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268 lines
10 KiB
268 lines
10 KiB
import sys
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from os.path import join as join_path
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import paddle.trainer_config_helpers.attrs as attrs
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from paddle.trainer_config_helpers.poolings import MaxPooling
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import paddle.v2 as paddle
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import paddle.v2.layer as layer
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import paddle.v2.activation as activation
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import paddle.v2.data_type as data_type
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def sequence_conv_pool(input,
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input_size,
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context_len,
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hidden_size,
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name=None,
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context_start=None,
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pool_type=None,
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context_proj_layer_name=None,
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context_proj_param_attr=False,
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fc_layer_name=None,
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fc_param_attr=None,
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fc_bias_attr=None,
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fc_act=None,
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pool_bias_attr=None,
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fc_attr=None,
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context_attr=None,
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pool_attr=None):
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"""
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Text convolution pooling layers helper.
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Text input => Context Projection => FC Layer => Pooling => Output.
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:param name: name of output layer(pooling layer name)
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:type name: basestring
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:param input: name of input layer
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:type input: LayerOutput
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:param context_len: context projection length. See
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context_projection's document.
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:type context_len: int
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:param hidden_size: FC Layer size.
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:type hidden_size: int
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:param context_start: context projection length. See
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context_projection's context_start.
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:type context_start: int or None
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:param pool_type: pooling layer type. See pooling_layer's document.
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:type pool_type: BasePoolingType.
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:param context_proj_layer_name: context projection layer name.
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None if user don't care.
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:type context_proj_layer_name: basestring
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:param context_proj_param_attr: context projection parameter attribute.
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None if user don't care.
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:type context_proj_param_attr: ParameterAttribute or None.
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:param fc_layer_name: fc layer name. None if user don't care.
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:type fc_layer_name: basestring
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:param fc_param_attr: fc layer parameter attribute. None if user don't care.
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:type fc_param_attr: ParameterAttribute or None
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:param fc_bias_attr: fc bias parameter attribute. False if no bias,
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None if user don't care.
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:type fc_bias_attr: ParameterAttribute or None
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:param fc_act: fc layer activation type. None means tanh
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:type fc_act: BaseActivation
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:param pool_bias_attr: pooling layer bias attr. None if don't care.
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False if no bias.
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:type pool_bias_attr: ParameterAttribute or None.
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:param fc_attr: fc layer extra attribute.
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:type fc_attr: ExtraLayerAttribute
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:param context_attr: context projection layer extra attribute.
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:type context_attr: ExtraLayerAttribute
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:param pool_attr: pooling layer extra attribute.
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:type pool_attr: ExtraLayerAttribute
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:return: output layer name.
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:rtype: LayerOutput
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"""
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# Set Default Value to param
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context_proj_layer_name = "%s_conv_proj" % name \
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if context_proj_layer_name is None else context_proj_layer_name
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with layer.mixed(
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name=context_proj_layer_name,
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size=input_size * context_len,
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act=activation.Linear(),
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layer_attr=context_attr) as m:
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m += layer.context_projection(
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input=input,
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context_len=context_len,
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context_start=context_start,
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padding_attr=context_proj_param_attr)
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fc_layer_name = "%s_conv_fc" % name \
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if fc_layer_name is None else fc_layer_name
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fl = layer.fc(name=fc_layer_name,
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input=m,
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size=hidden_size,
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act=fc_act,
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layer_attr=fc_attr,
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param_attr=fc_param_attr,
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bias_attr=fc_bias_attr)
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return layer.pooling(
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name=name,
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input=fl,
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pooling_type=pool_type,
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bias_attr=pool_bias_attr,
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layer_attr=pool_attr)
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def convolution_net(input_dim,
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class_dim=2,
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emb_dim=128,
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hid_dim=128,
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is_predict=False):
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data = layer.data("word", data_type.integer_value_sequence(input_dim))
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emb = layer.embedding(input=data, size=emb_dim)
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conv_3 = sequence_conv_pool(
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input=emb, input_size=emb_dim, context_len=3, hidden_size=hid_dim)
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conv_4 = sequence_conv_pool(
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input=emb, input_size=emb_dim, context_len=4, hidden_size=hid_dim)
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output = layer.fc(input=[conv_3, conv_4],
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size=class_dim,
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act=activation.Softmax())
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lbl = layer.data("label", data_type.integer_value(2))
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cost = layer.classification_cost(input=output, label=lbl)
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return cost
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def stacked_lstm_net(input_dim,
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class_dim=2,
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emb_dim=128,
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hid_dim=512,
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stacked_num=3,
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is_predict=False):
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"""
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A Wrapper for sentiment classification task.
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This network uses bi-directional recurrent network,
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consisting three LSTM layers. This configure is referred to
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the paper as following url, but use fewer layrs.
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http://www.aclweb.org/anthology/P15-1109
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input_dim: here is word dictionary dimension.
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class_dim: number of categories.
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emb_dim: dimension of word embedding.
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hid_dim: dimension of hidden layer.
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stacked_num: number of stacked lstm-hidden layer.
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is_predict: is predicting or not.
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Some layers is not needed in network when predicting.
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"""
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assert stacked_num % 2 == 1
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layer_attr = attrs.ExtraLayerAttribute(drop_rate=0.5)
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fc_para_attr = attrs.ParameterAttribute(learning_rate=1e-3)
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lstm_para_attr = attrs.ParameterAttribute(initial_std=0., learning_rate=1.)
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para_attr = [fc_para_attr, lstm_para_attr]
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bias_attr = attrs.ParameterAttribute(initial_std=0., l2_rate=0.)
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relu = activation.Relu()
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linear = activation.Linear()
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data = layer.data("word", data_type.integer_value_sequence(input_dim))
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emb = layer.embedding(input=data, size=emb_dim)
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fc1 = layer.fc(input=emb, size=hid_dim, act=linear, bias_attr=bias_attr)
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lstm1 = layer.lstmemory(
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input=fc1, act=relu, bias_attr=bias_attr, layer_attr=layer_attr)
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inputs = [fc1, lstm1]
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for i in range(2, stacked_num + 1):
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fc = layer.fc(input=inputs,
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size=hid_dim,
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act=linear,
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param_attr=para_attr,
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bias_attr=bias_attr)
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lstm = layer.lstmemory(
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input=fc,
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reverse=(i % 2) == 0,
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act=relu,
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bias_attr=bias_attr,
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layer_attr=layer_attr)
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inputs = [fc, lstm]
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fc_last = layer.pooling(input=inputs[0], pooling_type=MaxPooling())
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lstm_last = layer.pooling(input=inputs[1], pooling_type=MaxPooling())
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output = layer.fc(input=[fc_last, lstm_last],
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size=class_dim,
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act=activation.Softmax(),
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bias_attr=bias_attr,
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param_attr=para_attr)
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lbl = layer.data("label", data_type.integer_value(2))
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cost = layer.classification_cost(input=output, label=lbl)
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return cost
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def data_reader(data_file, dict_file):
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def reader():
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with open(dict_file, 'r') as fdict, open(data_file, 'r') as fdata:
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dictionary = dict()
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for i, line in enumerate(fdict):
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dictionary[line.split('\t')[0]] = i
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for line_count, line in enumerate(fdata):
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label, comment = line.strip().split('\t\t')
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label = int(label)
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words = comment.split()
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word_slot = [dictionary[w] for w in words if w in dictionary]
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yield (word_slot, label)
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return reader
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if __name__ == '__main__':
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# data file
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train_file = "./data/pre-imdb/train_part_000"
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test_file = "./data/pre-imdb/test_part_000"
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dict_file = "./data/pre-imdb/dict.txt"
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labels = "./data/pre-imdb/labels.list"
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# init
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paddle.init(use_gpu=True, trainer_count=4)
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# network config
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dict_dim = len(open(dict_file).readlines())
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class_dim = len(open(labels).readlines())
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# Please choose the way to build the network
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# by uncommenting the corresponding line.
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cost = convolution_net(dict_dim, class_dim=class_dim)
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# cost = stacked_lstm_net(dict_dim, class_dim=class_dim, stacked_num=3)
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# create parameters
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parameters = paddle.parameters.create(cost)
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# create optimizer
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adam_optimizer = paddle.optimizer.Adam(
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learning_rate=2e-3,
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regularization=paddle.optimizer.L2Regularization(rate=8e-4),
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model_average=paddle.optimizer.ModelAverage(average_window=0.5))
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# End batch and end pass event handler
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def event_handler(event):
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if isinstance(event, paddle.event.EndIteration):
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if event.batch_id % 100 == 0:
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print "\nPass %d, Batch %d, Cost %f, %s" % (
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event.pass_id, event.batch_id, event.cost, event.metrics)
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else:
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sys.stdout.write('.')
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sys.stdout.flush()
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if isinstance(event, paddle.event.EndPass):
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result = trainer.test(
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reader=paddle.reader.batched(
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data_reader(test_file, dict_file), batch_size=128),
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reader_dict={'word': 0,
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'label': 1})
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print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
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# create trainer
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trainer = paddle.trainer.SGD(cost=cost,
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parameters=parameters,
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update_equation=adam_optimizer)
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trainer.train(
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reader=paddle.reader.batched(
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paddle.reader.shuffle(
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data_reader(train_file, dict_file), buf_size=4096),
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batch_size=128),
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event_handler=event_handler,
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reader_dict={'word': 0,
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'label': 1},
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num_passes=10)
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