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@danijar
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TensorFlow Sequence Labelling
# Example for my blog post at:
# http://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
import functools
import sets
import tensorflow as tf
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 = tf.nn.rnn_cell.GRUCell(self._num_hidden)
network = tf.nn.rnn_cell.DropoutWrapper(
network, output_keep_prob=self.dropout)
network = tf.nn.rnn_cell.MultiRNNCell([network] * self._num_layers)
output, _ = tf.nn.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), [1, 2])
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 read_dataset():
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 = read_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))
@rolanvc
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rolanvc commented Aug 6, 2018

@sufertas, What you did is awesome. Thank you!!

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