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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))

facing problem in line line 107
dataset = sets.Ocr()
AttributeError: 'module' object has no attribute 'Ocr'

mrathi12 commented Sep 1, 2016

I'm unsure as to what we are inputting as data (format of input) and what we are outputting?

pfaucon commented Oct 11, 2016

running this gist (after installing your sets package) I get an error around 40% but I don't understand why. Looking at the ocr data from sets it looks like the input just says gommandin over and over, with a -1 in the column [2] when the sequence repeats, nonetheless the network seems incapable of recognizing this. It seems like the network should have 2 very good chances to solve this problem (once by memorizing gommandin, and once by actually learning the ocr letters) but at least it fails quite spectacularly on my system. I've tried fiddling with the parameters (epoch, learning, dropout etc...) but nothing seems to have a good effect. Any ideas as to what I'm missing or misunderstanding?

YiruS commented Nov 2, 2016

@pfaucon I'm learning the RNN too. From the official tensorflow tutorial, they said "If you want to do optimization to minimize the cross entropy, AND you're softmaxing after your last layer, you should use tf.nn.softmax_cross_entropy_with_logits instead of doing it yourself, because it covers numerically unstable corner cases in the mathematically right way." I think maybe that's the reason for your error.

Owner

danijar commented Nov 5, 2016

@adelsalehali1982 It works for me with the newest TensorFlow and sets versions.

@mrathi12 Take a look at the placeholders to see the input format. It's a tensor of shape batch_size x sequence_length x image_size for the sequences and a tensor of shape batch_size x sequence_length x num_classes for the targets. You can pass nested Python lists or Numpy arrays into the sess.run() call.

@pfaucon The parameters are not tuned at all, but the RNN starts learning and everything runs through:

Epoch  1 error 65.2%
Epoch  2 error 55.6%
Epoch  3 error 50.6%
Epoch  4 error 48.6%
Epoch  5 error 47.0%
Epoch  6 error 44.3%
Epoch  7 error 44.2%
Epoch  8 error 43.4%
Epoch  9 error 42.6%
Epoch 10 error 42.9%

@YiruS I implement the activation and cost manually so it's easier to understand what's going on. But I'd worth pointing out tf.nn.softmax_cross_entropy_with_logits() for performance and numerical stability reasons.

excuse me,i 'm a new hand in tensorflow, thanks for your bolg which helps me a lot. And i just want to know how can i save the model i trained and use it in another job? looking forward to your reply!

wirth6 commented Jan 5, 2017

@YiruS: I find this a bit confusing, as the description of the softmax_cross_entropy_with_logits function states "This op expects unscaled logits, since it performs a softmax on logits internally for efficiency. Do not call this op with the output of softmax, as it will produce incorrect results." and here self.prediction is the output of a softmax, so according to the documentation we shouldn't use this function here.

chhung3 commented Apr 22, 2017

Hi, Thanks very much for the example. It helps me a lot for creating my own training model.
I don't fully understand the setting of weight and bias. Is that only 1 weight and 1 bias are needed even for multiple layers network?

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