def testGradients(self):
np.random.seed(1618)
sp_shapes = [(10, 10, 10), (5, 5), (1618,), (3, 3, 7)]
dense_shapes = [(10, 10, 1), (5, 5), (1,), (1, 7)]
with self.test_session(use_gpu=False):
for dtype in [np.float32, np.float64]:
for sp_shape, dense_shape in zip(sp_shapes, dense_shapes):
sp_vals_np = np.random.rand(*sp_shape).astype(dtype) + 1
dense_vals_np = np.random.rand(*dense_shape).astype(dtype) + 1
sp_t, nnz = _sparsify(sp_vals_np, thresh=1.5)
dense_t = constant_op.constant(dense_vals_np)
cmul = sp_t * dense_t
err = gradient_checker.compute_gradient_error([sp_t.values, dense_t],
[(nnz,), dense_shape],
cmul.values, (nnz,))
self.assertLess(err, 1e-4)
cdiv = sp_t / dense_t
err = gradient_checker.compute_gradient_error(sp_t.values, (nnz,),
cdiv.values, (nnz,))
self.assertLess(err, 1e-4)
err = gradient_checker.compute_gradient_error(
dense_t,
dense_shape,
cdiv.values, (nnz,),
x_init_value=dense_vals_np)
self.assertLess(err, 2e-4)
def testFillFloat(self):
with self.test_session(use_gpu=False) as sess:
values = constant_op.constant(
[0.0, 10.0, 13.0, 14.0, 32.0, 33.0], dtype=dtypes.float64)
default_value = constant_op.constant(-1.0, dtype=dtypes.float64)
sp_input = sparse_tensor.SparseTensorValue(
indices=np.array([[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]]),
values=values,
dense_shape=np.array([5, 6]))
sp_output, empty_row_indicator = (sparse_ops.sparse_fill_empty_rows(
sp_input, default_value))
output, empty_row_indicator_out = sess.run(
[sp_output, empty_row_indicator])
self.assertAllEqual(output.indices, [[0, 0], [1, 0], [1, 3], [1, 4],
[2, 0], [3, 2], [3, 3], [4, 0]])
self.assertAllClose(output.values, [0, 10, 13, 14, -1, 32, 33, -1])
self.assertAllEqual(output.dense_shape, [5, 6])
self.assertAllEqual(empty_row_indicator_out,
np.array([0, 0, 1, 0, 1]).astype(np.bool))
values_grad_err = gradient_checker.compute_gradient_error(
values, values.shape.as_list(), sp_output.values, [8], delta=1e-8)
self.assertGreater(values_grad_err, 0)
self.assertLess(values_grad_err, 1e-8)
default_value_grad_err = gradient_checker.compute_gradient_error(
default_value,
default_value.shape.as_list(),
sp_output.values, [8],
delta=1e-8)
self.assertGreater(default_value_grad_err, 0)
self.assertLess(default_value_grad_err, 1e-8)
def _test_gradient(self,
x_shape,
scale_shape,
use_gpu=True,
data_format='NHWC'):
np.random.seed(1)
x_val = np.random.random_sample(x_shape).astype(np.float32)
scale_val = np.random.random_sample(scale_shape).astype(np.float32)
offset_val = np.random.random_sample(scale_shape).astype(np.float32)
with self.test_session(use_gpu=use_gpu):
x = constant_op.constant(x_val, name='x')
scale = constant_op.constant(scale_val, name='scale')
offset = constant_op.constant(offset_val, name='offset')
y, _, _ = nn_impl.fused_batch_norm(
x, scale, offset, data_format=data_format)
err_x = gradient_checker.compute_gradient_error(x, x_shape, y, x_shape)
err_scale = gradient_checker.compute_gradient_error(scale, scale_shape, y,
x_shape)
err_offset = gradient_checker.compute_gradient_error(offset, scale_shape,
y, x_shape)
err_tolerance = 1e-3
self.assertLess(err_x, err_tolerance)
self.assertLess(err_scale, err_tolerance)
self.assertLess(err_offset, err_tolerance)
def testGradient(self):
if np.__version__ == "1.13.0":
self.skipTest("numpy 1.13.0 bug")
np.random.seed(8161)
test_dims = [(11, 1, 5, 7, 1), (2, 2)]
with self.test_session(use_gpu=False):
for dims in test_dims:
sp_t, nnz = _sparsify(np.random.randn(*dims))
# reduce random axes from 1D to N-D
for d in range(1, len(dims) + 1):
axes = np.random.choice(len(dims), size=d, replace=False).tolist()
reduced = sparse_ops.sparse_reduce_sum(sp_t, axes)
err = gradient_checker.compute_gradient_error(sp_t.values, (nnz,),
reduced,
reduced.eval().shape)
self.assertLess(err, 1e-3)
# Tests for negative axes.
reduced = sparse_ops.sparse_reduce_sum(sp_t, -1)
err = gradient_checker.compute_gradient_error(sp_t.values, (nnz,),
reduced,
reduced.eval().shape)
self.assertLess(err, 1e-3)
def _testGradients(self, tr_a, tr_b, sp_a, sp_b, a_dtype, b_dtype, delta,
name):
with self.test_session():
a = constant_op.constant(
RandMatrix(
3, 2, tr_a, round_bfloat=True), dtype=dtypes.float32)
b = constant_op.constant(
RandMatrix(
2, 4, tr_b, round_bfloat=True), dtype=dtypes.float32)
tf_a = math_ops.cast(a, a_dtype) if a_dtype != dtypes.float32 else a
tf_b = math_ops.cast(b, b_dtype) if b_dtype != dtypes.float32 else b
m = math_ops.matmul(
tf_a,
tf_b,
name=name,
transpose_a=tr_a,
transpose_b=tr_b,
a_is_sparse=sp_a,
b_is_sparse=sp_b)
err = (gradient_checker.compute_gradient_error(
a, [2, 3] if tr_a else [3, 2],
m, [3, 4],
x_init_value=a.eval(),
delta=delta) + gradient_checker.compute_gradient_error(
b, [4, 2] if tr_b else [2, 4],
m, [3, 4],
x_init_value=b.eval(),
delta=delta))
self.assertLessEqual(err, delta / 2.)
def doOutputTest(self, input_shape, moments_axes, tol=1e-4,
check_gradients=False):
for mu in [0.0, 1.0, 1e3]:
for sigma in [1.0, 0.1]:
for keep_dims in [True, False]:
input_values = np.random.rand(*input_shape) * sigma + mu
expected_mean = np.mean(
input_values, axis=moments_axes, keepdims=keep_dims)
expected_var = np.var(
input_values, axis=moments_axes, keepdims=keep_dims)
with ops.Graph().as_default() as g:
with self.test_session(graph=g) as sess:
inputs = constant_op.constant(
input_values, shape=input_shape, dtype=dtypes.float32)
mean, variance = nn_impl.moments(
inputs, moments_axes, keep_dims=keep_dims)
if check_gradients:
err = gradient_checker.compute_gradient_error(
inputs, input_shape, mean, mean.shape.as_list())
self.assertLess(err, 1e-3)
err = gradient_checker.compute_gradient_error(
inputs, input_shape, variance, variance.shape.as_list())
self.assertLess(err, 1e-3)
# Evaluate.
[mean, variance] = sess.run([mean, variance])
# Make sure that there are no NaNs
self.assertFalse(np.isnan(mean).any())
self.assertFalse(np.isnan(variance).any())
self.assertAllClose(mean, expected_mean, rtol=tol, atol=tol)
self.assertAllClose(variance, expected_var, rtol=tol, atol=tol)
def testGradient(self):
np.random.seed(1) # Make it reproducible.
x_shape = [5, 10]
x_np = np.random.randn(*x_shape).astype(np.float32)
alpha_np = np.float32(np.random.rand(1, x_shape[1]) + 0.01)
clip_np = np.float32(np.random.rand(x_shape[0], 1) * 5.)
with self.test_session(use_gpu=True):
x_tf = constant_op.constant(x_np)
alpha_tf = constant_op.constant(alpha_np)
clip_tf = constant_op.constant(clip_np)
y_tf = scaled_softplus(x_tf, alpha_tf)
z_tf = scaled_softplus(x_tf, alpha_tf, clip_tf * 0.1)
err = gradient_checker.compute_gradient_error([x_tf, alpha_tf],
[x_shape, alpha_np.shape],
y_tf, x_shape,
[x_np, alpha_np],
delta=0.002)
err_clip = gradient_checker.compute_gradient_error(
[x_tf, alpha_tf, clip_tf],
[x_shape, alpha_np.shape, clip_np.shape],
z_tf, x_shape,
[x_np, alpha_np, clip_np],
delta=0.002)
eps = 2e-4
self.assertLess(err, eps)
self.assertLess(err_clip, eps)
def _test_gradient(self,
x_shape,
x_dtype,
scale_shape,
scale_dtype,
use_gpu=True,
data_format='NHWC',
is_training=True):
np.random.seed(1)
x_val = np.random.random_sample(x_shape).astype(x_dtype)
scale_val = np.random.random_sample(scale_shape).astype(scale_dtype)
offset_val = np.random.random_sample(scale_shape).astype(scale_dtype)
with self.test_session(use_gpu=use_gpu):
x = constant_op.constant(x_val, name='x')
scale = constant_op.constant(scale_val, name='scale')
offset = constant_op.constant(offset_val, name='offset')
if is_training:
pop_mean = None
pop_var = None
else:
pop_mean = np.random.random_sample(scale_shape).astype(scale_dtype)
pop_var = np.random.random_sample(scale_shape).astype(scale_dtype)
y, _, _ = nn_impl.fused_batch_norm(
x,
scale,
offset,
mean=pop_mean,
variance=pop_var,
data_format=data_format,
is_training=is_training)
if x_dtype != np.float16:
err_x = gradient_checker.compute_gradient_error(x, x_shape, y, x_shape)
err_scale = gradient_checker.compute_gradient_error(
scale, scale_shape, y, x_shape)
err_offset = gradient_checker.compute_gradient_error(
offset, scale_shape, y, x_shape)
else:
x32 = constant_op.constant(x_val, name='x32', dtype=dtypes.float32)
y32, _, _ = nn_impl.fused_batch_norm(
x32,
scale,
offset,
mean=pop_mean,
variance=pop_var,
data_format=data_format,
is_training=is_training)
err_x = self._compute_gradient_error_float16(x, x32, x_shape, y, y32,
x_shape)
err_scale = self._compute_gradient_error_float16(
scale, scale, scale_shape, y, y32, x_shape)
err_offset = self._compute_gradient_error_float16(
offset, offset, scale_shape, y, y32, x_shape)
x_err_tolerance = 2e-3 if x_dtype == np.float16 else 1e-3
scale_err_tolerance = 1e-3
self.assertLess(err_x, x_err_tolerance)
self.assertLess(err_scale, scale_err_tolerance)
self.assertLess(err_offset, scale_err_tolerance)
def testEmptyFails(self):
with ops.Graph().as_default() as g:
with self.session(graph=g):
x = array_ops.placeholder(dtypes.float32)
with g.gradient_override_map({"Identity": "BadGrad"}):
y = array_ops.identity(x)
bad = r"Empty gradient has wrong shape: expected \(0, 3\), got \(3, 0\)"
with self.assertRaisesRegexp(ValueError, bad):
gradient_checker.compute_gradient(x, (0, 3), y, (0, 3))
with self.assertRaisesRegexp(ValueError, bad):
gradient_checker.compute_gradient_error(x, (0, 3), y, (0, 3))
def testClipByValueGradient(self):
inputs = constant_op.constant([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32)
outputs_1 = clip_ops.clip_by_value(inputs, 0.5, 3.5)
min_val = constant_op.constant([0.5, 0.5, 0.5, 0.5], dtype=dtypes.float32)
max_val = constant_op.constant([3.5, 3.5, 3.5, 3.5], dtype=dtypes.float32)
outputs_2 = clip_ops.clip_by_value(inputs, min_val, max_val)
with self.test_session():
error_1 = gradient_checker.compute_gradient_error(inputs, [4],
outputs_1, [4])
self.assertLess(error_1, 1e-4)
error_2 = gradient_checker.compute_gradient_error(inputs, [4],
outputs_2, [4])
self.assertLess(error_2, 1e-4)
def testGradientsAxis0(self):
np.random.seed(7)
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
data = np.random.randn(*shape)
shapes = [shape[1:]] * shape[0]
with self.test_session(use_gpu=True):
# TODO(irving): Remove list() once we handle maps correctly
xs = list(map(constant_op.constant, data))
c = array_ops.pack(xs)
err = gradient_checker.compute_gradient_error(xs, shapes, c, shape)
self.assertLess(err, 1e-6)
c = array_ops.stack(xs)
err = gradient_checker.compute_gradient_error(xs, shapes, c, shape)
self.assertLess(err, 1e-6)
def testSegmentMaxGradient(self):
data = constant_op.constant([1.0, 2.0, 3.0], dtype=dtypes.float32)
segment_ids = constant_op.constant([0, 0, 1], dtype=dtypes.int64)
segment_max = math_ops.segment_max(data, segment_ids)
with self.test_session():
error = gradient_checker.compute_gradient_error(data, [3], segment_max, [2])
self.assertLess(error, 1e-4)
def testGrad(self):
shapes = ((3, 4, 4), (3, 3, 4), (3, 4, 3), (7, 4, 8, 8))
with self.test_session(use_gpu=self._use_gpu):
for shape in shapes:
x = constant_op.constant(
np.random.rand(*shape), dtype=dtypes_lib.float32)
diag_shape = shape[:-2] + (min(shape[-2:]),)
x_diag = constant_op.constant(
np.random.rand(*diag_shape), dtype=dtypes_lib.float32)
y = array_ops.matrix_set_diag(x, x_diag)
error_x = gradient_checker.compute_gradient_error(
x, x.get_shape().as_list(), y, y.get_shape().as_list())
self.assertLess(error_x, 1e-4)
error_x_diag = gradient_checker.compute_gradient_error(
x_diag, x_diag.get_shape().as_list(), y, y.get_shape().as_list())
self.assertLess(error_x_diag, 1e-4)
def _testGlobalGradient(self, from_y="mean"):
with self.cached_session():
x_shape = [3, 5, 4, 2]
x_val = np.random.random_sample(x_shape).astype(np.float64)
x = constant_op.constant(x_val)
x.set_shape(x_shape)
axes = [0, 1, 2]
y_shape = [2] # Depth of x
inputs_to_compute_gradients_for = [x]
out_mean, out_var = self._unweighted_moments(
x, axes, extra_out_grads=inputs_to_compute_gradients_for)
if from_y == "mean":
y = out_mean
elif from_y == "var":
y = out_var
for (i, v) in enumerate(inputs_to_compute_gradients_for):
err = gradient_checker.compute_gradient_error(v,
v.get_shape().as_list(),
y, y_shape)
print("Moments %s gradient err vs input %d = %g" % (from_y, i, err))
self.assertLess(err, 1e-11)
def testLargePoolingRatioThroughGradientError(self):
input_shape = (1, 17, 23, 1)
input_data = self._GenerateRandomInputTensor(input_shape)
pooling_ratio = (1, math.sqrt(13), math.sqrt(7), 1)
output_shape = [int(a / b) for a, b in zip(input_shape, pooling_ratio)]
overlapping = True
pseudo_random = False
with self.cached_session() as _:
input_tensor = constant_op.constant(input_data, shape=input_shape)
output_tensor, unused_a, unused_b = nn_ops.fractional_avg_pool(
input_tensor,
pooling_ratio,
pseudo_random=pseudo_random,
overlapping=overlapping,
deterministic=True,
seed=self._SEED,
seed2=self._SEED2)
# error_margin and delta setting is similar to avg_pool_grad.
error_margin = 1e-4
gradient_error = gradient_checker.compute_gradient_error(
input_tensor,
input_shape,
output_tensor,
output_shape,
x_init_value=input_data.reshape(input_shape),
delta=1e-2)
self.assertLess(gradient_error, error_margin)
def testDifferentTensorShapesThroughGradientError(self):
pseudo_random = True
overlapping = True
pooling_ratio = [1, math.sqrt(3), math.sqrt(2), 1]
for num_batches in [1, 2]:
for num_rows in [5, 13]:
for num_cols in [5, 11]:
for num_channels in [1, 3]:
input_shape = (num_batches, num_rows, num_cols, num_channels)
input_data = self._GenerateRandomInputTensor(input_shape)
with self.cached_session() as _:
input_tensor = constant_op.constant(input_data, shape=input_shape)
output_tensor, unused_a, unused_b = nn_ops.fractional_avg_pool(
input_tensor,
pooling_ratio,
pseudo_random=pseudo_random,
overlapping=overlapping,
deterministic=True,
seed=self._SEED,
seed2=self._SEED2)
output_data = output_tensor.eval()
output_shape = output_data.shape
# error_margin and delta setting is similar to avg_pool_grad.
error_margin = 1e-4
gradient_error = gradient_checker.compute_gradient_error(
input_tensor,
input_shape,
output_tensor,
output_shape,
x_init_value=input_data.reshape(input_shape),
delta=1e-2)
self.assertLess(gradient_error, error_margin)
def testAllInputOptionsThroughGradientError(self):
input_shape = (1, 7, 13, 1)
input_data = self._GenerateRandomInputTensor(input_shape)
pooling_ratio = [1, math.sqrt(2), math.sqrt(3), 1]
for pseudo_random in True, False:
for overlapping in True, False:
with self.cached_session() as _:
input_tensor = constant_op.constant(input_data, shape=input_shape)
output_tensor, unused_a, unused_b = nn_ops.fractional_avg_pool(
input_tensor,
pooling_ratio,
pseudo_random=pseudo_random,
overlapping=overlapping,
deterministic=True,
seed=self._SEED,
seed2=self._SEED2)
output_data = output_tensor.eval()
output_shape = output_data.shape
# error_margin and delta setting is similar to avg_pool_grad.
error_margin = 1e-4
gradient_error = gradient_checker.compute_gradient_error(
input_tensor,
input_shape,
output_tensor,
output_shape,
x_init_value=input_data.reshape(input_shape),
delta=1e-2)
self.assertLess(gradient_error, error_margin)
def testLargePoolingRatioThroughGradientError(self):
input_shape = (1, 17, 23, 1)
input_data = self._GenerateUniqueRandomInputTensor(input_shape)
# Add some randomness to make input_data not so 'integer'
input_data += self._PRNG.random_sample(input_shape)
pooling_ratio = (1, math.sqrt(13), math.sqrt(7), 1)
output_shape = [int(a / b) for a, b in zip(input_shape, pooling_ratio)]
overlapping = True
pseudo_random = False
with self.cached_session() as _:
input_tensor = constant_op.constant(input_data, shape=input_shape)
output_tensor, unused_a, unused_b = nn_ops.fractional_max_pool_v2(
input_tensor,
pooling_ratio,
pseudo_random=pseudo_random,
overlapping=overlapping,
seed=self._SEED)
# error_margin and delta setting is similar to max_pool_grad.
error_margin = 1e-3
gradient_error = gradient_checker.compute_gradient_error(
input_tensor,
input_shape,
output_tensor,
output_shape,
x_init_value=input_data.reshape(input_shape),
delta=1e-2)
self.assertLess(gradient_error, error_margin)
def testGradient(self):
with self.test_session():
for padding in ["SAME", "VALID"]:
for stride in [1, 2]:
np.random.seed(1)
in_shape = [2, 4, 3, 3, 2]
in_val = constant_op.constant(
2 * np.random.random_sample(in_shape) - 1, dtype=dtypes.float32)
filter_shape = [3, 3, 3, 2, 3]
strides = [1, stride, stride, stride, 1]
# Make a convolution op with the current settings, just to easily get
# the shape of the output.
conv_out = nn_ops.conv3d(in_val,
array_ops.zeros(filter_shape), strides,
padding)
out_backprop_shape = conv_out.get_shape().as_list()
out_backprop_val = constant_op.constant(
2 * np.random.random_sample(out_backprop_shape) - 1,
dtype=dtypes.float32)
output = nn_ops.conv3d_backprop_filter_v2(in_val, filter_shape,
out_backprop_val, strides,
padding)
err = gradient_checker.compute_gradient_error(
[in_val, out_backprop_val], [in_shape, out_backprop_shape],
output, filter_shape)
print("conv3d_backprop_filter gradient err = %g " % err)
err_tolerance = 1e-3
self.assertLess(err, err_tolerance)
def _RunAndVerifyGradients(self, dtype):
with self.test_session(use_gpu=True):
# random shape
shape = np.random.randint(1, 5, size=4)
# Make depth at least 2 to make it meaningful
shape[3] += 1
# random depth_radius, bias, alpha, beta. cuDNN requires depth_radius to
# be in [1, 7].
lrn_depth_radius = np.random.randint(1, min(8, shape[3]))
bias = 1.0 + np.random.rand()
alpha = 1.0 * np.random.rand()
# cuDNN requires beta >= 0.01.
beta = 0.01 + 1.0 * np.random.rand()
if dtype == dtypes.float32:
inp_array = np.random.rand(*shape).astype(np.float32)
else:
inp_array = np.random.rand(*shape).astype(np.float16)
inp = constant_op.constant(
list(inp_array.ravel(order="C")), shape=shape, dtype=dtype)
lrn_op = nn.local_response_normalization(
inp,
name="lrn",
depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
err = gradient_checker.compute_gradient_error(inp, shape, lrn_op, shape)
print("LRN Gradient error for bias ", bias, "alpha ", alpha, " beta ", beta,
" is ", err)
if dtype == dtypes.float32:
self.assertLess(err, 1e-4)
else:
self.assertLess(err, 1.0)
def testGradientsEmbeddingLookupSparse(self):
vocab_size = 12
batch_size = 4
param_shape = [2, 3]
sp_ids, sp_weights, _, _, _ = (self._RandomIdsAndWeights(
batch_size, vocab_size))
for num_shards, combiner, dtype, ignore_weights in itertools.product(
[1, 3], ["sum", "mean", "sqrtn"], [dtypes.float32,
dtypes.float64], [True, False]):
with self.test_session():
x, params, _ = _EmbeddingParams(
num_shards, vocab_size, shape=param_shape, dtype=dtype)
y = embedding_ops.embedding_lookup_sparse(
x,
sp_ids,
None if ignore_weights else sp_weights,
combiner=combiner)
x_name = [_PName(i) for i in range(num_shards)]
x_init_value = [params[x_n + ":0"] for x_n in x_name]
x_shape = [i.shape for i in x_init_value]
y_shape = [batch_size] + list(params[_PName(0) + ":0"].shape[1:])
err = gradient_checker.compute_gradient_error(
x, x_shape, y, y_shape, x_init_value=x_init_value)
self.assertLess(err, 1e-5 if dtype == dtypes.float64 else 2e-3)
def testGradient(self):
x_shape = [5, 10]
x_np = np.random.randn(*x_shape).astype(np.float64)
z_np = np.random.randint(0, 5, size=x_shape).astype(np.float64)
with self.test_session():
x_tf = constant_op.constant(x_np)
y_tf = nn_impl.log_poisson_loss(z_np, x_tf, compute_full_loss=False)
y_tf_stirling = nn_impl.log_poisson_loss(
z_np, x_tf, compute_full_loss=True)
err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf,
x_shape)
err_stirling = gradient_checker.compute_gradient_error(
x_tf, x_shape, y_tf_stirling, x_shape)
eps = 1e-6
self.assertLess(err, eps)
self.assertLess(err_stirling, eps)
def testGradientDilatedConv(self):
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
for padding in ["SAME", "VALID"]:
for stride in [1, 2]:
np.random.seed(1)
in_shape = [5, 8, 6, 4]
in_val = constant_op.constant(
2 * np.random.random_sample(in_shape) - 1, dtype=dtypes.float32)
filter_shape = [3, 3, 4, 6]
# Make a convolution op with the current settings,
# just to easily get the shape of the output.
conv_out = nn_ops.conv2d(
in_val,
array_ops.zeros(filter_shape),
dilations=[1, 2, 2, 1],
strides=[1, stride, stride, 1],
padding=padding)
out_backprop_shape = conv_out.get_shape().as_list()
out_backprop_val = constant_op.constant(
2 * np.random.random_sample(out_backprop_shape) - 1,
dtype=dtypes.float32)
output = nn_ops.conv2d_backprop_filter(
in_val,
filter_shape,
out_backprop_val,
dilations=[1, 2, 2, 1],
strides=[1, stride, stride, 1],
padding=padding)
err = gradient_checker.compute_gradient_error(
[in_val, out_backprop_val], [in_shape, out_backprop_shape],
output, filter_shape)
print("conv2d_backprop_filter gradient err = %g " % err)
err_tolerance = 2e-3
self.assertLess(err, err_tolerance)
def testGradient(self):
with self.test_session() as sess:
l = constant_op.constant(
[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.5],
shape=[3, 4],
dtype=dtypes.float64,
name="l")
f = constant_op.constant(
[0.1, 0.2, 0.3, 0.4, 0.1, 0.4, 0.9, 1.6, 0.1, 0.8, 2.7, 6.4],
shape=[3, 4],
dtype=dtypes.float64,
name="f")
x = nn_ops.softmax_cross_entropy_with_logits(
labels=l, logits=f, name="xent")
err = gradient_checker.compute_gradient_error(f, [3, 4], x, [3])
# Check that no extra computation performed. When only first derivative is requested,
# second derivative must not be computed. So when there is no second derivative,
# there is no `BatchMatMul` op in the graph.
op_names = [
op.op_def.name for op in sess.graph.get_operations() if op.op_def
]
self.assertNotIn("BatchMatMul", op_names)
print("cross entropy gradient err = ", err)
self.assertLess(err, 5e-8)
def testDifferentTensorShapesThroughGradientError(self):
pseudo_random = True
overlapping = True
pooling_ratio = [1, math.sqrt(3), math.sqrt(2), 1]
for num_batches in [1, 2]:
for num_rows in [5, 13]:
for num_cols in [5, 11]:
for num_channels in [1, 3]:
input_shape = (num_batches, num_rows, num_cols, num_channels)
input_data = self._GenerateUniqueRandomInputTensor(input_shape)
# Add some randomness to make input_data not so 'integer'
input_data += self._PRNG.random_sample(input_shape)
with self.cached_session() as _:
input_tensor = constant_op.constant(input_data, shape=input_shape)
output_tensor, unused_a, unused_b = nn_ops.fractional_max_pool_v2(
input_tensor,
pooling_ratio,
pseudo_random=pseudo_random,
overlapping=overlapping,
seed=self._SEED)
output_data = self.evaluate(output_tensor)
output_shape = output_data.shape
# error_margin and delta setting is similar to max_pool_grad.
error_margin = 1e-3
gradient_error = gradient_checker.compute_gradient_error(
input_tensor,
input_shape,
output_tensor,
output_shape,
x_init_value=input_data.reshape(input_shape),
delta=1e-2)
self.assertLess(gradient_error, error_margin)
def testGradientsAxis0(self):
for shape in (2,), (3,), (2, 3), (3, 2), (4, 3, 2):
data = np.random.randn(*shape)
shapes = [shape[1:]] * shape[0]
for i in xrange(shape[0]):
with self.test_session(use_gpu=True):
x = constant_op.constant(data)
cs = array_ops.unpack(x, num=shape[0])
err = gradient_checker.compute_gradient_error(x, shape, cs[i],
shapes[i])
self.assertLess(err, 1e-6)
cs = array_ops.unstack(x, num=shape[0])
err = gradient_checker.compute_gradient_error(x, shape, cs[i],
shapes[i])
self.assertLess(err, 1e-6)
def testSecondGradient(self):
with self.test_session() as sess:
l = constant_op.constant(
[
0.0, 0.0, 1.0 / 3, 0.0, 1.0 / 3, 0.0, 0.0, 0.0, 0.0, 0.5 / 3, 0.0,
0.5 / 3
],
shape=[12],
dtype=dtypes.float64,
name="l")
f = constant_op.constant(
[0.1, 0.2, 0.3, 0.4, 0.1, 0.4, 0.9, 1.6, 0.1, 0.8, 2.7, 6.4],
shape=[12],
dtype=dtypes.float64,
name="f")
x = nn_ops.softmax_cross_entropy_with_logits(
labels=l, logits=f, name="xent")
loss = math_ops.reduce_sum(x)
gradients = gradients_impl.gradients(loss, [f])[0]
err = gradient_checker.compute_gradient_error(f, [12], gradients, [12])
# Check that second derivative is calculated.
# (it is equivalent to being `BatchMatMul` op in the graph because of implementation of xentropy grad)
op_names = [
op.op_def.name for op in sess.graph.get_operations() if op.op_def
]
self.assertIn("BatchMatMul", op_names)
print("cross entropy hessian err = ", err)
self.assertLess(err, 5e-8)
def run_test(self, x, y):
with self.test_session():
error = gradient_checker.compute_gradient_error(x,
x.get_shape().as_list(),
y,
y.get_shape().as_list())
self.assertLess(error, 1e-3)
def _test_grad_different_shape(self, input_shape, output_shape):
with self.cached_session():
test_image_shape = input_shape
test_image = np.random.randn(*test_image_shape)
test_image_tensor = constant_op.constant(
test_image, shape=test_image_shape)
test_transform = image_ops.angles_to_projective_transforms(
np.pi / 2, 4, 4)
if len(output_shape) == 2:
resize_shape = output_shape
elif len(output_shape) == 3:
resize_shape = output_shape[0:2]
elif len(output_shape) == 4:
resize_shape = output_shape[1:3]
output = image_ops.transform(
images=test_image_tensor,
transforms=test_transform,
output_shape=resize_shape)
left_err = gradient_checker.compute_gradient_error(
test_image_tensor,
test_image_shape,
output,
output_shape,
x_init_value=test_image)
self.assertLess(left_err, 1e-10)
def testAllInputOptionsThroughGradientError(self):
input_shape = (1, 7, 13, 1)
input_data = self._GenerateUniqueRandomInputTensor(input_shape)
# Add some randomness to make input_data not so 'integer'
input_data += self._PRNG.random_sample(input_shape)
pooling_ratio = [1, math.sqrt(2), math.sqrt(3), 1]
for pseudo_random in True, False:
for overlapping in True, False:
with self.cached_session() as _:
input_tensor = constant_op.constant(input_data, shape=input_shape)
output_tensor, unused_a, unused_b = nn_ops.fractional_max_pool_v2(
input_tensor,
pooling_ratio,
pseudo_random=pseudo_random,
overlapping=overlapping,
seed=self._SEED)
output_data = self.evaluate(output_tensor)
output_shape = output_data.shape
# error_margin and delta setting is similar to max_pool_grad.
error_margin = 1e-3
gradient_error = gradient_checker.compute_gradient_error(
input_tensor,
input_shape,
output_tensor,
output_shape,
x_init_value=input_data.reshape(input_shape),
delta=1e-2)
self.assertLess(gradient_error, error_margin)
def testSequenceToSequenceGradient(self):
with self.test_session():
size = (17, 1, 15)
output_size = (17, 1, 8)
inputs = constant_op.constant(_rand(*size))
outputs = lstm1d.ndlstm_base(inputs, 8, dynamic=False)
variables.global_variables_initializer().run()
gradients = gradients_impl.gradients(outputs, inputs)
if 1: # pylint: disable=using-constant-test
gradients = gradients_impl.gradients(outputs, inputs)[0].eval()
self.assertEqual(gradients.shape, size)
else:
# TODO (tmb) tf.test.compute_gradient error is currently broken id:911 gh:912
# with dynamic_rnn. Enable this test case eventually.
err = gradient_checker.compute_gradient_error(
inputs, size, outputs, output_size, delta=1e-4)
self.assert_(not np.isnan(err))
self.assert_(err < 0.1)
def testGradGradGrad(self):
with self.cached_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5],
name="x")
y = nn_ops.softplus(x, name="softplus")
(grad, ) = gradients_impl.gradients(y, x)
(grad_grad, ) = gradients_impl.gradients(grad, x)
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float32,
order="F")
err = gradient_checker.compute_gradient_error(x, [2, 5],
grad_grad, [2, 5],
x_init_value=x_init)
print("softplus (float) third-order gradient err = ", err)
self.assertLess(err, 5e-5)
def testGradient(self):
x_shape = [2, 3, 4, 3, 2]
f_shape = [3, 3, 3, 2, 2]
y_shape = [2, 6, 8, 6, 2]
strides = [1, 2, 2, 2, 1]
np.random.seed(1) # Make it reproducible.
x_val = np.random.random_sample(x_shape).astype(np.float64)
f_val = np.random.random_sample(f_shape).astype(np.float64)
with self.test_session(), self.test_scope():
x = constant_op.constant(x_val, name="x", dtype=dtypes.float32)
f = constant_op.constant(f_val, name="f", dtype=dtypes.float32)
output = nn_ops.conv3d_transpose(
x, f, y_shape, strides=strides, padding="SAME")
err = gradient_checker.compute_gradient_error([x, f], [x_shape, f_shape],
output, y_shape)
print("conv3d_transpose gradient err = %g " % err)
err_tolerance = 0.0005
self.assertLess(err, err_tolerance)
def _testGrad(self, shape, dtype=None, max_error=None, bias=None, sigma=None):
np.random.seed(7)
if dtype in (dtypes.complex64, dtypes.complex128):
value = math_ops.complex(
self._biasedRandN(
shape, bias=bias, sigma=sigma),
self._biasedRandN(
shape, bias=bias, sigma=sigma))
else:
value = ops.convert_to_tensor(
self._biasedRandN(
shape, bias=bias), dtype=dtype)
with self.test_session(use_gpu=True):
output = math_ops.abs(value)
error = gradient_checker.compute_gradient_error(
value, shape, output, output.get_shape().as_list())
self.assertLess(error, max_error)
def _test_grad(self, shape_to_test):
with self.cached_session():
test_image_shape = shape_to_test
test_image = np.random.randn(*test_image_shape)
test_image_tensor = constant_op.constant(test_image,
shape=test_image_shape)
test_transform = image_ops.angles_to_projective_transforms(
np.pi / 2, 4, 4)
output_shape = test_image_shape
output = image_ops.transform(test_image_tensor, test_transform)
left_err = gradient_checker.compute_gradient_error(
test_image_tensor,
test_image_shape,
output,
output_shape,
x_init_value=test_image)
self.assertLess(left_err, 1e-10)
def testGradFromResizeToSmallerInBothDims(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape).astype(np.float32)
for align_corners in [True, False]:
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bicubic(input_tensor,
out_shape[1:3],
align_corners=align_corners)
with self.cached_session():
err = gradient_checker.compute_gradient_error(input_tensor,
in_shape,
resize_out,
out_shape,
x_init_value=x)
self.assertLess(err, 1e-3)
def testGradientLabelWithV2(self):
with self.test_session():
l = constant_op.constant(
[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.5],
shape=[3, 4],
dtype=dtypes.float64,
name="l")
f = constant_op.constant(
[0.1, 0.2, 0.3, 0.4, 0.1, 0.4, 0.9, 1.6, 0.1, 0.8, 2.7, 6.4],
shape=[3, 4],
dtype=dtypes.float64,
name="f")
x = nn_ops.softmax_cross_entropy_with_logits_v2(labels=l,
logits=f,
name="xent")
err = gradient_checker.compute_gradient_error(l, [3, 4], x, [3])
self.assertLess(err, 5e-8)
def testGradGradFloat64(self):
with self.test_session():
x = constant_op.constant(
[-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
shape=[2, 5],
dtype=dtypes.float64,
name="x")
y = nn_ops.relu(x, name="relu")
z = gradients_impl.gradients(y, x)
x_init = np.asarray(
[[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
dtype=np.float64,
order="F")
err = gradient_checker.compute_gradient_error(x, [2, 5],
z[0], [2, 5],
x_init_value=x_init)
print("relu (float64) gradient of gradient err = ", err)
self.assertLess(err, 1e-10)
def testGradientsEmbeddingLookupWithComputedParams(self):
vocab_size = 9
num_ids = 5
id_vals = list(np.random.randint(vocab_size, size=num_ids))
tf_logging.vlog(1, id_vals)
for num_shards in [1, 3]:
with self.cached_session():
ids = constant_op.constant(id_vals, dtype=dtypes.int32)
x, params, _ = _EmbeddingParams(num_shards, vocab_size, shape=[2])
# This will force a conversion from IndexedSlices to Tensor.
x_squared = [math_ops.square(elem) for elem in x]
y = embedding_ops.embedding_lookup(x_squared, ids)
y_shape = [num_ids] + list(params[_PName(0) + ":0"].shape[1:])
x_name = [_PName(i) for i in range(num_shards)]
x_init_value = [params[x_n + ":0"] for x_n in x_name]
x_shape = [i.shape for i in x_init_value]
err = gradient_checker.compute_gradient_error(
x, x_shape, y, y_shape, x_init_value=x_init_value)
self.assertLess(err, 1e-3)
def testGradientsEmbeddingLookup(self):
vocab_size = 9
num_ids = 10
id_vals = list(np.random.randint(vocab_size, size=num_ids))
tf_logging.vlog(1, id_vals)
for ids_shape in [(10,), (2, 5)]:
for num_shards in [1, 3]:
with self.cached_session():
ids = constant_op.constant(
id_vals, shape=ids_shape, dtype=dtypes.int32)
x, params, _ = _EmbeddingParams(num_shards, vocab_size, shape=[2])
y = embedding_ops.embedding_lookup(x, ids)
y_shape = ids_shape + tuple(params[_PName(0) + ":0"].shape[1:])
x_name = [_PName(i) for i in range(num_shards)]
x_init_value = [params[x_n + ":0"] for x_n in x_name]
x_shape = [i.shape for i in x_init_value]
err = gradient_checker.compute_gradient_error(
x, x_shape, y, y_shape, x_init_value=x_init_value)
self.assertLess(err, 1e-4)
def _test_gradient_against_estimate(self, dtype, random, use_gpu):
"""check sparsemax-loss Rop, against estimated-loss Rop"""
z = random.uniform(low=-3, high=3, size=(test_obs, 10)).astype(dtype)
q = np.zeros((test_obs, 10)).astype(dtype)
q[np.arange(0, test_obs), np.random.randint(0, 10, size=test_obs)] = 1
logits = array_ops.placeholder(dtype, name='z')
sparsemax_op = sparsemax(logits)
loss_op = sparsemax_loss(logits, sparsemax_op, q)
with self.test_session(use_gpu=use_gpu):
err = gradient_checker.compute_gradient_error(logits,
z.shape,
loss_op,
(test_obs, ),
x_init_value=z,
delta=1e-9)
self.assertLess(err, 1e-4)
def testIndexedSlicesConcatDim1Grad_UnknownInputDim(self):
x_shapes = [[20, 7, 3], [20, 3, 3], [20, 1, 3]]
output_shape = [4, 11, 3]
with self.test_session():
x_1 = array_ops.placeholder(dtypes.float64)
x_2 = array_ops.placeholder(dtypes.float64)
x_3 = array_ops.placeholder(dtypes.float64)
xs = [x_1, x_2, x_3]
x_concat = array_ops.concat(xs, 1)
output = array_ops.gather(x_concat, [1, 2, 0, 5])
params = {
x_1: np.random.random_sample(x_shapes[0]).astype(np.float64),
x_2: np.random.random_sample(x_shapes[1]).astype(np.float64),
x_3: np.random.random_sample(x_shapes[2]).astype(np.float64)
}
err = gradient_checker.compute_gradient_error(
xs, x_shapes, output, output_shape, extra_feed_dict=params)
self.assertLess(err, 1e-11)
def testGradientDilatedConv(self):
if test.is_gpu_available(cuda_only=True):
with self.session():
for padding in [
"SAME", "VALID", [(0, 0), (3, 5), (2, 1), (0, 0)],
[(0, 0), (5, 2), (5, 1), (0, 0)]
]:
for stride in [1, 2]:
np.random.seed(1)
in_shape = [5, 8, 6, 4]
in_val = constant_op.constant(
2 * np.random.random_sample(in_shape) - 1,
dtype=dtypes.float32)
filter_shape = [3, 3, 4, 6]
# Make a convolution op with the current settings,
# just to easily get the shape of the output.
conv_out = nn_ops.conv2d(
in_val,
array_ops.zeros(filter_shape),
dilations=[1, 2, 2, 1],
strides=[1, stride, stride, 1],
padding=padding)
out_backprop_shape = conv_out.get_shape().as_list()
out_backprop_val = constant_op.constant(
2 * np.random.random_sample(out_backprop_shape) -
1,
dtype=dtypes.float32)
output = nn_ops.conv2d_backprop_filter(
in_val,
filter_shape,
out_backprop_val,
dilations=[1, 2, 2, 1],
strides=[1, stride, stride, 1],
padding=padding)
err = gradient_checker.compute_gradient_error(
[in_val, out_backprop_val],
[in_shape, out_backprop_shape], output,
filter_shape)
print("conv2d_backprop_filter gradient err = %g " %
err)
err_tolerance = 1e-2
self.assertLess(err, err_tolerance)
def _impl_test_batch_major_fo_pool_grad(self, FT, tolerance):
shape = (3, 5, 2)
np_args = [
np.random.random(size=shape).astype(FT),
np.random.uniform(0, 1, size=shape).astype(FT),
np.random.random(size=(shape[0], shape[-1])).astype(FT)
]
with tf.Session() as S:
tf_args = [
constant_op.constant(arg, shape=arg.shape, dtype=FT)
for arg in np_args
]
y = tf.reduce_sum(batch_major_fo_pool_unsliced(*tf_args))
for d in ["cpu"] + self.gpu_devs:
with tf.device(d):
err = gradient_checker.compute_gradient_error(
tf_args, [arg.shape for arg in np_args],
y, [],
x_init_value=np_args)
self.assertLess(err, tolerance)
def testNestedGather(self):
np.random.seed(5) # Fix seed to avoid flakiness
with self.cached_session():
p_shape = (8, 2)
p_size = 16
index_values = [1, 3, 5, 6]
index_values2 = [0, 2]
y2_shape = [2, 2]
params = constant_op.constant(
np.arange(p_size).astype(np.float), shape=p_shape, name="p")
indices = constant_op.constant(index_values, name="i")
y = array_ops.gather(params, indices, name="y")
indices2 = constant_op.constant(index_values2, name="i2")
y2 = array_ops.gather(y, indices2, name="y2")
error = gradient_checker.compute_gradient_error(params, p_shape, y2,
y2_shape)
tf_logging.info("nested gather error = %f", error)
self.assertLess(error, 1e-4)
def testGradient(self, in_shape, ksizes, strides):
# Set graph seed for determinism.
random_seed = 42
random_seed_lib.set_random_seed(random_seed)
with self.cached_session():
np.random.seed(random_seed)
in_val = constant_op.constant(np.random.random(in_shape),
dtype=dtypes.float32)
for padding in ['VALID', 'SAME']:
out_val = array_ops.extract_volume_patches(
in_val, ksizes, strides, padding)
out_shape = out_val.get_shape().as_list()
err = gradient_checker.compute_gradient_error(
in_val, in_shape, out_val, out_shape)
print('extract_volume_patches gradient err: %.4e' % err)
self.assertLess(err, 1e-4)
def _testGradient(self, x_value_list, x_shape, lattice_sizes, y_shape,
is_hypercube):
"""Compute the numerical gradients, and check the error."""
for x_value in x_value_list:
with self.test_session(use_gpu=False):
x = array_ops.placeholder(dtype=dtypes.float32,
shape=x_shape,
name="x")
x_init_value = np.asarray(x_value, dtype=np.float32)
if is_hypercube:
y = lattice_ops.hypercube_interpolation(
x, lattice_sizes=lattice_sizes)
else:
y = lattice_ops.simplex_interpolation(
x, lattice_sizes=lattice_sizes)
error = gradient_checker.compute_gradient_error(
x, x_shape, y, y_shape, x_init_value=x_init_value)
tf_logging.info("x_init_value = %s" % x_init_value)
tf_logging.info("x error = %f", error)
self.assertTrue(error < 1e-4)
def _testBatchNormGradient(self,
param_index,
tag,
scale_after_normalization,
shift_after_normalization,
version,
err_tolerance=1e-11):
x_shape = [3, 5, 4, 5]
param_shape = [5]
np.random.seed(1) # Make it reproducible.
x_val = np.random.random_sample(x_shape).astype(np.float64)
m_val = np.random.random_sample(param_shape).astype(np.float64)
v_val = np.random.random_sample(param_shape).astype(np.float64)
beta_val = np.random.random_sample(param_shape).astype(np.float64)
gamma_val = np.random.random_sample(param_shape).astype(np.float64)
with self.test_session():
x = constant_op.constant(x_val, name="x")
m = constant_op.constant(m_val, name="m")
v = constant_op.constant(v_val, name="v")
beta = constant_op.constant(beta_val, name="beta")
gamma = constant_op.constant(gamma_val, name="gamma")
epsilon = 0.001
if version == 1:
output = self._tfBatchNormV1(x, m, v, beta, gamma, epsilon,
scale_after_normalization)
elif version == 2:
output = self._tfBatchNormV2(x, m, v, beta, gamma, epsilon,
scale_after_normalization,
shift_after_normalization)
else:
print("Invalid version", version)
raise ValueError()
all_params = [x, m, v, beta, gamma]
all_shapes = [x_shape, param_shape, param_shape, param_shape, param_shape]
err = gradient_checker.compute_gradient_error(all_params[param_index],
all_shapes[param_index],
output, x_shape)
print("Batch normalization v%d %s gradient %s scale and %s shift err = " %
(version, tag, "with" if scale_after_normalization else "without",
"with" if shift_after_normalization else "without"), err)
self.assertLess(err, err_tolerance)
def _VerifyInput1(self, transpose_a, transpose_b):
shape_x = [3, 2]
shape_y = [2, 4]
if transpose_a:
shape_x = list(reversed(shape_x))
if transpose_b:
shape_y = list(reversed(shape_y))
with self.test_session(use_gpu=False):
x = constant_op.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
shape=shape_x,
dtype=dtypes.float64,
name="x")
y = constant_op.constant([1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7],
shape=shape_y,
dtype=dtypes.float64,
name="y")
m = math_ops.matmul(x, y, transpose_a, transpose_b, name="matmul")
err = gradient_checker.compute_gradient_error(
y, shape_y, m, [3, 4])
print("matmul input1 gradient err = ", err)
self.assertLess(err, 1e-10)
def testGradient(self):
# Set graph seed for determinism.
random_seed = 42
random_seed_lib.set_random_seed(random_seed)
with self.cached_session():
for test_case in self._TEST_CASES:
np.random.seed(random_seed)
in_shape = test_case['in_shape']
in_val = constant_op.constant(np.random.random(in_shape),
dtype=dtypes.float32)
for padding in ['VALID', 'SAME']:
out_val = array_ops.extract_image_patches(
in_val, test_case['ksizes'], test_case['strides'],
test_case['rates'], padding)
out_shape = out_val.get_shape().as_list()
err = gradient_checker.compute_gradient_error(
in_val, in_shape, out_val, out_shape)
self.assertLess(err, 1e-4)
def testGradient(self):
with self.cached_session():
for padding in [
"SAME",
"VALID",
[(0, 0), (1, 2), (3, 4), (0, 0)],
[(0, 0), (0, 3), (4, 2), (0, 0)]
]:
for stride in [1, 2]:
np.random.seed(1)
in_shape = [5, 8, 6, 4]
in_val = constant_op.constant(
2 * np.random.random_sample(in_shape) - 1, dtype=dtypes.float32)
filter_shape = [3, 3, 4, 6]
# Make a convolution op with the current settings, just to easily get
# the shape of the output.
conv_out = nn_ops.conv2d(
in_val,
array_ops.zeros(filter_shape),
strides=[1, stride, stride, 1],
padding=padding)
out_backprop_shape = conv_out.get_shape().as_list()
out_backprop_val = constant_op.constant(
2 * np.random.random_sample(out_backprop_shape) - 1,
dtype=dtypes.float32)
output = nn_ops.conv2d_backprop_filter(
in_val,
filter_shape,
out_backprop_val,
strides=[1, stride, stride, 1],
padding=padding)
err = gradient_checker.compute_gradient_error(
[in_val, out_backprop_val], [in_shape, out_backprop_shape],
output, filter_shape)
print("conv2d_backprop_filter gradient err = %g " % err)
err_tolerance = 3e-2 if test.is_gpu_available() else 2e-3
self.assertLess(
err,
err_tolerance,
msg="padding={0},stride={1},".format(str(padding), stride))
def testGradient(self):
with self.session(use_gpu=True) as sess:
l = constant_op.constant([3, 0, 1], name="l")
f = constant_op.constant(
[0.1, 0.2, 0.3, 0.4, 0.1, 0.4, 0.9, 1.6, 0.1, 0.8, 2.7, 6.4],
shape=[3, 4],
dtype=dtypes.float64,
name="f")
x = nn_ops.sparse_softmax_cross_entropy_with_logits(
labels=l, logits=f, name="xent")
err = gradient_checker.compute_gradient_error(f, [3, 4], x, [3])
# Check that no extra computation performed. When only first derivative is
# requested, second derivative must not be computed. So when there is no
# second derivative, there is no `BatchMatMul` op in the graph.
op_names = [
op.op_def.name for op in sess.graph.get_operations() if op.op_def
]
self.assertNotIn("BatchMatMul", op_names)
self.assertNotIn("BatchMatMulV2", op_names)
self.assertLess(err, 5e-8)
def testGradient(self):
with self.session():
# Input: [batch, height, width, input_depth]
x_shape = [2, 5, 6, 2]
# Filter: [kernel_height, kernel_width, input_depth, output_depth]
f_shape = [3, 3, 2, 2]
# Output: [batch, height, width, output_depth]
y_shape = [2, 5, 6, 2]
np.random.seed(1) # Make it reproducible.
x_val = np.random.random_sample(x_shape).astype(np.float32)
f_val = np.random.random_sample(f_shape).astype(np.float32)
x = constant_op.constant(x_val, name="x", dtype=dtypes.float32)
f = constant_op.constant(f_val, name="f", dtype=dtypes.float32)
for rate in range(1, 4):
output = nn_ops.atrous_conv2d(x, f, rate=rate, padding="SAME")
err = gradient_checker.compute_gradient_error(
[x, f], [x_shape, f_shape], output, y_shape)
print("atrous_conv2d gradient err = %g " % err)
err_tolerance = 4e-3 if test_util.is_xla_enabled() else 1e-3
self.assertLess(err, err_tolerance)
def _testGradients(self, adjoint_a, adjoint_b, name, values_dtype,
indices_dtype):
n, k, m = np.random.randint(1, 10, size=3)
sp_t, nnz = self._randomTensor(
[n, k],
values_dtype,
adjoint=adjoint_a,
sparse=True,
indices_dtype=indices_dtype)
dense_t = self._randomTensor([k, m], values_dtype, adjoint=adjoint_b)
matmul = sparse_ops.sparse_tensor_dense_matmul(
sp_t, dense_t, adjoint_a=adjoint_a, adjoint_b=adjoint_b, name=name)
with self.cached_session():
dense_t_shape = [m, k] if adjoint_b else [k, m]
sp_t_val_shape = [nnz]
err = gradient_checker.compute_gradient_error(
[dense_t, sp_t.values], [dense_t_shape, sp_t_val_shape], matmul,
[n, m])
print("%s gradient err = %s" % (name, err))
self.assertLess(err, 1e-3)
def testAddCustomized(self):
np.random.seed(3) # Fix seed to avoid flakiness
with self.cached_session():
# a test case for Add operation
size = (2, 3)
x1 = constant_op.constant(2.0,
shape=size,
dtype=dtypes.float64,
name="x1")
x2 = constant_op.constant(3.0,
shape=size,
dtype=dtypes.float64,
name="x2")
y = math_ops.add(x1, x2, name="y")
# checkint gradients for x2 using a special init_value and delta
x_init_value = np.asarray(
np.arange(6, dtype=np.float64).reshape(2, 3))
error = gradient_checker.compute_gradient_error(
x2, size, y, size, x_init_value=x_init_value, delta=1e-2)
tf_logging.info("x2 error = %f", error)
self.assertLess(error, 1e-10)
def test_gradient(device):
if device == "gpu" and visible_gpu():
pytest.xfail("no gpu is visible")
with NumpySeed(100):
with tf.device('/{}:0'.format(device)):
sprites, n_sprites, scales, offsets, backgrounds = get_data(
random_alpha=True, squash=0.99)
sprites_tf = constant_op.constant(sprites)
n_sprites_tf = constant_op.constant(n_sprites)
scales_tf = constant_op.constant(scales)
offsets_tf = constant_op.constant(offsets)
backgrounds_tf = constant_op.constant(backgrounds)
images = render_sprites.render_sprites(sprites_tf, n_sprites_tf,
scales_tf, offsets_tf,
backgrounds_tf)
sess = get_session()
with sess.as_default():
with tf.device(device):
err = gradient_checker.compute_gradient_error(
[sprites_tf, scales_tf, offsets_tf, backgrounds_tf], [
sprites.shape, scales.shape, offsets.shape,
backgrounds.shape
],
images,
backgrounds.shape,
[sprites, scales, offsets, backgrounds],
delta=0.002)
print("Jacobian error: {}".format(err))
threshold = 2e-4
assert err < threshold, "Jacobian error ({}) exceeded threshold ({})".format(
err, threshold)
def testSecondGradient(self):
with self.cached_session() as sess:
l = constant_op.constant([
0.0, 0.0, 1.0 / 3, 0.0, 1.0 / 3, 0.0, 0.0, 0.0, 0.0, 0.5 / 3,
0.0, 0.5 / 3
],
shape=[12],
dtype=dtypes.float64,
name="l")
f = constant_op.constant(
[0.1, 0.2, 0.3, 0.4, 0.1, 0.4, 0.9, 1.6, 0.1, 0.8, 2.7, 6.4],
shape=[12],
dtype=dtypes.float64,
name="f")
x = nn_ops.softmax_cross_entropy_with_logits(labels=l,
logits=f,
name="xent")
loss = math_ops.reduce_sum(x)
gradients = gradients_impl.gradients(loss, [f])[0]
err = gradient_checker.compute_gradient_error(
f, [12], gradients, [12])
# Check that second derivative is calculated.
# (it is equivalent to being `BatchMatMul` op in the graph because of implementation of xentropy grad)
op_names = [
op.op_def.name for op in sess.graph.get_operations()
if op.op_def
]
if compat.forward_compatible(2019, 4, 25):
self.assertIn("BatchMatMulV2", op_names)
else:
self.assertIn("BatchMatMul", op_names)
print("cross entropy hessian err = ", err)
self.assertLess(err, 5e-8)
def testDifferentTensorShapesThroughGradientError(self):
pseudo_random = True
overlapping = True
pooling_ratio = [1, math.sqrt(3), math.sqrt(2), 1]
for num_batches in [1, 2]:
for num_rows in [5, 13]:
for num_cols in [5, 11]:
for num_channels in [1, 3]:
input_shape = (num_batches, num_rows, num_cols,
num_channels)
input_data = self._GenerateUniqueRandomInputTensor(
input_shape)
# Add some randomness to make input_data not so 'integer'
input_data += self._PRNG.random_sample(input_shape)
with self.test_session() as _:
input_tensor = constant_op.constant(
input_data, shape=input_shape)
output_tensor, unused_a, unused_b = nn_ops.fractional_max_pool(
input_tensor,
pooling_ratio,
pseudo_random=pseudo_random,
overlapping=overlapping,
deterministic=True,
seed=self._SEED,
seed2=self._SEED2)
output_data = output_tensor.eval()
output_shape = output_data.shape
# error_margin and delta setting is similar to max_pool_grad.
error_margin = 1e-3
gradient_error = gradient_checker.compute_gradient_error(
input_tensor,
input_shape,
output_tensor,
output_shape,
x_init_value=input_data.reshape(input_shape),
delta=1e-2)
self.assertLess(gradient_error, error_margin)
def _test_grad_different_shape(self, input_shape, output_shape):
with self.cached_session():
test_image_shape = input_shape
test_image = np.random.randn(*test_image_shape)
test_image_tensor = tf.constant(test_image, shape=test_image_shape)
test_transform = transform_ops.angles_to_projective_transforms(
np.pi / 2, 4, 4)
if len(output_shape) == 2:
resize_shape = output_shape
elif len(output_shape) == 3:
resize_shape = output_shape[0:2]
elif len(output_shape) == 4:
resize_shape = output_shape[1:3]
output = transform_ops.transform(images=test_image_tensor,
transforms=test_transform,
output_shape=resize_shape)
left_err = gradient_checker.compute_gradient_error(
test_image_tensor,
test_image_shape,
output,
output_shape,
x_init_value=test_image)
self.assertLess(left_err, 1e-10)
def _RunAndVerifyGradients(self, dtype):
with self.test_session(use_gpu=True):
# random shape
shape = np.random.randint(1, 5, size=4)
# Make depth at least 2 to make it meaningful
shape[3] += 1
# random depth_radius, bias, alpha, beta. cuDNN requires depth_radius to
# be in [1, 7].
lrn_depth_radius = np.random.randint(1, min(8, shape[3]))
bias = 1.0 + np.random.rand()
alpha = 1.0 * np.random.rand()
# cuDNN requires beta >= 0.01.
beta = 0.01 + 1.0 * np.random.rand()
if dtype == dtypes.float32:
inp_array = np.random.rand(*shape).astype(np.float32)
else:
inp_array = np.random.rand(*shape).astype(np.float16)
inp = constant_op.constant(list(inp_array.ravel(order="C")),
shape=shape,
dtype=dtype)
lrn_op = nn.local_response_normalization(
inp,
name="lrn",
depth_radius=lrn_depth_radius,
bias=bias,
alpha=alpha,
beta=beta)
err = gradient_checker.compute_gradient_error(
inp, shape, lrn_op, shape)
print("LRN Gradient error for bias ", bias, "alpha ", alpha, " beta ",
beta, " is ", err)
if dtype == dtypes.float32:
self.assertLess(err, 1e-4)
else:
self.assertLess(err, 1.0)
