def testInitializerDifferent(self):
for dtype in [dtypes.float32, dtypes.float64]:
init1 = init_ops.convolutional_delta_orthogonal(seed=1,
dtype=dtype)
init2 = init_ops.convolutional_delta_orthogonal(seed=2,
dtype=dtype)
self.assertFalse(identicaltest(self, init1, init2, (3, 3, 10, 10)))
def testGain(self):
shape = (3, 3, 10, 10)
for dtype in [dtypes.float32, dtypes.float64]:
init1 = init_ops.convolutional_delta_orthogonal(seed=1, dtype=dtype)
init2 = init_ops.convolutional_delta_orthogonal(gain=3.14,
seed=1, dtype=dtype)
with self.test_session(graph=ops.Graph(), use_gpu=True):
t1 = init1(shape).eval()
t2 = init2(shape).eval()
return np.allclose(t1, t2 / 3.14, rtol=1e-15, atol=1e-15)
def testGain(self):
shape = (3, 3, 10, 10)
for dtype in [dtypes.float32, dtypes.float64]:
init1 = init_ops.convolutional_delta_orthogonal(seed=1, dtype=dtype)
init2 = init_ops.convolutional_delta_orthogonal(gain=3.14,
seed=1, dtype=dtype)
with self.session(graph=ops.Graph(), use_gpu=True):
t1 = init1(shape).eval()
t2 = init2(shape).eval()
self.assertAllClose(t1, t2 / 3.14)
def testGain(self):
shape = (3, 3, 10, 10)
for dtype in [dtypes.float32, dtypes.float64]:
init1 = init_ops.convolutional_delta_orthogonal(seed=1, dtype=dtype)
init2 = init_ops.convolutional_delta_orthogonal(gain=3.14,
seed=1, dtype=dtype)
with self.test_session(graph=ops.Graph(), use_gpu=True):
t1 = init1(shape).eval()
t2 = init2(shape).eval()
return np.allclose(t1, t2 / 3.14, rtol=1e-15, atol=1e-15)
def testGain(self):
shape = (3, 3, 10, 10)
for dtype in [dtypes.float32, dtypes.float64]:
init1 = init_ops.convolutional_delta_orthogonal(seed=1, dtype=dtype)
init2 = init_ops.convolutional_delta_orthogonal(gain=3.14,
seed=1, dtype=dtype)
with self.session(graph=ops.Graph(), use_gpu=True):
t1 = init1(shape).eval()
t2 = init2(shape).eval()
self.assertAllClose(t1, t2 / 3.14)
def testShapesValues(self):
gain = 3.14
for dtype in [dtypes.float32]:
for kernel_size in [[3], [8], [3, 5], [2, 4], [3, 3, 3], [2, 2, 2]]:
tol = 1e-2
# Check orthogonality by computing ratio between
# the 2-norms of the inputs and outputs.
if len(kernel_size) == 1:
shape = [4, 32, 64]
convolution = convolutional.conv1d
elif len(kernel_size) == 2:
convolution = convolutional.conv2d
shape = [4, 32, 32, 64]
else:
shape = [4, 16, 16, 16, 64]
convolution = convolutional.conv3d
inputs = random_ops.random_normal(shape, dtype=dtype)
inputs_2norm = linalg_ops.norm(inputs)
outputs = convolution(
inputs, padding="same", filters=128,
kernel_size=kernel_size, use_bias=False,
kernel_initializer=init_ops.convolutional_delta_orthogonal(
gain=gain))
outputs_shape = shape[0:-1] + [128]
outputs_2norm = linalg_ops.norm(outputs)
ratio = outputs_2norm / inputs_2norm
my_ops = variables.global_variables_initializer()
with self.test_session(use_gpu=True) as sess:
sess.run(my_ops)
# Check the shape of the outputs
t = outputs.eval()
self.assertAllEqual(t.shape, outputs_shape)
# Check isometry of the delta-orthogonal kernel.
self.assertAllClose(sess.run(ratio), np.sqrt(gain),
rtol=tol, atol=tol)
def testShapesValues(self):
for dtype in [dtypes.float32]:
for kernel_size in [[3], [8], [3, 5], [2, 4], [3, 3, 3], [2, 2, 2]]:
tol = 1e-2
# Check orthogonality by computing the 2-norms of the inputs and outputs.
if len(kernel_size) == 1:
shape = [4, 32, 64]
convolution = convolutional.conv1d
elif len(kernel_size) == 2:
convolution = convolutional.conv2d
shape = [4, 32, 32, 64]
else:
shape = [4, 16, 16, 16, 64]
convolution = convolutional.conv3d
inputs = random_ops.random_normal(shape, dtype=dtype)
inputs_2norm = linalg_ops.norm(inputs)
outputs = convolution(
inputs, padding="same", filters=128,
kernel_size=kernel_size, use_bias=False,
kernel_initializer=init_ops.convolutional_delta_orthogonal(
gain=3.14))
outputs_shape = shape[0:-1] + [128]
outputs_2norm = linalg_ops.norm(outputs)
my_ops = variables.global_variables_initializer()
with self.test_session(use_gpu=True) as sess:
sess.run(my_ops)
# Check the shape of the outputs
t = outputs.eval()
self.assertAllEqual(t.shape, outputs_shape)
# Check isometry of the delta-orthogonal kernel.
self.assertAllClose(
sess.run(inputs_2norm)/np.sqrt(np.prod(shape)),
sess.run(outputs_2norm)/(np.sqrt(np.prod(shape))*np.sqrt(3.14)),
rtol=tol, atol=tol)
def testShapesValues(self):
gain = 3.14
for dtype in [dtypes.float32]:
for kernel_size in [[3], [8], [3, 5], [2, 4], [3, 3, 3], [2, 2, 2]]:
tol = 1e-2
# Check orthogonality by computing ratio between
# the 2-norms of the inputs and outputs.
if len(kernel_size) == 1:
shape = [4, 32, 64]
convolution = convolutional.conv1d
elif len(kernel_size) == 2:
convolution = convolutional.conv2d
shape = [4, 32, 32, 64]
else:
shape = [4, 16, 16, 16, 64]
convolution = convolutional.conv3d
if test.is_built_with_rocm():
# 5D tensors are not yet supported in ROCm
continue
inputs = random_ops.random_normal(shape, dtype=dtype)
inputs_2norm = linalg_ops.norm(inputs)
outputs = convolution(
inputs, padding="same", filters=128,
kernel_size=kernel_size, use_bias=False,
kernel_initializer=init_ops.convolutional_delta_orthogonal(
gain=gain))
outputs_shape = shape[0:-1] + [128]
outputs_2norm = linalg_ops.norm(outputs)
ratio = outputs_2norm / inputs_2norm
my_ops = variables.global_variables_initializer()
with self.test_session(use_gpu=True) as sess:
sess.run(my_ops)
# Check the shape of the outputs
t = outputs.eval()
self.assertAllEqual(t.shape, outputs_shape)
# Check isometry of the delta-orthogonal kernel.
self.assertAllClose(sess.run(ratio), np.sqrt(gain),
rtol=tol, atol=tol)
def testInvalidShape(self):
init1 = init_ops.convolutional_delta_orthogonal()
with self.test_session(graph=ops.Graph(), use_gpu=True):
self.assertRaises(ValueError, init1, shape=[3, 3, 6, 5])
def testDuplicatedInitializer(self): init = init_ops.convolutional_delta_orthogonal() self.assertFalse(duplicated_initializer(self, init, 1, (3, 3, 10, 10)))
def testInitializerDifferent(self):
for dtype in [dtypes.float32, dtypes.float64]:
init1 = init_ops.convolutional_delta_orthogonal(seed=1, dtype=dtype)
init2 = init_ops.convolutional_delta_orthogonal(seed=2, dtype=dtype)
self.assertFalse(identicaltest(self, init1, init2, (3, 3, 10, 10)))
def testInvalidShape(self):
init1 = init_ops.convolutional_delta_orthogonal()
with self.test_session(graph=ops.Graph(), use_gpu=True):
self.assertRaises(ValueError, init1, shape=[3, 3, 6, 5])
def testDuplicatedInitializer(self):
init = init_ops.convolutional_delta_orthogonal()
self.assertFalse(duplicated_initializer(self, init, 1, (3, 3, 10, 10)))
