Yang Yu
7 years ago
parent
311334e083
commit
4fee15e860
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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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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from __future__ import print_function
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import numpy as np
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import paddle.v2 as paddle
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import paddle.v2.fluid as fluid
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def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
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hid_dim=32):
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emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
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conv_3 = fluid.nets.sequence_conv_pool(
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input=emb,
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num_filters=hid_dim,
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filter_size=3,
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act="tanh",
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pool_type="sqrt")
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conv_4 = fluid.nets.sequence_conv_pool(
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input=emb,
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num_filters=hid_dim,
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filter_size=4,
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act="tanh",
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pool_type="sqrt")
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prediction = fluid.layers.fc(input=[conv_3, conv_4],
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size=class_dim,
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act="softmax")
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
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adam_optimizer.minimize(avg_cost)
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accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
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return avg_cost, accuracy, accuracy.metrics[0]
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def to_lodtensor(data, place):
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seq_lens = [len(seq) for seq in data]
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cur_len = 0
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lod = [cur_len]
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for l in seq_lens:
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cur_len += l
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lod.append(cur_len)
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flattened_data = np.concatenate(data, axis=0).astype("int64")
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flattened_data = flattened_data.reshape([len(flattened_data), 1])
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res = fluid.LoDTensor()
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res.set(flattened_data, place)
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res.set_lod([lod])
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return res
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def main():
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BATCH_SIZE = 100
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PASS_NUM = 5
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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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data = fluid.layers.data(
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name="words", shape=[1], dtype="int64", lod_level=1)
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label = fluid.layers.data(name="label", shape=[1], dtype="int64")
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cost, accuracy, acc_out = convolution_net(
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data, label, input_dim=dict_dim, class_dim=class_dim)
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train_data = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.imdb.train(word_dict), buf_size=1000),
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batch_size=BATCH_SIZE)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
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exe.run(fluid.default_startup_program())
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for pass_id in xrange(PASS_NUM):
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accuracy.reset(exe)
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for data in train_data():
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cost_val, acc_val = exe.run(fluid.default_main_program(),
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feed=feeder.feed(data),
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fetch_list=[cost, acc_out])
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pass_acc = accuracy.eval(exe)
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print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
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" pass_acc=" + str(pass_acc))
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if cost_val < 1.0 and pass_acc > 0.8:
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exit(0)
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exit(1)
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if __name__ == '__main__':
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main()
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@ -1,160 +0,0 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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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 numpy as np
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import paddle.v2 as paddle
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import paddle.v2.fluid as fluid
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from paddle.v2.fluid.layer_helper import LayerHelper
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def lstm(x, c_pre_init, hidden_dim, forget_bias=None):
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"""
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This function helps create an operator for the LSTM (Long Short Term
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Memory) cell that can be used inside an RNN.
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"""
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helper = LayerHelper('lstm_unit', **locals())
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rnn = fluid.layers.StaticRNN()
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with rnn.step():
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c_pre = rnn.memory(init=c_pre_init)
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x_t = rnn.step_input(x)
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before_fc = fluid.layers.concat(input=[x_t, c_pre], axis=1)
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after_fc = fluid.layers.fc(input=before_fc, size=hidden_dim * 4)
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dtype = x.dtype
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c = helper.create_tmp_variable(dtype)
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h = helper.create_tmp_variable(dtype)
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helper.append_op(
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type='lstm_unit',
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inputs={"X": after_fc,
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"C_prev": c_pre},
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outputs={"C": c,
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"H": h},
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attrs={"forget_bias": forget_bias})
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rnn.update_memory(c_pre, c)
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rnn.output(h)
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return rnn()
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def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
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data = fluid.layers.data(
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name="words",
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shape=[seq_len * batch_size, 1],
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append_batch_size=False,
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dtype="int64",
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lod_level=1)
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label = fluid.layers.data(
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name="label",
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shape=[batch_size, 1],
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append_batch_size=False,
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dtype="int64")
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emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
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emb = fluid.layers.reshape(x=emb, shape=[batch_size, seq_len, emb_dim])
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emb = fluid.layers.transpose(x=emb, perm=[1, 0, 2])
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c_pre_init = fluid.layers.fill_constant(
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dtype=emb.dtype, shape=[batch_size, emb_dim], value=0.0)
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c_pre_init.stop_gradient = False
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layer_1_out = lstm(emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
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layer_1_out = fluid.layers.transpose(x=layer_1_out, perm=[1, 0, 2])
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prediction = fluid.layers.fc(input=layer_1_out,
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size=class_dim,
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act="softmax")
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
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adam_optimizer.minimize(avg_cost)
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acc = fluid.layers.accuracy(input=prediction, label=label)
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return avg_cost, acc
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def to_lodtensor(data, place):
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seq_lens = [len(seq) for seq in data]
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cur_len = 0
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lod = [cur_len]
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for l in seq_lens:
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cur_len += l
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lod.append(cur_len)
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flattened_data = np.concatenate(data, axis=0).astype("int64")
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flattened_data = flattened_data.reshape([len(flattened_data), 1])
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res = fluid.LoDTensor()
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res.set(flattened_data, place)
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res.set_lod([lod])
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return res
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def chop_data(data, chop_len=80, batch_size=50):
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data = [(x[0][:chop_len], x[1]) for x in data if len(x[0]) >= chop_len]
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return data[:batch_size]
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def prepare_feed_data(data, place):
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tensor_words = to_lodtensor(map(lambda x: x[0], data), place)
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label = np.array(map(lambda x: x[1], data)).astype("int64")
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label = label.reshape([len(label), 1])
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tensor_label = fluid.LoDTensor()
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tensor_label.set(label, place)
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return tensor_words, tensor_label
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def main():
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BATCH_SIZE = 100
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PASS_NUM = 5
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word_dict = paddle.dataset.imdb.word_dict()
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print "load word dict successfully"
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dict_dim = len(word_dict)
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class_dim = 2
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cost, acc = lstm_net(dict_dim=dict_dim, class_dim=class_dim)
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train_data = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.imdb.train(word_dict), buf_size=BATCH_SIZE * 10),
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batch_size=BATCH_SIZE)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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for pass_id in xrange(PASS_NUM):
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for data in train_data():
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chopped_data = chop_data(data)
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tensor_words, tensor_label = prepare_feed_data(chopped_data, place)
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outs = exe.run(fluid.default_main_program(),
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feed={"words": tensor_words,
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"label": tensor_label},
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fetch_list=[cost, acc])
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cost_val = np.array(outs[0])
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acc_val = np.array(outs[1])
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print("cost=" + str(cost_val) + " acc=" + str(acc_val))
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if acc_val > 0.7:
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exit(0)
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exit(1)
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if __name__ == '__main__':
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main()
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