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# Copyright 2015 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.
# ==============================================================================
"""Functional tests for coefficient-wise operations.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import gen_math_ops
_ADD = lambda x, y: x + y
_SUB = lambda x, y: x - y
_MUL = lambda x, y: x * y
_POW = lambda x, y: x ** y
_TRUEDIV = lambda x, y: x / y
_FLOORDIV = lambda x, y: x // y
_MOD = lambda x, y: x % y
_NEG = lambda x: -x
_ABS = abs
_LT = lambda x, y: x < y
_LE = lambda x, y: x <= y
_GT = lambda x, y: x > y
_GE = lambda x, y: x >= y
_AND = lambda x, y: x & y
_OR = lambda x, y: x | y
_XOR = lambda x, y: x ^ y
_INV = lambda x: ~x
# TODO(zongheng): it'd be great to factor out this function and various random
# SparseTensor gen funcs.
def _sparsify(x, thresh=0.5, index_dtype=np.int64):
x[x < thresh] = 0
non_zero = np.where(x)
x_indices = np.vstack(non_zero).astype(index_dtype).T
x_values = x[non_zero]
x_shape = x.shape
return tf.SparseTensor(
indices=x_indices, values=x_values, shape=x_shape), x_values
class UnaryOpTest(tf.test.TestCase):
def _compareCpu(self, x, np_func, tf_func):
np_ans = np_func(x)
with self.test_session(use_gpu=False):
inx = tf.convert_to_tensor(x)
if x.dtype in (np.float32, np.float64):
y = 1.1 * tf_func(inx)
np_ans *= 1.1
else:
y = tf_func(inx)
tf_cpu = y.eval()
self.assertShapeEqual(np_ans, y)
if x.dtype == np.float16:
self.assertAllClose(np_ans, tf_cpu, rtol=1e-3, atol=1e-3)
else:
self.assertAllClose(np_ans, tf_cpu)
if x.dtype in (np.complex64, np.complex128) and tf_func == tf.sign:
return # Return early
if x.dtype == np.float16:
s = list(np.shape(x))
jacob_t, _ = tf.test.compute_gradient(inx,
s,
y,
s,
x_init_value=x)
xf = x.astype(np.float)
inxf = tf.convert_to_tensor(xf)
yf = tf_func(inxf)
_, jacob_n = tf.test.compute_gradient(inxf,
s,
yf,
s,
x_init_value=xf)
jacob_n = jacob_n.astype(np.float16)
self.assertAllClose(jacob_t, jacob_n, rtol=5e-3, atol=5e-3)
elif x.dtype in (np.float32, np.complex64):
s = list(np.shape(x))
jacob_t, jacob_n = tf.test.compute_gradient(inx,
s,
y,
s,
x_init_value=x)
self.assertAllClose(jacob_t, jacob_n, rtol=1e-3, atol=1e-3)
elif x.dtype in (np.float64, np.complex128):
s = list(np.shape(x))
jacob_t, jacob_n = tf.test.compute_gradient(inx,
s,
y,
s,
x_init_value=x)
self.assertAllClose(jacob_t, jacob_n, rtol=1e-5, atol=1e-5)
def _check(self, result_tensor, result_np, input_sp_t, tol):
self.assertTrue(isinstance(result_tensor, tf.SparseTensor))
self.assertTrue(isinstance(input_sp_t, tf.SparseTensor))
self.assertAllEqual(input_sp_t.indices.eval(), result_tensor.indices.eval())
self.assertAllEqual(input_sp_t.shape.eval(), result_tensor.shape.eval())
if tol is None:
self.assertAllClose(result_np, result_tensor.values.eval())
else:
self.assertAllClose(result_np, result_tensor.values.eval(), rtol=tol,
atol=tol)
def _compareSparseCpu(self, x, np_func, tf_func, tol):
x_sp, x_sp_vals = _sparsify(x)
res_np = np_func(x_sp_vals)
with self.test_session(use_gpu=False):
self._check(tf_func(x_sp), res_np, x_sp, tol)
def _compareGpu(self, x, np_func, tf_func):
np_ans = np_func(x)
with self.test_session(use_gpu=True):
result = tf_func(tf.convert_to_tensor(x))
tf_gpu = result.eval()
if x.dtype == np.float16:
self.assertAllClose(np_ans, tf_gpu, rtol=1e-3, atol=1e-3)
else:
self.assertAllClose(np_ans, tf_gpu)
# TODO(zhifengc/ke): make gradient checker work on GPU.
def _compareSparseGpu(self, x, np_func, tf_func, tol):
x_sp, x_sp_vals = _sparsify(x)
res_np = np_func(x_sp_vals)
with self.test_session(use_gpu=True):
self._check(tf_func(x_sp), res_np, x_sp, tol)
def _compareBoth(self, x, np_func, tf_func):
self._compareCpu(x, np_func, tf_func)
self._compareGpu(x, np_func, tf_func)
def _compareBothSparse(self, x, np_func, tf_func, tol=None):
self._compareSparseCpu(x, np_func, tf_func, tol)
self._compareSparseGpu(x, np_func, tf_func, tol)
def _inv(self, x):
return 1.0 / x
def _rsqrt(self, x):
return self._inv(np.sqrt(x))
def _sigmoid(self, x):
return 1.0 / (1.0 + np.exp(-x))
def _replace_domain_error_with_inf(self, fn):
def func(x):
try:
return fn(x)
except ValueError as e:
if "domain error" in str(e):
return np.inf * np.ones_like(x)
else:
raise e
return func
def testFloatBasic(self):
x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float32)
y = (x + .5).astype(np.float32) # no zero
z = (x + 15.5).astype(np.float32) # all positive
k = np.arange(-0.90, 0.90, 0.25).astype(np.float32) # between -1 and 1
self._compareBoth(x, np.abs, tf.abs)
self._compareBoth(x, np.abs, _ABS)
self._compareBoth(x, np.negative, tf.neg)
self._compareBoth(x, np.negative, _NEG)
self._compareBoth(y, self._inv, tf.reciprocal)
self._compareBoth(x, np.square, tf.square)
self._compareBoth(z, np.sqrt, tf.sqrt)
self._compareBoth(z, self._rsqrt, tf.rsqrt)
self._compareBoth(x, np.exp, tf.exp)
self._compareBoth(z, np.log, tf.log)
self._compareBoth(z, np.log1p, tf.log1p)
self._compareBoth(x, np.tanh, tf.tanh)
self._compareBoth(x, self._sigmoid, tf.sigmoid)
self._compareBoth(y, np.sign, tf.sign)
self._compareBoth(x, np.sin, tf.sin)
self._compareBoth(x, np.cos, tf.cos)
self._compareBoth(k, np.arcsin, tf.asin)
self._compareBoth(k, np.arccos, tf.acos)
self._compareBoth(x, np.arctan, tf.atan)
self._compareBoth(x, np.tan, tf.tan)
self._compareBoth(
y,
np.vectorize(self._replace_domain_error_with_inf(math.lgamma)),
tf.lgamma)
self._compareBoth(x, np.vectorize(math.erf), tf.erf)
self._compareBoth(x, np.vectorize(math.erfc), tf.erfc)
self._compareBothSparse(x, np.abs, tf.abs)
self._compareBothSparse(x, np.negative, tf.neg)
self._compareBothSparse(x, np.square, tf.square)
self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3)
self._compareBothSparse(x, np.tanh, tf.tanh)
self._compareBothSparse(y, np.sign, tf.sign)
self._compareBothSparse(x, np.vectorize(math.erf), tf.erf)
def testFloatTanhEdge(self):
x = np.arange(40, 40 + 6).reshape(6).astype(np.float32)
self._compareBoth(x, np.tanh, tf.tanh)
x = np.arange(-40, -40 + 6).reshape(6).astype(np.float32)
self._compareBoth(x, np.tanh, tf.tanh)
def testFloatEmpty(self):
x = np.empty((2, 0, 5), dtype=np.float32)
self._compareBoth(x, np.abs, tf.abs)
self._compareBoth(x, np.abs, _ABS)
self._compareBoth(x, np.negative, tf.neg)
self._compareBoth(x, np.negative, _NEG)
self._compareBoth(x, self._inv, tf.reciprocal)
self._compareBoth(x, np.square, tf.square)
self._compareBoth(x, np.sqrt, tf.sqrt)
self._compareBoth(x, self._rsqrt, tf.rsqrt)
self._compareBoth(x, np.exp, tf.exp)
self._compareBoth(x, np.log, tf.log)
self._compareBoth(x, np.log1p, tf.log1p)
self._compareBoth(x, np.tanh, tf.tanh)
self._compareBoth(x, self._sigmoid, tf.sigmoid)
self._compareBoth(x, np.sign, tf.sign)
self._compareBoth(x, np.sin, tf.sin)
self._compareBoth(x, np.cos, tf.cos)
# Can't use vectorize below, so just use some arbitrary function
self._compareBoth(x, np.sign, tf.lgamma)
self._compareBoth(x, np.sign, tf.erf)
self._compareBoth(x, np.sign, tf.erfc)
self._compareBoth(x, np.tan, tf.tan)
self._compareBoth(x, np.arcsin, tf.asin)
self._compareBoth(x, np.arccos, tf.acos)
self._compareBoth(x, np.arctan, tf.atan)
self._compareBothSparse(x, np.abs, tf.abs)
self._compareBothSparse(x, np.negative, tf.neg)
self._compareBothSparse(x, np.square, tf.square)
self._compareBothSparse(x, np.sqrt, tf.sqrt, tol=1e-3)
self._compareBothSparse(x, np.tanh, tf.tanh)
self._compareBothSparse(x, np.sign, tf.sign)
self._compareBothSparse(x, np.sign, tf.erf)
def testDoubleBasic(self):
x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float64)
y = (x + .5).astype(np.float64) # no zero
z = (x + 15.5).astype(np.float64) # all positive
k = np.arange(-0.90, 0.90, 0.35).reshape(1, 3, 2).astype(np.float64) # between -1 and 1
self._compareBoth(x, np.abs, tf.abs)
self._compareBoth(x, np.abs, _ABS)
self._compareBoth(x, np.negative, tf.neg)
self._compareBoth(x, np.negative, _NEG)
self._compareBoth(y, self._inv, tf.reciprocal)
self._compareBoth(x, np.square, tf.square)
self._compareBoth(z, np.sqrt, tf.sqrt)
self._compareBoth(z, self._rsqrt, tf.rsqrt)
self._compareBoth(x, np.exp, tf.exp)
self._compareBoth(z, np.log, tf.log)
self._compareBoth(z, np.log1p, tf.log1p)
self._compareBoth(x, np.tanh, tf.tanh)
self._compareBoth(x, self._sigmoid, tf.sigmoid)
self._compareBoth(y, np.sign, tf.sign)
self._compareBoth(x, np.sin, tf.sin)
self._compareBoth(x, np.cos, tf.cos)
self._compareBoth(
y,
np.vectorize(self._replace_domain_error_with_inf(math.lgamma)),
tf.lgamma)
self._compareBoth(x, np.vectorize(math.erf), tf.erf)
self._compareBoth(x, np.vectorize(math.erfc), tf.erfc)
self._compareBoth(x, np.arctan, tf.atan)
self._compareBoth(k, np.arcsin, tf.asin)
self._compareBoth(k, np.arccos, tf.acos)
self._compareBoth(k, np.tan, tf.tan)
self._compareBothSparse(x, np.abs, tf.abs)
self._compareBothSparse(x, np.negative, tf.neg)
self._compareBothSparse(x, np.square, tf.square)
self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3)
self._compareBothSparse(x, np.tanh, tf.tanh)
self._compareBothSparse(y, np.sign, tf.sign)
self._compareBothSparse(x, np.vectorize(math.erf), tf.erf)
def testHalfBasic(self):
x = np.arange(-3, 3).reshape(1, 3, 2).astype(np.float16)
y = (x + .5).astype(np.float16) # no zero
z = (x + 15.5).astype(np.float16) # all positive
self._compareBoth(x, np.abs, tf.abs)
self._compareBoth(x, np.abs, _ABS)
self._compareBoth(x, np.negative, tf.neg)
self._compareBoth(x, np.negative, _NEG)
self._compareBoth(y, self._inv, tf.reciprocal)
self._compareBoth(x, np.square, tf.square)
self._compareBoth(z, np.sqrt, tf.sqrt)
self._compareBoth(z, self._rsqrt, tf.rsqrt)
self._compareBoth(x, np.exp, tf.exp)
self._compareBoth(z, np.log, tf.log)
self._compareBoth(z, np.log1p, tf.log1p)
self._compareBoth(x, np.tanh, tf.tanh)
self._compareBoth(x, self._sigmoid, tf.sigmoid)
self._compareBoth(y, np.sign, tf.sign)
self._compareBoth(x, np.sin, tf.sin)
self._compareBoth(x, np.cos, tf.cos)
self._compareBoth(
y,
np.vectorize(self._replace_domain_error_with_inf(math.lgamma)),
tf.lgamma)
self._compareBoth(x, np.vectorize(math.erf), tf.erf)
self._compareBoth(x, np.vectorize(math.erfc), tf.erfc)
self._compareBothSparse(x, np.abs, tf.abs)
self._compareBothSparse(x, np.negative, tf.neg)
self._compareBothSparse(x, np.square, tf.square)
self._compareBothSparse(z, np.sqrt, tf.sqrt, tol=1e-3)
self._compareBothSparse(x, np.tanh, tf.tanh)
self._compareBothSparse(y, np.sign, tf.sign)
self._compareBothSparse(x, np.vectorize(math.erf), tf.erf, tol=1e-3)
def testInt32Basic(self):
x = np.arange(-6, 6, 2).reshape(1, 3, 2).astype(np.int32)
self._compareCpu(x, np.abs, tf.abs)
self._compareCpu(x, np.abs, _ABS)
self._compareBoth(x, np.negative, tf.neg)
self._compareBoth(x, np.negative, _NEG)
self._compareBoth(x, np.square, tf.square)
self._compareCpu(x, np.sign, tf.sign)
self._compareBothSparse(x, np.abs, tf.abs)
self._compareBothSparse(x, np.negative, tf.neg)
self._compareBothSparse(x, np.square, tf.square)
self._compareBothSparse(x, np.sign, tf.sign)
def testInt64Basic(self):
x = np.arange(
-6 << 40, 6 << 40, 2 << 40).reshape(1, 3, 2).astype(np.int64)
self._compareCpu(x, np.abs, tf.abs)
self._compareCpu(x, np.abs, _ABS)
self._compareCpu(x, np.negative, tf.neg)
self._compareCpu(x, np.negative, _NEG)
self._compareCpu(x, np.square, tf.square)
self._compareCpu(x, np.sign, tf.sign)
self._compareBothSparse(x, np.abs, tf.abs)
self._compareBothSparse(x, np.negative, tf.neg)
self._compareBothSparse(x, np.square, tf.square)
self._compareBothSparse(x, np.sign, tf.sign)
def testComplex64Basic(self):
x = np.complex(1, 1) * np.arange(-3, 3).reshape(1, 3, 2).astype(
np.complex64)
y = x + 0.5 # no zeros
self._compareCpu(x, np.abs, tf.complex_abs)
self._compareCpu(x, np.abs, _ABS)
self._compareCpu(x, np.negative, tf.neg)
self._compareCpu(x, np.negative, _NEG)
self._compareCpu(y, self._inv, tf.reciprocal)
self._compareCpu(x, np.square, tf.square)
self._compareCpu(y, np.sqrt, tf.sqrt)
self._compareCpu(y, self._rsqrt, tf.rsqrt)
self._compareCpu(x, np.exp, tf.exp)
self._compareCpu(y, np.log, tf.log)
self._compareCpu(y, np.log1p, tf.log1p)
self._compareCpu(x, np.tanh, tf.tanh)
self._compareCpu(x, self._sigmoid, tf.sigmoid)
self._compareCpu(x, np.sin, tf.sin)
self._compareCpu(x, np.cos, tf.cos)
self._compareBothSparse(x, np.abs, tf.abs)
self._compareBothSparse(x, np.negative, tf.neg)
self._compareBothSparse(x, np.square, tf.square)
self._compareBothSparse(x, np.sqrt, tf.sqrt, 1e-3)
self._compareBothSparse(x, np.tanh, tf.tanh)
# Numpy uses an incorrect definition of sign; use the right one instead.
def complex_sign(x):
return x / np.abs(x)
self._compareCpu(y, complex_sign, tf.sign)
self._compareBothSparse(y, complex_sign, tf.sign)
def testComplex128Basic(self):
x = np.complex(1, 1) * np.arange(-3, 3).reshape(1, 3, 2).astype(
np.complex128)
y = x + 0.5 # no zeros
self._compareCpu(x, np.abs, tf.abs)
self._compareCpu(x, np.abs, _ABS)
self._compareCpu(x, np.negative, tf.neg)
self._compareCpu(x, np.negative, _NEG)
self._compareCpu(y, self._inv, tf.reciprocal)
self._compareCpu(x, np.square, tf.square)
self._compareCpu(y, np.sqrt, tf.sqrt)
self._compareCpu(y, self._rsqrt, tf.rsqrt)
self._compareCpu(x, np.exp, tf.exp)
self._compareCpu(y, np.log, tf.log)
self._compareCpu(y, np.log1p, tf.log1p)
self._compareCpu(x, np.tanh, tf.tanh)
self._compareCpu(x, self._sigmoid, tf.sigmoid)
self._compareCpu(x, np.sin, tf.sin)
self._compareCpu(x, np.cos, tf.cos)
self._compareBothSparse(x, np.abs, tf.abs)
self._compareBothSparse(x, np.negative, tf.neg)
self._compareBothSparse(x, np.square, tf.square)
self._compareBothSparse(x, np.sqrt, tf.sqrt, 1e-3)
self._compareBothSparse(x, np.tanh, tf.tanh)
# Numpy uses an incorrect definition of sign; use the right one instead.
def complex_sign(x):
return x / np.abs(x)
self._compareCpu(y, complex_sign, tf.sign)
self._compareBothSparse(y, complex_sign, tf.sign)
def testGradGrad(self):
np.random.seed(7)
shape = (5,)
dtype_tols = [(np.float32, 5e-4), (np.float64, 1e-6), (np.complex64, 5e-4),
(np.complex128, 1e-6)]
op_range = [(gen_math_ops._reciprocal_grad, [-2, 2]),
(gen_math_ops._rsqrt_grad, [0.1, 3]),
(gen_math_ops._sigmoid_grad, [-2, 2]),
(gen_math_ops._sqrt_grad, [0.1, 3]),
(gen_math_ops._tanh_grad, [-2, 2]),]
def rand(dtype):
x = np.random.uniform(
real_range[0], real_range[1], size=shape[0]).astype(dtype)
if dtype in (np.complex64, np.complex128):
x += 1j * np.random.uniform(-2, 2, size=shape[0]).astype(dtype)
return x
for op, real_range in op_range:
with self.test_session():
for dtype, tol in dtype_tols:
x = tf.constant(rand(dtype))
y = tf.constant(rand(dtype))
z = op(x, y)
grads = tf.test.compute_gradient(
[x, y], [shape, shape],
z,
shape,
x_init_value=[rand(dtype), rand(dtype)])
if isinstance(grads, tuple):
grads = [grads]
for analytical, numerical in grads:
self.assertAllClose(analytical, numerical, rtol=tol, atol=tol)
class BinaryOpTest(tf.test.TestCase):
def _compareCpu(self, x, y, np_func, tf_func, also_compare_variables=False):
np_ans = np_func(x, y)
with self.test_session(use_gpu=False):
inx = tf.convert_to_tensor(x)
iny = tf.convert_to_tensor(y)
out = tf_func(inx, iny)
tf_cpu = out.eval()
# Test that the op takes precedence over numpy operators.
np_left = tf_func(x, iny).eval()
np_right = tf_func(inx, y).eval()
if also_compare_variables:
var_x = tf.Variable(x)
var_y = tf.Variable(y)
tf.global_variables_initializer().run()
print(type(x), type(y), type(var_x), type(var_y))
print(type(tf_func(x, var_y)), type(tf_func(var_x, y)))
np_var_left = tf_func(x, var_y).eval()
np_var_right = tf_func(var_x, y).eval()
if np_ans.dtype != np.object:
self.assertAllClose(np_ans, tf_cpu)
self.assertAllClose(np_ans, np_left)
self.assertAllClose(np_ans, np_right)
if also_compare_variables:
self.assertAllClose(np_ans, np_var_left)
self.assertAllClose(np_ans, np_var_right)
self.assertShapeEqual(np_ans, out)
_GRAD_TOL = {tf.float16: 1e-3,
tf.float32: 1e-3,
tf.complex64: 1e-2,
tf.float64: 1e-5,
tf.complex128: 1e-4}
def _compareGradientX(self, x, y, np_func, tf_func,
numeric_gradient_type=None):
z = np_func(x, y)
zs = list(z.shape)
with self.test_session():
inx = tf.convert_to_tensor(x)
iny = tf.convert_to_tensor(y)
if x.dtype in (np.float32, np.float64):
out = 1.1 * tf_func(inx, iny)
else:
out = tf_func(inx, iny)
xs = list(x.shape)
jacob_t, jacob_n = tf.test.compute_gradient(inx,
xs,
out,
zs,
x_init_value=x)
if numeric_gradient_type is not None:
xf = x.astype(numeric_gradient_type)
yf = y.astype(numeric_gradient_type)
inxf = tf.convert_to_tensor(xf)
inyf = tf.convert_to_tensor(yf)
outf = tf_func(inxf, inyf)
_, jacob_n = tf.test.compute_gradient(inxf,
xs,
outf,
zs,
x_init_value=xf,
delta=1e-3)
jacob_n = jacob_n.astype(x.dtype)
tol = self._GRAD_TOL[tf.as_dtype(x.dtype)]
self.assertAllClose(jacob_t, jacob_n, rtol=tol, atol=tol)
def _compareGradientY(self, x, y, np_func, tf_func,
numeric_gradient_type=None):
z = np_func(x, y)
zs = list(z.shape)
with self.test_session():
inx = tf.convert_to_tensor(x)
iny = tf.convert_to_tensor(y)
if x.dtype in (np.float32, np.float64):
out = 1.1 * tf_func(inx, iny)
else:
out = tf_func(inx, iny)
ys = list(np.shape(y))
jacob_t, jacob_n = tf.test.compute_gradient(iny,
ys,
out,
zs,
x_init_value=y)
if numeric_gradient_type is not None:
xf = x.astype(numeric_gradient_type)
yf = y.astype(numeric_gradient_type)
inxf = tf.convert_to_tensor(xf)
inyf = tf.convert_to_tensor(yf)
outf = tf_func(inxf, inyf)
_, jacob_n = tf.test.compute_gradient(inyf,
ys,
outf,
zs,
x_init_value=yf)
jacob_n = jacob_n.astype(x.dtype)
tol = self._GRAD_TOL[tf.as_dtype(x.dtype)]
self.assertAllClose(jacob_t, jacob_n, rtol=tol, atol=tol)
def _compareGpu(self, x, y, np_func, tf_func):
np_ans = np_func(x, y)
with self.test_session(use_gpu=True):
inx = tf.convert_to_tensor(x)
iny = tf.convert_to_tensor(y)
out = tf_func(inx, iny)
tf_gpu = out.eval()
self.assertAllClose(np_ans, tf_gpu)
self.assertShapeEqual(np_ans, out)
# TODO(zhifengc/ke): make gradient checker work on GPU.
def _compareBoth(self, x, y, np_func, tf_func, also_compare_variables=False):
self._compareCpu(x, y, np_func, tf_func, also_compare_variables)
if x.dtype in (np.float16, np.float32, np.float64):
if tf_func not in (_FLOORDIV, tf.floordiv, tf.igamma, tf.igammac, tf.zeta, tf.polygamma):
self._compareGradientX(x, y, np_func, tf_func)
self._compareGradientY(x, y, np_func, tf_func)
if tf_func in (tf.igamma, tf.igammac, tf.zeta, tf.polygamma):
# These methods only support gradients in the second parameter
self._compareGradientY(x, y, np_func, tf_func)
self._compareGpu(x, y, np_func, tf_func)
def testFloatBasic(self):
x = np.linspace(-5, 20, 15).reshape(1, 3, 5).astype(np.float32)
y = np.linspace(20, -5, 15).reshape(1, 3, 5).astype(np.float32)
self._compareBoth(x, y, np.add, tf.add, also_compare_variables=True)
self._compareBoth(x, y, np.subtract, tf.sub)
self._compareBoth(x, y, np.multiply, tf.mul)
self._compareBoth(x, y + 0.1, np.true_divide, tf.truediv)
self._compareBoth(x, y + 0.1, np.floor_divide, tf.floordiv)
self._compareBoth(x, y, np.add, _ADD)
self._compareBoth(x, y, np.subtract, _SUB)
self._compareBoth(x, y, np.multiply, _MUL)
self._compareBoth(x, y + 0.1, np.true_divide, _TRUEDIV)
self._compareBoth(x, y + 0.1, np.floor_divide, _FLOORDIV)
try:
from scipy import special # pylint: disable=g-import-not-at-top
a_pos_small = np.linspace(0.1, 2, 15).reshape(1, 3, 5).astype(np.float32)
x_pos_small = np.linspace(0.1, 10, 15).reshape(1, 3, 5).astype(np.float32)
self._compareBoth(a_pos_small, x_pos_small, special.gammainc, tf.igamma)
self._compareBoth(a_pos_small, x_pos_small, special.gammaincc, tf.igammac)
# Need x > 1
self._compareBoth(x_pos_small + 1, a_pos_small, special.zeta, tf.zeta)
n_small = np.arange(0, 15).reshape(1, 3, 5).astype(np.float32)
self._compareBoth(n_small, x_pos_small, special.polygamma, tf.polygamma)
except ImportError as e:
tf.logging.warn("Cannot test special functions: %s" % str(e))
def testFloatDifferentShapes(self):
x = np.array([1, 2, 3, 4]).reshape(2, 2).astype(np.float32)
y = np.array([1, 2]).reshape(2, 1).astype(np.float32)
with self.test_session() as sess:
inx = tf.convert_to_tensor(x)
iny = tf.convert_to_tensor(y)
s = tf.reduce_sum(inx * iny)
gx, gy = sess.run(tf.gradients(s, [inx, iny]))
# gx is simply the broadcasted y
self.assertAllEqual(gx, np.array([1, 1, 2, 2])
.reshape(2, 2).astype(np.float32))
# gy is x's column summed up
self.assertAllEqual(gy, np.array([3, 7]).
reshape(2, 1).astype(np.float32))
def testFloatVariableOverload(self):
x = np.array([1, 2, 3, 4]).reshape(2, 2).astype(np.int32)
y = np.array([1, 2]).reshape(2, 1).astype(np.int32)
var_x = tf.Variable(x)
var_y = tf.Variable(y)
with self.test_session() as sess:
sess.run([var_x.initializer, var_y.initializer])
left_result = (var_x * y).eval()
right_result = (x * var_y).eval()
np_result = x * y
self.assertAllEqual(np_result, left_result)
self.assertAllEqual(np_result, right_result)
def testDoubleBasic(self):
x = np.linspace(-5, 20, 15).reshape(1, 3, 5).astype(np.float64)
y = np.linspace(20, -5, 15).reshape(1, 3, 5).astype(np.float64)
self._compareBoth(x, y, np.add, tf.add)
self._compareBoth(x, y, np.subtract, tf.sub)
self._compareBoth(x, y, np.multiply, tf.mul)
self._compareBoth(x, y + 0.1, np.true_divide, tf.truediv)
self._compareBoth(x, y + 0.1, np.floor_divide, tf.floordiv)
self._compareBoth(x, y, np.add, _ADD)
self._compareBoth(x, y, np.subtract, _SUB)
self._compareBoth(x, y, np.multiply, _MUL)
self._compareBoth(x, y + 0.1, np.true_divide, _TRUEDIV)
self._compareBoth(x, y + 0.1, np.floor_divide, _FLOORDIV)
try:
from scipy import special # pylint: disable=g-import-not-at-top
a_pos_small = np.linspace(0.1, 2, 15).reshape(1, 3, 5).astype(np.float32)
x_pos_small = np.linspace(0.1, 10, 15).reshape(1, 3, 5).astype(np.float32)
self._compareBoth(a_pos_small, x_pos_small, special.gammainc, tf.igamma)
self._compareBoth(a_pos_small, x_pos_small, special.gammaincc, tf.igammac)
except ImportError as e:
tf.logging.warn("Cannot test special functions: %s" % str(e))
def testInt8Basic(self):
x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.int8)
y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.int8)
self._compareBoth(x, y, np.multiply, tf.mul)
self._compareBoth(x, y, np.multiply, _MUL)
def testInt16Basic(self):
x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.int16)
y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.int16)
self._compareBoth(x, y, np.multiply, tf.mul)
self._compareBoth(x, y, np.multiply, _MUL)
def testUint16Basic(self):
x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.uint16)
y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.uint16)
self._compareBoth(x, y, np.multiply, tf.mul)
self._compareBoth(x, y, np.multiply, _MUL)
self._compareBoth(x, y, np.true_divide, tf.truediv)
self._compareBoth(x, y, np.floor_divide, tf.floordiv)
self._compareBoth(x, y, np.true_divide, _TRUEDIV)
self._compareBoth(x, y, np.floor_divide, _FLOORDIV)
def testInt32Basic(self):
x = np.arange(1, 13, 2).reshape(1, 3, 2).astype(np.int32)
y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.int32)
self._compareBoth(x, y, np.add, tf.add)
self._compareBoth(x, y, np.subtract, tf.sub)
self._compareBoth(x, y, np.multiply, tf.mul)
self._compareBoth(x, y, np.true_divide, tf.truediv)
self._compareBoth(x, y, np.floor_divide, tf.floordiv)
self._compareBoth(x, y, np.mod, tf.mod)
self._compareBoth(x, y, np.add, _ADD)
self._compareBoth(x, y, np.subtract, _SUB)
self._compareBoth(x, y, np.multiply, _MUL)
self._compareBoth(x, y, np.true_divide, _TRUEDIV)
self._compareBoth(x, y, np.floor_divide, _FLOORDIV)
self._compareBoth(x, y, np.mod, _MOD)
# _compareBoth tests on GPU only for floating point types, so test
# _MOD for int32 on GPU by calling _compareGpu
self._compareGpu(x, y, np.mod, _MOD)
def testInt64Basic(self):
x = np.arange(1 << 40, 13 << 40, 2 << 40).reshape(1, 3, 2).astype(np.int64)
y = np.arange(1, 7, 1).reshape(1, 3, 2).astype(np.int64)
self._compareBoth(x, y, np.subtract, tf.sub)
self._compareBoth(x, y, np.multiply, tf.mul)
self._compareBoth(x, y, np.true_divide, tf.truediv)
self._compareBoth(x, y, np.floor_divide, tf.floordiv)
self._compareBoth(x, y, np.mod, tf.mod)
self._compareBoth(x, y, np.subtract, _SUB)
self._compareBoth(x, y, np.multiply, _MUL)
self._compareBoth(x, y, np.true_divide, _TRUEDIV)
self._compareBoth(x, y, np.floor_divide, _FLOORDIV)
self._compareBoth(x, y, np.mod, _MOD)
def testComplex64Basic(self):
x = np.complex(1, 1) * np.linspace(-10, 10, 6).reshape(1, 3, 2).astype(
np.complex64)
y = np.complex(1, 1) * np.linspace(20, -20, 6).reshape(1, 3, 2).astype(
np.complex64)
self._compareBoth(x, y, np.add, tf.add)
self._compareBoth(x, y, np.subtract, tf.sub)
self._compareBoth(x, y, np.multiply, tf.mul)
self._compareBoth(x, y + 0.1, np.true_divide, tf.truediv)
self._compareBoth(x, y, np.add, _ADD)
self._compareBoth(x, y, np.subtract, _SUB)
self._compareBoth(x, y, np.multiply, _MUL)
self._compareBoth(x, y + 0.1, np.true_divide, _TRUEDIV)
def testComplex128Basic(self):
x = np.complex(1, 1) * np.linspace(-10, 10, 6).reshape(1, 3, 2).astype(
np.complex128)
y = np.complex(1, 1) * np.linspace(20, -20, 6).reshape(1, 3, 2).astype(
np.complex128)
self._compareBoth(x, y, np.add, tf.add)
self._compareBoth(x, y, np.subtract, tf.sub)
self._compareBoth(x, y, np.multiply, tf.mul)
self._compareBoth(x, y + 0.1, np.true_divide, tf.truediv)
self._compareBoth(x, y, np.add, _ADD)
self._compareBoth(x, y, np.subtract, _SUB)
self._compareBoth(x, y, np.multiply, _MUL)
self._compareBoth(x, y + 0.1, np.true_divide, _TRUEDIV)
def testStringComparison(self):
x = np.array([["abc", "bh"], ["c", ""]])
y = np.array([["abc", "bh"], ["def", "hi"]])
with self.test_session(use_gpu=False) as sess:
cmp_eq = tf.equal(x, y)
cmp_not_eq = tf.not_equal(x, y)
values = sess.run([cmp_eq, cmp_not_eq])
self.assertAllEqual([[True, True], [False, False]], values[0])
self.assertAllEqual([[False, False], [True, True]], values[1])
def testString(self):
x = np.array([["x_0_0", "x_0_1", "x_0_2"],
["x_1_0", "x_1_1", "x_1_2"],
["x_2_0", "x_2_1", "x_2_2"]], dtype=np.object)
y = np.array([["y_0_0", "y_0_1", "y_0_2"],
["y_1_0", "y_1_1", "y_1_2"],
["y_2_0", "y_2_1", "y_2_2"]], dtype=np.object)
z = np.array([["z_0", "z_1", "z_2"]], dtype=np.object)
w = np.array("w", dtype=np.object)
self._compareCpu(x, y, _ADD, _ADD)
self._compareCpu(x, z, _ADD, _ADD)
self._compareCpu(x, w, _ADD, _ADD)
self._compareCpu(z, w, _ADD, _ADD)
def _compareBCast(self, xs, ys, dtype, np_func, tf_func):
if dtype in (np.complex64, np.complex128):
x = (1 + np.linspace(0, 2 + 3j, np.prod(xs))).astype(dtype).reshape(xs)
y = (1 + np.linspace(0, 2 - 2j, np.prod(ys))).astype(dtype).reshape(ys)
else:
x = (1 + np.linspace(0, 5, np.prod(xs))).astype(dtype).reshape(xs)
y = (1 + np.linspace(0, 5, np.prod(ys))).astype(dtype).reshape(ys)
self._compareCpu(x, y, np_func, tf_func)
if x.dtype in (np.float16, np.float32, np.float64, np.complex64,
np.complex128):
if tf_func not in (_FLOORDIV, tf.floordiv):
if x.dtype == np.float16:
# Compare fp16 theoretical gradients to fp32 numerical gradients,
# since fp16 numerical gradients are too imprecise unless great
# care is taken with choosing the inputs and the delta. This is
# a weaker check (in particular, it does not test the op itself,
# only its gradient), but it's much better than nothing.
self._compareGradientX(x, y, np_func, tf_func, np.float)
self._compareGradientY(x, y, np_func, tf_func, np.float)
else:
self._compareGradientX(x, y, np_func, tf_func)
self._compareGradientY(x, y, np_func, tf_func)
self._compareGpu(x, y, np_func, tf_func)
# TODO(josh11b,vrv): Refactor this to use parameterized tests.
def _testBCastByFunc(self, funcs, xs, ys):
dtypes = [
np.float16,
np.float32,
np.float64,
np.int32,
np.int64,
np.complex64,
np.complex128,
]
for dtype in dtypes:
for (np_func, tf_func) in funcs:
if (dtype in (np.complex64, np.complex128) and
tf_func in (_FLOORDIV, tf.floordiv)):
continue # floordiv makes no sense for complex numbers
self._compareBCast(xs, ys, dtype, np_func, tf_func)
self._compareBCast(ys, xs, dtype, np_func, tf_func)
def _testBCastA(self, xs, ys):
funcs = [
(np.add, tf.add),
(np.add, _ADD),
]
self._testBCastByFunc(funcs, xs, ys)
def _testBCastB(self, xs, ys):
funcs = [
(np.subtract, tf.sub),
(np.subtract, _SUB),
(np.power, tf.pow),
]
self._testBCastByFunc(funcs, xs, ys)
def _testBCastC(self, xs, ys):
funcs = [
(np.multiply, tf.mul),
(np.multiply, _MUL),
]
self._testBCastByFunc(funcs, xs, ys)
def _testBCastD(self, xs, ys):
funcs = [
(np.true_divide, tf.truediv),
(np.floor_divide, tf.floordiv),
(np.true_divide, _TRUEDIV),
(np.floor_divide, _FLOORDIV),
]
self._testBCastByFunc(funcs, xs, ys)
def testBCast_0A(self):
self._testBCastA([1, 3, 2], [1])
def testBCast_0B(self):
self._testBCastB([1, 3, 2], [1])
def testBCast_0C(self):
self._testBCastC([1, 3, 2], [1])
def testBCast_0D(self):
self._testBCastD([1, 3, 2], [1])
def testBCast_1A(self):
self._testBCastA([1, 3, 2], [2])
def testBCast_1B(self):
self._testBCastB([1, 3, 2], [2])
def testBCast_1C(self):
self._testBCastC([1, 3, 2], [2])
def testBCast_1D(self):
self._testBCastD([1, 3, 2], [2])
def testBCast_2A(self):
self._testBCastA([1, 3, 2], [3, 2])
def testBCast_2B(self):
self._testBCastB([1, 3, 2], [3, 2])
def testBCast_2C(self):
self._testBCastC([1, 3, 2], [3, 2])
def testBCast_2D(self):
self._testBCastD([1, 3, 2], [3, 2])
def testBCast_3A(self):
self._testBCastA([1, 3, 2], [3, 1])
def testBCast_3B(self):
self._testBCastB([1, 3, 2], [3, 1])
def testBCast_3C(self):
self._testBCastC([1, 3, 2], [3, 1])
def testBCast_3D(self):
self._testBCastD([1, 3, 2], [3, 1])
def testBCast_4A(self):
self._testBCastA([1, 3, 2], [1, 3, 2])
def testBCast_4B(self):
self._testBCastB([1, 3, 2], [1, 3, 2])
def testBCast_4C(self):
self._testBCastC([1, 3, 2], [1, 3, 2])
def testBCast_4D(self):
self._testBCastD([1, 3, 2], [1, 3, 2])
def testBCast_5A(self):
self._testBCastA([1, 3, 2], [2, 3, 1])
def testBCast_5B(self):
self._testBCastB([1, 3, 2], [2, 3, 1])
def testBCast_5C(self):
self._testBCastC([1, 3, 2], [2, 3, 1])
def testBCast_5D(self):
self._testBCastD([1, 3, 2], [2, 3, 1])
def testBCast_6A(self):
self._testBCastA([1, 3, 2], [2, 1, 1])
def testBCast_6B(self):
self._testBCastB([1, 3, 2], [2, 1, 1])
def testBCast_6C(self):
self._testBCastC([1, 3, 2], [2, 1, 1])
def testBCast_6D(self):
self._testBCastD([1, 3, 2], [2, 1, 1])
def testBCast_7A(self):
self._testBCastA([1, 3, 2], [1, 3, 1])
def testBCast_7B(self):
self._testBCastB([1, 3, 2], [1, 3, 1])
def testBCast_7C(self):
self._testBCastC([1, 3, 2], [1, 3, 1])
def testBCast_7D(self):
self._testBCastD([1, 3, 2], [1, 3, 1])
def testBCast_8A(self):
self._testBCastA([2, 1, 5], [2, 3, 1])
def testBCast_8B(self):
self._testBCastB([2, 1, 5], [2, 3, 1])
def testBCast_8C(self):
self._testBCastC([2, 1, 5], [2, 3, 1])
def testBCast_8D(self):
self._testBCastD([2, 1, 5], [2, 3, 1])
def testBCast_9A(self):
self._testBCastA([2, 0, 5], [2, 0, 1])
def testBCast_9B(self):
self._testBCastB([2, 0, 5], [2, 0, 1])
def testBCast_9C(self):
self._testBCastC([2, 0, 5], [2, 0, 1])
def testBCast_9D(self):
self._testBCastD([2, 0, 5], [2, 0, 1])
def testBCast_10A(self):
self._testBCastA([2, 3, 0], [2, 3, 1])
def testBCast_10B(self):
self._testBCastB([2, 3, 0], [2, 3, 1])
def testBCast_10C(self):
self._testBCastC([2, 3, 0], [2, 3, 1])
def testBCast_10D(self):
self._testBCastD([2, 3, 0], [2, 3, 1])
def testBCast_11A(self):
self._testBCastA([1, 3, 2], [1, 3, 2])
def testBCast_11B(self):
self._testBCastB([1, 3, 2], [1, 3, 2])
def testBCast_11C(self):
self._testBCastC([1, 3, 2], [1, 3, 2])
def testBCast_11D(self):
self._testBCastD([1, 3, 2], [1, 3, 2])
def testBCast_12A(self):
self._testBCastA([1, 1, 1, 1, 3, 2], [1, 3, 2])
def testBCast_12B(self):
self._testBCastB([1, 1, 1, 1, 3, 2], [1, 3, 2])
def testBCast_12C(self):
self._testBCastC([1, 1, 1, 1, 3, 2], [1, 3, 2])
def testBCast_12D(self):