH 运行分布式作业。 它们还可以供那些运行更复杂的集群管理系统（如 MPI 和 [Kubernetes](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/cluster/k8s) ）的用户参考。
+在本文中使用的[脚本](https://github.com/baidu/Paddle/tree/develop/paddle/scripts/cluster_train)通过 SSH 运行分布式作业。 它们还可以供那些运行更复杂的集群管理系统（如 MPI 和 [Kubernetes](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/k8s) ）的用户参考。
 
 ## 前提条件
 

doc/howto/usage/cluster/cluster_train_en.md

@@ -2,7 +2,7 @@
 
 In this article, we explain how to run distributed Paddle training jobs on clusters.  We will create the distributed version of the single-process training example, [recommendation](https://github.com/baidu/Paddle/tree/develop/demo/recommendation).
 
-[Scripts](https://github.com/baidu/Paddle/tree/develop/paddle/scripts/cluster_train) used in this article launch distributed jobs via SSH.  They also work as a reference for users running more sophisticated cluster management systems like MPI and [Kubernetes](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/cluster/k8s).
+[Scripts](https://github.com/baidu/Paddle/tree/develop/paddle/scripts/cluster_train) used in this article launch distributed jobs via SSH.  They also work as a reference for users running more sophisticated cluster management systems like MPI and [Kubernetes](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/k8s).
 
 ## Prerequisite
 

python/paddle/v2/dataset/common.py

@@ -32,3 +32,10 @@ def download(url, module_name, md5sum):
             shutil.copyfileobj(r.raw, f)
 
     return filename
+
+
+def dict_add(a_dict, ele):
+    if ele in a_dict:
+        a_dict[ele] += 1
+    else:
+        a_dict[ele] = 1

python/paddle/v2/dataset/imdb.py

+# /usr/bin/env python
+# -*- coding:utf-8 -*-
+
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+IMDB dataset: http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz
+"""
+import paddle.v2.dataset.common
+import tarfile
+import Queue
+import re
+import string
+import threading
+
+__all__ = ['build_dict', 'train', 'test']
+
+URL = 'http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz'
+MD5 = '7c2ac02c03563afcf9b574c7e56c153a'
+
+
+# Read files that match pattern.  Tokenize and yield each file.
+def tokenize(pattern):
+    with tarfile.open(paddle.v2.dataset.common.download(URL, 'imdb',
+                                                        MD5)) as tarf:
+        # Note that we should use tarfile.next(), which does
+        # sequential access of member files, other than
+        # tarfile.extractfile, which does random access and might
+        # destroy hard disks.
+        tf = tarf.next()
+        while tf != None:
+            if bool(pattern.match(tf.name)):
+                # newline and punctuations removal and ad-hoc tokenization.
+                yield tarf.extractfile(tf).read().rstrip("\n\r").translate(
+                    None, string.punctuation).lower().split()
+            tf = tarf.next()
+
+
+def build_dict(pattern, cutoff):
+    word_freq = {}
+    for doc in tokenize(pattern):
+        for word in doc:
+            paddle.v2.dataset.common.dict_add(word_freq, word)
+
+    # Not sure if we should prune less-frequent words here.
+    word_freq = filter(lambda x: x[1] > cutoff, word_freq.items())
+
+    dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0]))
+    words, _ = list(zip(*dictionary))
+    word_idx = dict(zip(words, xrange(len(words))))
+    word_idx['<unk>'] = len(words)
+    return word_idx
+
+
+def reader_creator(pos_pattern, neg_pattern, word_idx, buffer_size):
+    UNK = word_idx['<unk>']
+
+    qs = [Queue.Queue(maxsize=buffer_size), Queue.Queue(maxsize=buffer_size)]
+
+    def load(pattern, queue):
+        for doc in tokenize(pattern):
+            queue.put(doc)
+        queue.put(None)
+
+    def reader():
+        # Creates two threads that loads positive and negative samples
+        # into qs.
+        t0 = threading.Thread(
+            target=load, args=(
+                pos_pattern,
+                qs[0], ))
+        t0.daemon = True
+        t0.start()
+
+        t1 = threading.Thread(
+            target=load, args=(
+                neg_pattern,
+                qs[1], ))
+        t1.daemon = True
+        t1.start()
+
+        # Read alternatively from qs[0] and qs[1].
+        i = 0
+        doc = qs[i].get()
+        while doc != None:
+            yield [word_idx.get(w, UNK) for w in doc], i % 2
+            i += 1
+            doc = qs[i % 2].get()
+
+        # If any queue is empty, reads from the other queue.
+        i += 1
+        doc = qs[i % 2].get()
+        while doc != None:
+            yield [word_idx.get(w, UNK) for w in doc], i % 2
+            doc = qs[i % 2].get()
+
+    return reader()
+
+
+def train(word_idx):
+    return reader_creator(
+        re.compile("aclImdb/train/pos/.*\.txt$"),
+        re.compile("aclImdb/train/neg/.*\.txt$"), word_idx, 1000)
+
+
+def test(word_idx):
+    return reader_creator(
+        re.compile("aclImdb/test/pos/.*\.txt$"),
+        re.compile("aclImdb/test/neg/.*\.txt$"), word_idx, 1000)

python/paddle/v2/dataset/imikolov.py

+"""
+imikolov's simple dataset: http://www.fit.vutbr.cz/~imikolov/rnnlm/
+"""
+import paddle.v2.dataset.common
+import tarfile
+
+__all__ = ['train', 'test']
+
+URL = 'http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz'
+MD5 = '30177ea32e27c525793142b6bf2c8e2d'
+
+
+def word_count(f, word_freq=None):
+    add = paddle.v2.dataset.common.dict_add
+    if word_freq == None:
+        word_freq = {}
+
+    for l in f:
+        for w in l.strip().split():
+            add(word_freq, w)
+        add(word_freq, '<s>')
+        add(word_freq, '<e>')
+
+    return word_freq
+
+
+def build_dict(train_filename, test_filename):
+    with tarfile.open(
+            paddle.v2.dataset.common.download(
+                paddle.v2.dataset.imikolov.URL, 'imikolov',
+                paddle.v2.dataset.imikolov.MD5)) as tf:
+        trainf = tf.extractfile(train_filename)
+        testf = tf.extractfile(test_filename)
+        word_freq = word_count(testf, word_count(trainf))
+
+        TYPO_FREQ = 50
+        word_freq = filter(lambda x: x[1] > TYPO_FREQ, word_freq.items())
+
+        dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0]))
+        words, _ = list(zip(*dictionary))
+        word_idx = dict(zip(words, xrange(len(words))))
+        word_idx['<unk>'] = len(words)
+
+    return word_idx
+
+
+word_idx = {}
+
+
+def reader_creator(filename, n):
+    global word_idx
+    if len(word_idx) == 0:
+        word_idx = build_dict('./simple-examples/data/ptb.train.txt',
+                              './simple-examples/data/ptb.valid.txt')
+
+    def reader():
+        with tarfile.open(
+                paddle.v2.dataset.common.download(
+                    paddle.v2.dataset.imikolov.URL, 'imikolov',
+                    paddle.v2.dataset.imikolov.MD5)) as tf:
+            f = tf.extractfile(filename)
+
+            UNK = word_idx['<unk>']
+            for l in f:
+                l = ['<s>'] + l.strip().split() + ['<e>']
+                if len(l) >= n:
+                    l = [word_idx.get(w, UNK) for w in l]
+                    for i in range(n, len(l) + 1):
+                        yield tuple(l[i - n:i])
+
+    return reader
+
+
+def train(n):
+    return reader_creator('./simple-examples/data/ptb.train.txt', n)
+
+
+def test(n):
+    return reader_creator('./simple-examples/data/ptb.valid.txt', n)

python/paddle/v2/dataset/mnist.py

@@ -9,9 +9,9 @@ __all__ = ['train', 'test']
 
 URL_PREFIX = 'http://yann.lecun.com/exdb/mnist/'
 TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
-TEST_IMAGE_MD5 = '25e3cc63507ef6e98d5dc541e8672bb6'
+TEST_IMAGE_MD5 = '9fb629c4189551a2d022fa330f9573f3'
 TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
-TEST_LABEL_MD5 = '4e9511fe019b2189026bd0421ba7b688'
+TEST_LABEL_MD5 = 'ec29112dd5afa0611ce80d1b7f02629c'
 TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
 TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873'
 TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'

python/paddle/v2/dataset/tests/imdb_test.py

+import paddle.v2.dataset.imdb
+import unittest
+import re
+
+TRAIN_POS_PATTERN = re.compile("aclImdb/train/pos/.*\.txt$")
+TRAIN_NEG_PATTERN = re.compile("aclImdb/train/neg/.*\.txt$")
+TRAIN_PATTERN = re.compile("aclImdb/train/.*\.txt$")
+
+TEST_POS_PATTERN = re.compile("aclImdb/test/pos/.*\.txt$")
+TEST_NEG_PATTERN = re.compile("aclImdb/test/neg/.*\.txt$")
+TEST_PATTERN = re.compile("aclImdb/test/.*\.txt$")
+
+
+class TestIMDB(unittest.TestCase):
+    word_idx = None
+
+    def test_build_dict(self):
+        if self.word_idx == None:
+            self.word_idx = paddle.v2.dataset.imdb.build_dict(TRAIN_PATTERN,
+                                                              150)
+
+        self.assertEqual(len(self.word_idx), 7036)
+
+    def check_dataset(self, dataset, expected_size):
+        if self.word_idx == None:
+            self.word_idx = paddle.v2.dataset.imdb.build_dict(TRAIN_PATTERN,
+                                                              150)
+
+        sum = 0
+        for l in dataset(self.word_idx):
+            self.assertEqual(l[1], sum % 2)
+            sum += 1
+        self.assertEqual(sum, expected_size)
+
+    def test_train(self):
+        self.check_dataset(paddle.v2.dataset.imdb.train, 25000)
+
+    def test_test(self):
+        self.check_dataset(paddle.v2.dataset.imdb.test, 25000)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/dataset/tests/imikolov_test.py

+import paddle.v2.dataset.imikolov
+import unittest
+
+
+class TestMikolov(unittest.TestCase):
+    def check_reader(self, reader, n):
+        for l in reader():
+            self.assertEqual(len(l), n)
+
+    def test_train(self):
+        n = 5
+        self.check_reader(paddle.v2.dataset.imikolov.train(n), n)
+
+    def test_test(self):
+        n = 5
+        self.check_reader(paddle.v2.dataset.imikolov.test(n), n)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/event.py

@@ -11,7 +11,10 @@ There are:
 TODO(yuyang18): Complete it!
 """
 import py_paddle.swig_paddle as api
-__all__ = ['EndIteration', 'BeginIteration', 'BeginPass', 'EndPass']
+
+__all__ = [
+    'EndIteration', 'BeginIteration', 'BeginPass', 'EndPass', 'TestResult'
+]
 
 
 class WithMetric(object):
@@ -30,6 +33,11 @@ class WithMetric(object):
         return retv
 
 
+class TestResult(WithMetric):
+    def __init__(self, evaluator):
+        super(TestResult, self).__init__(evaluator)
+
+
 class BeginPass(object):
     """
     Event On One Pass Training Start.

python/paddle/v2/topology.py

@@ -21,6 +21,28 @@ import layer as v2_layer
 __all__ = ['Topology']
 
 
+def __flatten__(lis):
+    """
+    Given a list, possibly nested to any level, return it flattened.
+    """
+    new_lis = []
+    for item in lis:
+        if isinstance(item, collections.Sequence):
+            new_lis.extend(__flatten__(item))
+        else:
+            new_lis.append(item)
+    return new_lis
+
+
+def __bfs_travel__(callback, *layers):
+    layers = __flatten__(layers)
+    for each_layer in layers:
+        __break__ = callback(each_layer)
+        if __break__:
+            return
+        __bfs_travel__(callback, *each_layer.__parent_layers__.values())
+
+
 class Topology(object):
     """
     Topology is used to store the information about all layers
@@ -46,21 +68,17 @@ class Topology(object):
         :param name:
         :return:
         """
-        result_layer = []
-
-        def find_layer_by_name(layer, layer_name):
-            if len(result_layer) == 1:
-                return
-            elif layer.name == layer_name:
-                result_layer.append(layer)
-            else:
-                for parent_layer in layer.__parent_layers__.values():
-                    find_layer_by_name(parent_layer, layer_name)
+        result_layer = [None]
 
-        for layer in self.layers:
-            find_layer_by_name(layer, name)
+        def __impl__(l):
+            if l.name == name:
+                result_layer[0] = l
+                return True  # break
+            return False
 
-        assert len(result_layer) == 1
+        __bfs_travel__(__impl__, *self.layers)
+        if result_layer[0] is None:
+            raise ValueError("No such layer %s" % name)
         return result_layer[0]
 
     def data_layers(self):
@@ -70,18 +88,11 @@ class Topology(object):
         """
         data_layers = dict()
 
-        def find_data_layer(layer):
-            if isinstance(layer, v2_layer.DataLayerV2):
-                data_layers[layer.name] = layer
-            if not isinstance(layer, collections.Sequence):
-                layer = [layer]
-            for each_l in layer:
-                for parent_layer in each_l.__parent_layers__.values():
-                    find_data_layer(parent_layer)
-
-        for layer in self.layers:
-            find_data_layer(layer)
+        def __impl__(l):
+            if isinstance(l, v2_layer.DataLayerV2):
+                data_layers[l.name] = l
 
+        __bfs_travel__(__impl__, *self.layers)
         return data_layers
 
     def data_type(self):
@@ -89,12 +100,9 @@ class Topology(object):
         get data_type from proto, such as:
         [('image', dense_vector(768)), ('label', integer_value(10))]
         """
-
-        data_types_lists = []
         data_layers = self.data_layers()
-        for each in self.__model_config__.input_layer_names:
-            data_types_lists.append((each, data_layers[each].type))
-        return data_types_lists
+        return [(nm, data_layers[nm].type)
+                for nm in self.proto().input_layer_names]
 
 
 def __check_layer_type__(layer):

python/paddle/v2/trainer.py

@@ -42,25 +42,35 @@ class ITrainer(object):
 
 
 class SGD(ITrainer):
-    def __init__(self, update_equation):
+    def __init__(self, cost, parameters, update_equation):
         """
         Simple SGD Trainer.
 
         :param update_equation: The optimizer object.
         :type update_equation: v2_optimizer.Optimizer
         """
+
+        if not isinstance(parameters, v2_parameters.Parameters):
+            raise TypeError('parameters should be parameters')
+
         if not isinstance(update_equation, v2_optimizer.Optimizer):
-            raise ValueError("update equation parameter must be "
+            raise TypeError("update equation parameter must be "
                             "paddle.v2.optimizer.Optimizer")
+        topology = Topology(cost)
         self.__optimizer__ = update_equation
+        self.__topology__ = topology
+        self.__parameters__ = parameters
+        self.__topology_in_proto__ = topology.proto()
+        self.__data_types__ = topology.data_type()
+        gm = api.GradientMachine.createFromConfigProto(
+            self.__topology_in_proto__, api.CREATE_MODE_NORMAL,
+            self.__optimizer__.enable_types())
+        assert isinstance(gm, api.GradientMachine)
+        parameters.append_gradient_machine(gm)
+        self.__gradient_machine__ = gm
+        self.__gradient_machine__.randParameters()
 
-    def train(self,
-              reader,
-              cost,
-              parameters,
-              num_passes=1,
-              event_handler=None,
-              reader_dict=None):
+    def train(self, reader, num_passes=1, event_handler=None, reader_dict=None):
         """
         Training method. Will train num_passes of input data.
 
@@ -76,27 +86,22 @@ class SGD(ITrainer):
         if event_handler is None:
             event_handler = default_event_handler
 
-        topology = Topology(cost)
+        if reader_dict is None:
+            reader_dict = self.default_reader_dict()
 
         __check_train_args__(**locals())
 
-        gm = api.GradientMachine.createFromConfigProto(
-            topology.proto(), api.CREATE_MODE_NORMAL,
-            self.__optimizer__.enable_types())
-        assert isinstance(gm, api.GradientMachine)
-        parameters.append_gradient_machine(gm)
-        gm.randParameters()
         updater = self.__optimizer__.create_local_updater()
-        updater.init(gm)
+        updater.init(self.__gradient_machine__)
 
-        gm.start()
-        batch_evaluator = gm.makeEvaluator()
+        self.__gradient_machine__.start()
+        batch_evaluator = self.__gradient_machine__.makeEvaluator()
         assert isinstance(batch_evaluator, api.Evaluator)
-        pass_evaluator = gm.makeEvaluator()
+        pass_evaluator = self.__gradient_machine__.makeEvaluator()
         assert isinstance(pass_evaluator, api.Evaluator)
         out_args = api.Arguments.createArguments(0)
 
-        feeder = DataFeeder(topology.data_type(), reader_dict)
+        feeder = DataFeeder(self.__data_types__, reader_dict)
 
         for pass_id in xrange(num_passes):
             event_handler(v2_event.BeginPass(pass_id))
@@ -104,16 +109,18 @@ class SGD(ITrainer):
             updater.startPass()
             for batch_id, data_batch in enumerate(reader()):
                 pass_type = updater.startBatch(len(data_batch))
-                gm.forwardBackward(feeder(data_batch), out_args, pass_type)
+                self.__gradient_machine__.forwardBackward(
+                    feeder(data_batch), out_args, pass_type)
                 batch_evaluator.start()
                 event_handler(
                     v2_event.BeginIteration(
                         pass_id=pass_id, batch_id=batch_id))
                 pass_type = updater.startBatch(len(data_batch))
-                gm.forwardBackward(feeder(data_batch), out_args, pass_type)
-                gm.eval(pass_evaluator)
-                gm.eval(batch_evaluator)
-                for each_param in gm.getParameters():
+                self.__gradient_machine__.forwardBackward(
+                    feeder(data_batch), out_args, pass_type)
+                self.__gradient_machine__.eval(pass_evaluator)
+                self.__gradient_machine__.eval(batch_evaluator)
+                for each_param in self.__gradient_machine__.getParameters():
                     updater.update(each_param)
                 # Get cost. We use numpy to calculate total cost for this batch.
                 cost_vec = out_args.getSlotValue(0)
@@ -131,22 +138,37 @@ class SGD(ITrainer):
             updater.finishPass()
             pass_evaluator.finish()
             event_handler(v2_event.EndPass(pass_id, evaluator=pass_evaluator))
-        gm.finish()
+        self.__gradient_machine__.finish()
+
+    def default_reader_dict(self):
+        reader_dict = dict()
+        for i, tp in enumerate(self.__data_types__):
+            reader_dict[tp[0]] = i
+        return reader_dict
+
+    def test(self, reader, reader_dict=None):
+        if reader_dict is None:
+            reader_dict = self.default_reader_dict()
+
+        feeder = DataFeeder(self.__data_types__, reader_dict)
+        evaluator = self.__gradient_machine__.makeEvaluator()
+        out_args = api.Arguments.createArguments(0)
+        evaluator.start()
+        for data_batch in reader():
+            self.__gradient_machine__.forward(
+                feeder(data_batch), out_args, api.PASS_TEST)
+            self.__gradient_machine__.eval(evaluator)
 
+        evaluator.finish()
+        return v2_event.TestResult(evaluator=evaluator)
 
-def __check_train_args__(reader, topology, parameters, event_handler, **kwargs):
+
+def __check_train_args__(reader, event_handler, **kwargs):
     """
     Check train function's argument types
     """
     if not callable(reader) or not isinstance(reader(), collections.Iterator):
         raise TypeError('train_data_reader should be a function, '
                         'which can return a iterator')
-
-    if not isinstance(topology, Topology):
-        raise TypeError('topology should be a model config')
-
-    if not isinstance(parameters, v2_parameters.Parameters):
-        raise TypeError('parameters should be a parameter pool')
-
     if not callable(event_handler):
         raise TypeError('event handler should be a function')