Add demo for fault tolerant label semantic role and machine translation.

.gitignore

@@ -2,3 +2,4 @@
 *.crt
 .cache
 vendor
+*~

demo/label_semantic_roles/train_ft.py

@@ -0,0 +1,229 @@
+import os
+import math
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.dataset.conll05 as conll05
+import paddle.v2.evaluator as evaluator
+from paddle.v2.reader.creator import cloud_reader
+
+master_ip = os.getenv("MASTER_IP")
+etcd_endpoint = "http://" + master_ip + ":" + "2379"
+
+word_dict, verb_dict, label_dict = conll05.get_dict()
+word_dict_len = len(word_dict)
+label_dict_len = len(label_dict)
+pred_len = len(verb_dict)
+
+mark_dict_len = 2
+word_dim = 32
+mark_dim = 5
+hidden_dim = 512
+depth = 8
+default_std = 1 / math.sqrt(hidden_dim) / 3.0
+mix_hidden_lr = 1e-3
+
+
+def d_type(size):
+    return paddle.data_type.integer_value_sequence(size)
+
+
+def db_lstm():
+    #8 features
+    word = paddle.layer.data(name='word_data', type=d_type(word_dict_len))
+    predicate = paddle.layer.data(name='verb_data', type=d_type(pred_len))
+
+    ctx_n2 = paddle.layer.data(name='ctx_n2_data', type=d_type(word_dict_len))
+    ctx_n1 = paddle.layer.data(name='ctx_n1_data', type=d_type(word_dict_len))
+    ctx_0 = paddle.layer.data(name='ctx_0_data', type=d_type(word_dict_len))
+    ctx_p1 = paddle.layer.data(name='ctx_p1_data', type=d_type(word_dict_len))
+    ctx_p2 = paddle.layer.data(name='ctx_p2_data', type=d_type(word_dict_len))
+    mark = paddle.layer.data(name='mark_data', type=d_type(mark_dict_len))
+
+    emb_para = paddle.attr.Param(name='emb', initial_std=0., is_static=True)
+    std_0 = paddle.attr.Param(initial_std=0.)
+    std_default = paddle.attr.Param(initial_std=default_std)
+
+    predicate_embedding = paddle.layer.embedding(
+        size=word_dim,
+        input=predicate,
+        param_attr=paddle.attr.Param(name='vemb', initial_std=default_std))
+    mark_embedding = paddle.layer.embedding(
+        size=mark_dim, input=mark, param_attr=std_0)
+
+    word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
+    emb_layers = [
+        paddle.layer.embedding(size=word_dim, input=x, param_attr=emb_para)
+        for x in word_input
+    ]
+    emb_layers.append(predicate_embedding)
+    emb_layers.append(mark_embedding)
+
+    hidden_0 = paddle.layer.mixed(
+        size=hidden_dim,
+        bias_attr=std_default,
+        input=[
+            paddle.layer.full_matrix_projection(
+                input=emb, param_attr=std_default) for emb in emb_layers
+        ])
+
+    lstm_para_attr = paddle.attr.Param(initial_std=0.0, learning_rate=1.0)
+    hidden_para_attr = paddle.attr.Param(
+        initial_std=default_std, learning_rate=mix_hidden_lr)
+
+    lstm_0 = paddle.layer.lstmemory(
+        input=hidden_0,
+        act=paddle.activation.Relu(),
+        gate_act=paddle.activation.Sigmoid(),
+        state_act=paddle.activation.Sigmoid(),
+        bias_attr=std_0,
+        param_attr=lstm_para_attr)
+
+    #stack L-LSTM and R-LSTM with direct edges
+    input_tmp = [hidden_0, lstm_0]
+
+    for i in range(1, depth):
+        mix_hidden = paddle.layer.mixed(
+            size=hidden_dim,
+            bias_attr=std_default,
+            input=[
+                paddle.layer.full_matrix_projection(
+                    input=input_tmp[0], param_attr=hidden_para_attr),
+                paddle.layer.full_matrix_projection(
+                    input=input_tmp[1], param_attr=lstm_para_attr)
+            ])
+
+        lstm = paddle.layer.lstmemory(
+            input=mix_hidden,
+            act=paddle.activation.Relu(),
+            gate_act=paddle.activation.Sigmoid(),
+            state_act=paddle.activation.Sigmoid(),
+            reverse=((i % 2) == 1),
+            bias_attr=std_0,
+            param_attr=lstm_para_attr)
+
+        input_tmp = [mix_hidden, lstm]
+
+    feature_out = paddle.layer.mixed(
+        size=label_dict_len,
+        bias_attr=std_default,
+        input=[
+            paddle.layer.full_matrix_projection(
+                input=input_tmp[0], param_attr=hidden_para_attr),
+            paddle.layer.full_matrix_projection(
+                input=input_tmp[1], param_attr=lstm_para_attr)
+        ], )
+
+    return feature_out
+
+
+def load_parameter(file_name, h, w):
+    with open(file_name, 'rb') as f:
+        f.read(16)  # skip header.
+        return np.fromfile(f, dtype=np.float32).reshape(h, w)
+
+
+def main():
+    paddle.init()
+
+    # define network topology
+    feature_out = db_lstm()
+    target = paddle.layer.data(name='target', type=d_type(label_dict_len))
+    crf_cost = paddle.layer.crf(
+        size=label_dict_len,
+        input=feature_out,
+        label=target,
+        param_attr=paddle.attr.Param(
+            name='crfw', initial_std=default_std, learning_rate=mix_hidden_lr))
+
+    crf_dec = paddle.layer.crf_decoding(
+        size=label_dict_len,
+        input=feature_out,
+        label=target,
+        param_attr=paddle.attr.Param(name='crfw'))
+    evaluator.sum(input=crf_dec)
+
+    # create parameters
+    parameters = paddle.parameters.create(crf_cost)
+    parameters.set('emb', load_parameter(conll05.get_embedding(), 44068, 32))
+
+    # create optimizer
+    optimizer = paddle.optimizer.Momentum(
+        momentum=0,
+        learning_rate=2e-2,
+        regularization=paddle.optimizer.L2Regularization(rate=8e-4),
+        model_average=paddle.optimizer.ModelAverage(
+            average_window=0.5, max_average_window=10000), )
+
+    trainer = paddle.trainer.SGD(
+        cost=crf_cost,
+        parameters=parameters,
+        update_equation=optimizer,
+        extra_layers=crf_dec)
+
+    reader = paddle.batch(
+        paddle.reader.shuffle(cloud_reader(
+                ["/pfs/dlnel/public/dataset/conll05/conl105_train-*"],
+                etcd_endpoint), buf_size=8192), batch_size=10)
+
+    feeding = {
+        'word_data': 0,
+        'ctx_n2_data': 1,
+        'ctx_n1_data': 2,
+        'ctx_0_data': 3,
+        'ctx_p1_data': 4,
+        'ctx_p2_data': 5,
+        'verb_data': 6,
+        'mark_data': 7,
+        'target': 8
+    }
+
+    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=reader, feeding=feeding)
+                print "\nTest with Pass %d, Batch %d, %s" % (
+                    event.pass_id, event.batch_id, result.metrics)
+
+        if isinstance(event, paddle.event.EndPass):
+            # save parameters
+            with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
+                parameters.to_tar(f)
+
+            result = trainer.test(reader=reader, feeding=feeding)
+            print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
+
+    trainer.train(
+        reader=reader,
+        event_handler=event_handler,
+        num_passes=1,
+        feeding=feeding)
+
+    test_creator = paddle.dataset.conll05.test()
+    test_data = []
+    for item in test_creator():
+        test_data.append(item[0:8])
+        if len(test_data) == 1:
+            break
+
+    predict = paddle.layer.crf_decoding(
+        size=label_dict_len,
+        input=feature_out,
+        param_attr=paddle.attr.Param(name='crfw'))
+    probs = paddle.infer(
+        output_layer=predict,
+        parameters=parameters,
+        input=test_data,
+        field='id')
+    assert len(probs) == len(test_data[0][0])
+    labels_reverse = {}
+    for (k, v) in label_dict.items():
+        labels_reverse[v] = k
+    pre_lab = [labels_reverse[i] for i in probs]
+    print pre_lab
+
+
+if __name__ == '__main__':
+    main()