def testGain(self):
shape = (3, 10, 10)
for dtype in [dtypes.float32, dtypes.float64]:
init1 = init_ops.convolutional_orthogonal_1d(seed=1, dtype=dtype)
init2 = init_ops.convolutional_orthogonal_1d(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, 10, 10)
for dtype in [dtypes.float32, dtypes.float64]:
init1 = init_ops.convolutional_orthogonal_1d(seed=1, dtype=dtype)
init2 = init_ops.convolutional_orthogonal_1d(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, 10, 10)
for dtype in [dtypes.float32, dtypes.float64]:
init1 = init_ops.convolutional_orthogonal_1d(seed=1, dtype=dtype)
init2 = init_ops.convolutional_orthogonal_1d(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, 10, 10)
for dtype in [dtypes.float32, dtypes.float64]:
init1 = init_ops.convolutional_orthogonal_1d(seed=1, dtype=dtype)
init2 = init_ops.convolutional_orthogonal_1d(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):
def circular_pad(input_, width, kernel_size):
"""Pad input_ for computing (circular) convolution.
Args:
input_: the input tensor
width: the width of the tensor.
kernel_size: the kernel size of the filter.
Returns:
a tensor whose width is (width + kernel_size - 1).
"""
beginning = kernel_size // 2
end = kernel_size - 1 - beginning
tmp_up = array_ops.slice(input_, [0, width - beginning, 0],
[-1, beginning, -1])
tmp_down = array_ops.slice(input_, [0, 0, 0], [-1, end, -1])
tmp = array_ops.concat([tmp_up, input_, tmp_down], 1)
return tmp
cout = 64
shape = [10, 20, 32]
outputs_shape = shape[0:-1] + [cout]
dtype = dtypes.float32
tol = 1e-3
gain = 3.14
# Check orthogonality/isometry by computing the ratio between
# the 2-norms of the inputs and outputs.
for kernel_size in [[1], [2], [3], [4], [5], [6]]:
convolution = convolutional.conv1d
inputs = random_ops.random_normal(shape, dtype=dtype)
inputs_2norm = linalg_ops.norm(inputs)
input_with_circular_pad = circular_pad(inputs, shape[1], kernel_size[0])
outputs = convolution(
input_with_circular_pad, padding="valid", filters=cout,
kernel_size=kernel_size[0], use_bias=False,
kernel_initializer=init_ops.convolutional_orthogonal_1d(gain=gain))
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 orthogonal kernel.
self.assertAllClose(sess.run(ratio), np.sqrt(gain), rtol=tol, atol=tol)
def testShapesValues(self):
def circular_pad(input_, width, kernel_size):
"""Pad input_ for computing (circular) convolution.
Args:
input_: the input tensor
width: the width of the tensor.
kernel_size: the kernel size of the filter.
Returns:
a tensor whose width is (width + kernel_size - 1).
"""
beginning = kernel_size // 2
end = kernel_size - 1 - beginning
tmp_up = array_ops.slice(input_, [0, width - beginning, 0],
[-1, beginning, -1])
tmp_down = array_ops.slice(input_, [0, 0, 0], [-1, end, -1])
tmp = array_ops.concat([tmp_up, input_, tmp_down], 1)
return tmp
cout = 64
shape = [10, 20, 32]
outputs_shape = shape[0:-1] + [cout]
dtype = dtypes.float32
tol = 1e-3
gain = 3.14
# Check orthogonality/isometry by computing the ratio between
# the 2-norms of the inputs and outputs.
for kernel_size in [[1], [2], [3], [4], [5], [6]]:
convolution = convolutional.conv1d
inputs = random_ops.random_normal(shape, dtype=dtype)
inputs_2norm = linalg_ops.norm(inputs)
input_with_circular_pad = circular_pad(inputs, shape[1], kernel_size[0])
outputs = convolution(
input_with_circular_pad, padding="valid", filters=cout,
kernel_size=kernel_size[0], use_bias=False,
kernel_initializer=init_ops.convolutional_orthogonal_1d(gain=gain))
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 orthogonal kernel.
self.assertAllClose(sess.run(ratio), np.sqrt(gain), rtol=tol, atol=tol)
def testInvalidShape(self):
init1 = init_ops.convolutional_orthogonal_1d()
with self.test_session(graph=ops.Graph(), use_gpu=True):
self.assertRaises(ValueError, init1, shape=[3, 6, 5])
def testDuplicatedInitializer(self): init = init_ops.convolutional_orthogonal_1d() self.assertFalse(duplicated_initializer(self, init, 1, (3, 10, 10)))
def testInitializerDifferent(self):
for dtype in [dtypes.float32, dtypes.float64]:
init1 = init_ops.convolutional_orthogonal_1d(seed=1, dtype=dtype)
init2 = init_ops.convolutional_orthogonal_1d(seed=2, dtype=dtype)
self.assertFalse(identicaltest(self, init1, init2, (3, 10, 10)))
def testInvalidShape(self):
init1 = init_ops.convolutional_orthogonal_1d()
with self.test_session(graph=ops.Graph(), use_gpu=True):
self.assertRaises(ValueError, init1, shape=[3, 6, 5])
def testDuplicatedInitializer(self): init = init_ops.convolutional_orthogonal_1d() self.assertFalse(duplicated_initializer(self, init, 1, (3, 10, 10)))
def testInitializerDifferent(self):
for dtype in [dtypes.float32, dtypes.float64]:
init1 = init_ops.convolutional_orthogonal_1d(seed=1, dtype=dtype)
init2 = init_ops.convolutional_orthogonal_1d(seed=2, dtype=dtype)
self.assertFalse(identicaltest(self, init1, init2, (3, 10, 10)))
