add network configuration and train script

word_embedding/network_conf.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import math
+import paddle.v2 as paddle
+
+
+def network_conf(hidden_size, embed_size, dict_size):
+    def word_embed(in_layer):
+        ''' word embedding layer '''
+        word_embed = paddle.layer.table_projection(
+            input=in_layer,
+            size=embed_size,
+            param_attr=paddle.attr.Param(
+                name="_proj", initial_std=0.001, learning_rate=1, l2_rate=0))
+        return word_embed
+
+    first_word = paddle.layer.data(
+        name='firstw', type=paddle.data_type.integer_value(dict_size))
+    second_word = paddle.layer.data(
+        name='secondw', type=paddle.data_type.integer_value(dict_size))
+    third_word = paddle.layer.data(
+        name='thirdw', type=paddle.data_type.integer_value(dict_size))
+    fourth_word = paddle.layer.data(
+        name='fourthw', type=paddle.data_type.integer_value(dict_size))
+    target_word = paddle.layer.data(
+        name='fifthw', type=paddle.data_type.integer_value(dict_size))
+
+    first_word_embed = word_embed(first_word)
+    second_word_embed = word_embed(second_word)
+    third_word_embed = word_embed(third_word)
+    fourth_word_embed = word_embed(fourth_word)
+
+    context_embed = paddle.layer.concat(input=[
+        first_word_embed, second_word_embed, third_word_embed, fourth_word_embed
+    ])
+
+    hidden_layer = paddle.layer.fc(
+        input=context_embed,
+        size=hidden_size,
+        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(embed_size * 8), learning_rate=1))
+
+    with paddle.layer.mixed(
+            size=dict_size - 1,
+            act=paddle.activation.Sigmoid(),
+            bias_attr=paddle.attr.Param(name='sigmoid_b')) as prediction:
+        prediction += paddle.layer.trans_full_matrix_projection(
+            input=hidden_layer, param_attr=paddle.attr.Param(name='sigmoid_w'))
+
+    cost = paddle.layer.hsigmoid(
+        input=hidden_layer,
+        label=target_word,
+        num_classes=dict_size,
+        param_attr=paddle.attr.Param(name='sigmoid_w'),
+        bias_attr=paddle.attr.Param(name='sigmoid_b'))
+
+    parameters = paddle.parameters.create([cost, prediction])
+
+    adam_optimizer = paddle.optimizer.Adam(
+        learning_rate=3e-3,
+        regularization=paddle.optimizer.L2Regularization(8e-4))
+
+    input_data_lst = ['firstw', 'secondw', 'thirdw', 'fourthw', 'fifthw']
+
+    trainer = paddle.trainer.SGD(cost, parameters, adam_optimizer)
+
+    return input_data_lst, trainer, prediction, parameters

word_embedding/train_v2.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import paddle.v2 as paddle
+from network_conf import network_conf
+import gzip
+
+
+def main():
+    paddle.init(use_gpu=False, trainer_count=1)
+    word_dict = paddle.dataset.imikolov.build_dict()
+    dict_size = len(word_dict)
+    input_data_lst, trainer, _, parameters = network_conf(
+        hidden_size=256, embed_size=32, dict_size=dict_size)
+
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndPass):
+            model_name = './models/model_pass_%05d.tar.gz' % event.pass_id
+            print("Save model into %s ..." % model_name)
+            with gzip.open(model_name, 'w') as f:
+                parameters.to_tar(f)
+
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 100 == 0:
+                result = trainer.test(
+                    paddle.batch(
+                        paddle.dataset.imikolov.test(word_dict, 5), 32))
+                print "Pass %d, Batch %d, Cost %f" % (
+                    event.pass_id, event.batch_id, event.cost)
+
+    feeding = dict(zip(input_data_lst, xrange(len(input_data_lst))))
+
+    trainer.train(
+        paddle.batch(
+            paddle.reader.shuffle(
+                lambda: paddle.dataset.imikolov.train(word_dict, 5)(),
+                buf_size=1000), 64),
+        num_passes=30,
+        event_handler=event_handler,
+        feeding=feeding)
+
+
+if __name__ == '__main__':
+    main()