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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Example of recurrent neural networks over characters for DBpedia dataset.
This model is similar to one described in this paper:
"Character-level Convolutional Networks for Text Classification"
http://arxiv.org/abs/1509.01626
and is somewhat alternative to the Lua code from here:
https://github.com/zhangxiangxiao/Crepe
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import numpy as np
import pandas
import tensorflow as tf
FLAGS = None
MAX_DOCUMENT_LENGTH = 100
HIDDEN_SIZE = 20
MAX_LABEL = 15
CHARS_FEATURE = 'chars' # Name of the input character feature.
def char_rnn_model(features, labels, mode):
"""Character level recurrent neural network model to predict classes."""
byte_vectors = tf.one_hot(features[CHARS_FEATURE], 256, 1., 0.)
byte_list = tf.unstack(byte_vectors, axis=1)
cell = tf.nn.rnn_cell.GRUCell(HIDDEN_SIZE)
_, encoding = tf.nn.static_rnn(cell, byte_list, dtype=tf.float32)
logits = tf.layers.dense(encoding, MAX_LABEL, activation=None)
predicted_classes = tf.argmax(logits, 1)
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(
mode=mode,
predictions={
'class': predicted_classes,
'prob': tf.nn.softmax(logits)
})
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
eval_metric_ops = {
'accuracy': tf.metrics.accuracy(
labels=labels, predictions=predicted_classes)
}
return tf.estimator.EstimatorSpec(
mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
def main(unused_argv):
# Prepare training and testing data
dbpedia = tf.contrib.learn.datasets.load_dataset(
'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)
x_train = pandas.DataFrame(dbpedia.train.data)[1]
y_train = pandas.Series(dbpedia.train.target)
x_test = pandas.DataFrame(dbpedia.test.data)[1]
y_test = pandas.Series(dbpedia.test.target)
# Process vocabulary
char_processor = tf.contrib.learn.preprocessing.ByteProcessor(
MAX_DOCUMENT_LENGTH)
x_train = np.array(list(char_processor.fit_transform(x_train)))
x_test = np.array(list(char_processor.transform(x_test)))
# Build model
classifier = tf.estimator.Estimator(model_fn=char_rnn_model)
# Train.
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={CHARS_FEATURE: x_train},
y=y_train,
batch_size=128,
num_epochs=None,
shuffle=True)
classifier.train(input_fn=train_input_fn, steps=100)
# Eval.
test_input_fn = tf.estimator.inputs.numpy_input_fn(
x={CHARS_FEATURE: x_test},
y=y_test,
num_epochs=1,
shuffle=False)
scores = classifier.evaluate(input_fn=test_input_fn)
print('Accuracy: {0:f}'.format(scores['accuracy']))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--test_with_fake_data',
default=False,
help='Test the example code with fake data.',
action='store_true')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
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