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Implementation of truncated backpropagation through time in rnn with tensorflow.
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import tensorflow as tf | |
class testCell(tf.nn.rnn_cell.RNNCell): | |
def __init__(self, input_size=1, state_size=1): | |
self.input_size = input_size | |
self._state_size = state_size | |
@property | |
def state_size(self): | |
return self._state_size | |
@property | |
def output_size(self): | |
return self._state_size | |
def __call__(self, inputs, state, scope=None): | |
scope = scope or type(self).__name__ | |
# It's always a good idea to scope variables in functions lest they | |
# be defined elsewhere! | |
with tf.variable_scope(scope, dtype=tf.float32): | |
# w1 = tf.Variable([3.], name='w1') | |
wx = tf.get_variable('wx',[1],initializer=tf.constant_initializer(3.)) | |
# b1 = tf.Variable([1.], name='b1') | |
bx = tf.get_variable('bx',[1],initializer=tf.constant_initializer(1.)) | |
# wh = tf.Variable([2.], name='w2') | |
wh = tf.get_variable('wh',[1],initializer=tf.constant_initializer(2.)) | |
# b2 = tf.Variable([1.], name='b2') | |
bh = tf.get_variable('bh',[1],initializer=tf.constant_initializer(1.)) | |
output = wx * inputs + bx + wh * state + bh | |
new_state = output | |
return output, new_state | |
def sequence_length(mask): | |
return tf.reduce_sum(mask, axis=1) | |
def main(): | |
with tf.name_scope('test'): | |
inputs = tf.constant([[1,2,3,0] | |
# ,[1,2,3,4] | |
], dtype=tf.float32,name='inputs') | |
inputs = tf.expand_dims(inputs, axis=2) | |
mask = tf.constant([[1,1,1,0] | |
# ,[1,1,1,1] | |
], dtype=tf.int32,name='mask') | |
y = tf.constant([[4,6,8,0] | |
# ,[4,6,8,10] | |
],dtype=tf.float32,name='y') | |
y = tf.expand_dims(y, axis=2) | |
cell = testCell() | |
init_state = cell.zero_state(1, tf.float32) | |
outs = [] | |
states = [] | |
with tf.variable_scope('test_rnn') as scope: | |
for i in xrange(4): | |
pin = tf.slice(inputs, [0, i,0],[-1, 1, -1]) | |
m = tf.slice(mask, [0, i],[-1, 1]) | |
out, init_state = tf.nn.dynamic_rnn(cell, pin, | |
sequence_length=sequence_length(m), | |
initial_state=init_state, | |
dtype=tf.float32) | |
scope.reuse_variables() | |
init_state = tf.stop_gradient(init_state) | |
outs.append(out) | |
states.append(init_state) | |
o = tf.concat(outs, axis=1) | |
print('shape of o:{}'.format(o.shape)) | |
optimizer = tf.train.GradientDescentOptimizer(0.001) | |
loss = tf.reduce_sum(o - y) | |
gradients = optimizer.compute_gradients(loss) | |
grad = [x[0] for x in gradients] | |
vars = [x[1] for x in gradients] | |
with tf.Session() as sess: | |
sess.run(tf.global_variables_initializer()) | |
tf.summary.FileWriter('test', sess.graph) | |
for i in tf.trainable_variables(): | |
print(i.name) | |
output = sess.run([grad, vars, loss, o, outs, states]) | |
for i,x in enumerate(output): | |
print('================') | |
print(x) | |
if __name__ == '__main__': | |
main() | |
'''out: | |
test_rnn/rnn/testCell/wx:0 | |
test_rnn/rnn/testCell/bx:0 | |
test_rnn/rnn/testCell/wh:0 | |
test_rnn/rnn/testCell/bh:0 | |
================gradients wrt wx bx wh bh | |
[array([ 6.], dtype=float32), array([ 3.], dtype=float32), array([ 23.], dtype=float32), array([ 3.], dtype=float32)] | |
================variables:wx bx wh bh | |
[array([ 3.], dtype=float32), array([ 1.], dtype=float32), array([ 2.], dtype=float32), array([ 1.], dtype=float32)] | |
================loss | |
52.0 | |
================outputs at each time step | |
[[[ 5.] | |
[ 18.] | |
[ 47.] | |
[ 0.]]] | |
================hidden uinits for each time step | |
[array([[[ 5.]]], dtype=float32), array([[[ 18.]]], dtype=float32), array([[[ 47.]]], dtype=float32), array([[[ 0.]]], dtype=float32)] | |
================states units for each time step | |
[array([[ 5.]], dtype=float32), array([[ 18.]], dtype=float32), array([[ 47.]], dtype=float32), array([[ 47.]], dtype=float32)] | |
''' |
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