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June 24, 2016 19:34
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TensorFlow Sequence Labelling
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# Working example for my blog post at: | |
# http://danijar.com/introduction-to-recurrent-networks-in-tensorflow/ | |
import functools | |
import sets | |
import tensorflow as tf | |
from tensorflow.models.rnn import rnn_cell | |
from tensorflow.models.rnn import rnn | |
def lazy_property(function): | |
attribute = '_' + function.__name__ | |
@property | |
@functools.wraps(function) | |
def wrapper(self): | |
if not hasattr(self, attribute): | |
setattr(self, attribute, function(self)) | |
return getattr(self, attribute) | |
return wrapper | |
class SequenceLabelling: | |
def __init__(self, data, target, dropout, num_hidden=200, num_layers=3): | |
self.data = data | |
self.target = target | |
self.dropout = dropout | |
self._num_hidden = num_hidden | |
self._num_layers = num_layers | |
self.prediction | |
self.error | |
self.optimize | |
@lazy_property | |
def prediction(self): | |
# Recurrent network. | |
network = rnn_cell.GRUCell(self._num_hidden) | |
network = rnn_cell.DropoutWrapper( | |
network, output_keep_prob=self.dropout) | |
network = rnn_cell.MultiRNNCell([network] * self._num_layers) | |
output, _ = rnn.dynamic_rnn(network, data, dtype=tf.float32) | |
# Softmax layer. | |
max_length = int(self.target.get_shape()[1]) | |
num_classes = int(self.target.get_shape()[2]) | |
weight, bias = self._weight_and_bias(self._num_hidden, num_classes) | |
# Flatten to apply same weights to all time steps. | |
output = tf.reshape(output, [-1, self._num_hidden]) | |
prediction = tf.nn.softmax(tf.matmul(output, weight) + bias) | |
prediction = tf.reshape(prediction, [-1, max_length, num_classes]) | |
return prediction | |
@lazy_property | |
def cost(self): | |
cross_entropy = -tf.reduce_sum( | |
self.target * tf.log(self.prediction), reduction_indices=1) | |
cross_entropy = tf.reduce_mean(cross_entropy) | |
return cross_entropy | |
@lazy_property | |
def optimize(self): | |
learning_rate = 0.003 | |
optimizer = tf.train.RMSPropOptimizer(learning_rate) | |
return optimizer.minimize(self.cost) | |
@lazy_property | |
def error(self): | |
mistakes = tf.not_equal( | |
tf.argmax(self.target, 2), tf.argmax(self.prediction, 2)) | |
return tf.reduce_mean(tf.cast(mistakes, tf.float32)) | |
@staticmethod | |
def _weight_and_bias(in_size, out_size): | |
weight = tf.truncated_normal([in_size, out_size], stddev=0.01) | |
bias = tf.constant(0.1, shape=[out_size]) | |
return tf.Variable(weight), tf.Variable(bias) | |
def get_dataset(): | |
"""Read dataset and flatten images.""" | |
dataset = sets.Ocr() | |
dataset = sets.OneHot(dataset.target, depth=2)(dataset, columns=['target']) | |
dataset['data'] = dataset.data.reshape( | |
dataset.data.shape[:-2] + (-1,)).astype(float) | |
train, test = sets.Split(0.66)(dataset) | |
return train, test | |
if __name__ == '__main__': | |
train, test = get_dataset() | |
_, length, image_size = train.data.shape | |
num_classes = train.target.shape[2] | |
data = tf.placeholder(tf.float32, [None, length, image_size]) | |
target = tf.placeholder(tf.float32, [None, length, num_classes]) | |
dropout = tf.placeholder(tf.float32) | |
model = SequenceLabelling(data, target, dropout) | |
sess = tf.Session() | |
sess.run(tf.initialize_all_variables()) | |
for epoch in range(10): | |
for _ in range(100): | |
batch = train.sample(10) | |
sess.run(model.optimize, { | |
data: batch.data, target: batch.target, dropout: 0.5}) | |
error = sess.run(model.error, { | |
data: test.data, target: test.target, dropout: 1}) | |
print('Epoch {:2d} error {:3.1f}%'.format(epoch + 1, 100 * error)) |
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