Add w2v demo

demo/word2vec/train.py

@@ -0,0 +1,156 @@
+import math
+import pickle
+import glob
+import os
+import sys
+import paddle.v2 as paddle
+import paddle.v2.dataset.common as common
+
+embsize = 32
+hiddensize = 256
+N = 5
+
+# NOTE: You need to generate and split dataset then put it under your cloud storage.
+#       then you can use different size of embedding.
+
+# NOTE: must change this to your own username on paddlecloud.
+USERNAME = "wuyi05@baidu.com"
+DC = os.getenv("PADDLE_CLOUD_CURRENT_DATACENTER")
+common.DATA_HOME = "/pfs/%s/home/%s" % (DC, USERNAME)
+TRAIN_FILES_PATH = os.path.join(common.DATA_HOME, "imikolov")
+WORD_DICT_PATH = os.path.join(common.DATA_HOME, "imikolov/word_dict.pickle")
+
+TRAINER_ID = int(os.getenv("PADDLE_INIT_TRAINER_ID", "-1"))
+TRAINER_COUNT = int(os.getenv("PADDLE_INIT_NUM_GRADIENT_SERVERS", "-1"))
+
+def prepare_dataset():
+    word_dict = paddle.dataset.imikolov.build_dict()
+    with open(WORD_DICT_PATH, "w") as fn:
+        pickle.dump(word_dict, fn)
+    # convert will also split the dataset by line-count
+    common.convert(TRAIN_FILES_PATH, 
+                   paddle.dataset.imikolov.train(word_dict, N),
+                   1000, "train")
+
+def cluster_reader_recordio(trainer_id, trainer_count):
+    '''
+        read from cloud dataset which is stored as recordio format
+        each trainer will read a subset of files of the whole dataset.
+    '''
+    import recordio
+    def reader():
+        TRAIN_FILES_PATTERN = os.path.join(TRAIN_FILES_PATH, "train-*")
+        file_list = glob.glob(TRAIN_FILES_PATTERN)
+        file_list.sort()
+        my_file_list = []
+        # read files for current trainer_id
+        for idx, f in enumerate(file_list):
+            if idx % trainer_count == trainer_id:
+                my_file_list.append(f)
+        for f in my_file_list:
+            print "processing ", f
+            reader = recordio.reader(f)
+            record_raw = reader.read()
+            while record_raw:
+                yield pickle.loads(record_raw)
+                record_raw = reader.read()
+            reader.close()
+    return reader
+
+def cluster_reader_recordio_from_master(etcd_endpoints):
+    '''
+        call paddle master's RPC to get recordio metadata,
+        then read from cloud storage.
+    '''
+    TRAIN_FILES_PATTERN = os.path.join(TRAIN_FILES_PATH, "train-*")
+    return paddle.reader.creator.cloud_reader(TRAIN_FILES_PATTERN, etcd_endpoints)
+
+
+def wordemb(inlayer):
+    wordemb = paddle.layer.table_projection(
+        input=inlayer,
+        size=embsize,
+        param_attr=paddle.attr.Param(
+            name="_proj",
+            initial_std=0.001,
+            learning_rate=1,
+            l2_rate=0, ))
+    return wordemb
+
+
+def main():
+    paddle.init(use_gpu=False, trainer_count=1)
+    # load dict from cloud file
+    with open(WORD_DICT_PATH) as fn:
+        word_dict = pickle.load(fn)
+    dict_size = len(word_dict)
+    firstword = paddle.layer.data(
+        name="firstw", type=paddle.data_type.integer_value(dict_size))
+    secondword = paddle.layer.data(
+        name="secondw", type=paddle.data_type.integer_value(dict_size))
+    thirdword = paddle.layer.data(
+        name="thirdw", type=paddle.data_type.integer_value(dict_size))
+    fourthword = paddle.layer.data(
+        name="fourthw", type=paddle.data_type.integer_value(dict_size))
+    nextword = paddle.layer.data(
+        name="fifthw", type=paddle.data_type.integer_value(dict_size))
+
+    Efirst = wordemb(firstword)
+    Esecond = wordemb(secondword)
+    Ethird = wordemb(thirdword)
+    Efourth = wordemb(fourthword)
+
+    contextemb = paddle.layer.concat(input=[Efirst, Esecond, Ethird, Efourth])
+    hidden1 = paddle.layer.fc(
+        input=contextemb,
+        size=hiddensize,
+        act=paddle.activation.Sigmoid(),
+        layer_attr=paddle.attr.Extra(drop_rate=0.5),
+        bias_attr=paddle.attr.Param(learning_rate=2),
+        param_attr=paddle.attr.Param(
+            initial_std=1. / math.sqrt(embsize * 8), learning_rate=1))
+    predictword = paddle.layer.fc(
+        input=hidden1,
+        size=dict_size,
+        bias_attr=paddle.attr.Param(learning_rate=2),
+        act=paddle.activation.Softmax())
+
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 100 == 0:
+                result = trainer.test(
+                    paddle.batch(
+                        # NOTE: if you're going to use cluster test files,
+                        #       prepare them on the storage first
+                        paddle.dataset.imikolov.test(word_dict, N), 32))
+                print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics,
+                    result.metrics)
+
+    cost = paddle.layer.classification_cost(input=predictword, label=nextword)
+    parameters = paddle.parameters.create(cost)
+    adam_optimizer = paddle.optimizer.Adam(
+        learning_rate=3e-3,
+        regularization=paddle.optimizer.L2Regularization(8e-4))
+    trainer = paddle.trainer.SGD(cost, parameters, adam_optimizer)
+    trainer.train(
+        # NOTE: use either cluster_reader_recordio or cluster_reader_recordio_from_master
+        paddle.batch(cluster_reader_recordio(TRAINER_ID, TRAINER_COUNT), 32),
+        num_passes=30,
+        event_handler=event_handler)
+
+
+if __name__ == '__main__':
+    usage = "python train.py [prepare|train]"
+    if len(sys.argv) != 2:
+        print usage
+        exit(1)
+
+    if TRAINER_ID == -1 or TRAINER_COUNT == -1:
+        print "no cloud environ found, must run on cloud"
+        exit(1)
+
+    if sys.argv[1] == "prepare":
+        prepare_dataset()
+    elif sys.argv[1] == "train":
+        main()