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# Copyright 2018 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.
# ==============================================================================
"""Tests for Recurrent ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.contrib.recurrent.python.ops import recurrent
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import function
from tensorflow.python.framework import random_seed
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.platform import test as test_lib
from tensorflow.python.platform import tf_logging as logging
_ElmanState = collections.namedtuple('ElmanState', ('h'))
_ElmanTheta = collections.namedtuple('ElmanTheta', ('w', 'b'))
_ElmanInputs = collections.namedtuple('ElmanInputs', ('x'))
# TODO(drpng): add test for max length computation.
class RecurrentTest(test_util.TensorFlowTestCase):
def testBasic(self):
# pylint:disable=invalid-name
_PolyState = collections.namedtuple('PolyState', ('value', 'x_power'))
_PolyTheta = collections.namedtuple('PolyTheta', ('x'))
_PolyInputs = collections.namedtuple('PolyInputs', ('coeff'))
# pylint:enable=invalid-name
def Poly(theta, state, inputs):
next_state = _PolyState(
value=state.value + inputs.coeff * state.x_power,
x_power=state.x_power * theta.x)
return next_state, []
with self.test_session() as sess:
theta = _PolyTheta(x=array_ops.constant(2.0))
state = _PolyState(
value=array_ops.constant(0.0),
x_power=array_ops.constant(1.0))
inputs = _PolyInputs(coeff=array_ops.constant([1., 2., 3.]))
# x = 2
# 1 + 2*x + 3*x^2
ret = recurrent.Recurrent(theta, state, inputs, Poly)
acc, state = sess.run(ret)
self.assertAllClose(acc.value, [1., 5., 17.])
self.assertAllClose(acc.x_power, [2., 4., 8.])
self.assertAllClose(state.value, 17.)
self.assertAllClose(state.x_power, 8.)
y = ret[1].value
dx, d_coeff = gradients_impl.gradients(ys=[y], xs=[theta.x, inputs.coeff])
dx_val, d_coeff_val = sess.run([dx, d_coeff])
# 2 + 6*x
self.assertAllClose(dx_val, 14.)
self.assertAllClose(d_coeff_val, [1., 2., 4.])
# acc = [1, 1+2x, 1+2x+3x^2]
# sum(acc) = 3 + 4x + 3x^2
acc = ret[0].value
dx, d_coeff = gradients_impl.gradients(
ys=[math_ops.reduce_sum(acc)], xs=[theta.x, inputs.coeff])
dx_val, d_coeff_val = sess.run([dx, d_coeff])
# 4 + 6*x
self.assertAllClose(dx_val, 16.)
self.assertAllClose(d_coeff_val, [3., 4., 4.])
@staticmethod
def Rand(shape):
return random_ops.random_uniform(
shape, minval=-0.2, maxval=0.2, dtype=dtypes.float64)
@staticmethod
def Elman(theta, state0, inputs):
h0, w, b, x = state0.h, theta.w, theta.b, inputs.x
xw = math_ops.matmul(array_ops.concat([x, h0], axis=1), w)
h1 = math_ops.sigmoid(xw + b)
state1 = _ElmanState(h=h1)
return (state1, state1)
@staticmethod
def ElmanGrad(theta, state0, inputs, extras, dstate1):
@function.Defun()
def Grad(h0, w, b, x, h1, dh1):
del b
# We hand-roll the gradient for the 2nd half of the cell as a demo.
dxwb = (dh1 * (1 - h1) * h1)
dxw, db = dxwb, math_ops.reduce_sum(dxwb, axis=0)
# Uses tf.gradient for the 1nd half of the cell as a demo.
xw = math_ops.matmul(array_ops.concat([x, h0], axis=1), w)
dh0, dx, dw = gradients_impl.gradients(
ys=[xw], xs=[h0, x, w], grad_ys=[dxw])
return dh0, dx, dw, db
dh0, dx, dw, db = Grad(state0.h, theta.w, theta.b, inputs.x,
extras.h, dstate1.h)
dstate0 = _ElmanState(h=dh0)
dinputs = _ElmanInputs(x=dx)
return (_ElmanTheta(w=dw, b=db), dstate0, dinputs)
@staticmethod
def ElmanOut(state1):
return _ElmanState(x=state1.h)
@staticmethod
def ElmanOutGrad(dout):
return _ElmanState(h=dout.x)
def testElman(self):
for seqlen, use_grad in [(1, False), (1, True), (7, False), (7, True)]:
logging.info('== Elman: seqlen=%s, use_grad=%s', seqlen, use_grad)
self._ParameterizedTestElman(seqlen, use_grad)
def _ParameterizedTestElman(self, seqlen, use_grad):
with self.test_session() as sess:
random_seed.set_random_seed(342462)
batch = 3
dims = 4
theta = _ElmanTheta(w=RecurrentTest.Rand([2 * dims, dims]),
b=RecurrentTest.Rand([dims]))
state0 = _ElmanState(h=RecurrentTest.Rand([batch, dims]))
inputs = _ElmanInputs(x=RecurrentTest.Rand([seqlen, batch, dims]))
# Statically unrolled.
s = state0
out = []
for i in xrange(seqlen):
inp = _ElmanInputs(x=inputs.x[i, :])
s, _ = RecurrentTest.Elman(theta, s, inp)
out += [s.h]
acc0, final0 = array_ops.stack(out), s.h
loss0 = math_ops.reduce_sum(acc0) + math_ops.reduce_sum(final0)
(dw0, db0, dh0, di0) = gradients_impl.gradients(
loss0, [theta.w, theta.b, state0.h, inputs.x])
acc1, final1 = recurrent.Recurrent(
theta=theta,
state0=state0,
inputs=inputs,
cell_fn=RecurrentTest.Elman,
cell_grad=RecurrentTest.ElmanGrad if use_grad else None)
assert isinstance(acc1, _ElmanState)
assert isinstance(final1, _ElmanState)
acc1, final1 = acc1.h, final1.h
loss1 = math_ops.reduce_sum(acc1) + math_ops.reduce_sum(final1)
(dw1, db1, dh1, di1) = gradients_impl.gradients(
loss1, [theta.w, theta.b, state0.h, inputs.x])
# Fetches a few values and compare them.
(acc0, acc1, final0, final1, dw0, dw1, db0, db1, dh0, dh1, di0,
di1) = sess.run(
[acc0, acc1, final0, final1, dw0, dw1, db0, db1, dh0, dh1, di0, di1])
self.assertAllClose(acc0, acc1)
self.assertAllClose(final0, final1)
self.assertAllClose(dw0, dw1)
self.assertAllClose(db0, db1)
self.assertAllClose(dh0, dh1)
self.assertAllClose(di0, di1)
if __name__ == '__main__':
test_lib.main()
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