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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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.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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import paddle.v2.dataset.imdb as imdb
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import paddle.v2 as paddle
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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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data = paddle.layer.data("word",
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paddle.data_type.integer_value_sequence(input_dim))
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emb = paddle.layer.embedding(input=data, size=emb_dim)
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conv_3 = paddle.networks.sequence_conv_pool(
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input=emb, context_len=3, hidden_size=hid_dim)
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conv_4 = paddle.networks.sequence_conv_pool(
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input=emb, context_len=4, hidden_size=hid_dim)
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output = paddle.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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act=paddle.activation.Softmax())
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lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
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cost = paddle.layer.classification_cost(input=output, label=lbl)
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return cost
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@ -152,24 +66,28 @@ def stacked_lstm_net(input_dim,
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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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relu = paddle.activation.Relu()
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linear = paddle.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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data = paddle.layer.data("word",
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paddle.data_type.integer_value_sequence(input_dim))
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emb = paddle.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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fc1 = paddle.layer.fc(input=emb,
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size=hid_dim,
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act=linear,
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bias_attr=bias_attr)
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lstm1 = paddle.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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fc = paddle.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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lstm = paddle.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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@ -177,16 +95,16 @@ def stacked_lstm_net(input_dim,
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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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fc_last = paddle.layer.pooling(input=inputs[0], pooling_type=MaxPooling())
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lstm_last = paddle.layer.pooling(input=inputs[1], pooling_type=MaxPooling())
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output = paddle.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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act=paddle.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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lbl = paddle.layer.data("label", paddle.data_type.integer_value(2))
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cost = paddle.layer.classification_cost(input=output, label=lbl)
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return cost
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@ -196,7 +114,7 @@ if __name__ == '__main__':
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# network config
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print 'load dictionary...'
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word_dict = imdb.word_dict()
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word_dict = paddle.dataset.imdb.word_dict()
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dict_dim = len(word_dict)
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class_dim = 2
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@ -226,7 +144,8 @@ if __name__ == '__main__':
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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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lambda: imdb.test(word_dict), batch_size=128),
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lambda: paddle.dataset.imdb.test(word_dict),
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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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@ -239,7 +158,7 @@ if __name__ == '__main__':
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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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lambda: imdb.train(word_dict), buf_size=1000),
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lambda: paddle.dataset.imdb.train(word_dict), buf_size=1000),
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batch_size=100),
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event_handler=event_handler,
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reader_dict={'word': 0,
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wangkuiyi
8 years ago
committed by
GitHub