Adding ConvLinearLayer test functions.
This commit is contained in:
Matt Graham
parent
162e28b498
commit
eef54933c1
295
mlp/utils.py
295
mlp/utils.py
@ -64,3 +64,298 @@ def verify_layer_gradient(layer, x, eps=1e-4, tol=1e-6):
|
||||
return numpy.sum(h), ograds
|
||||
|
||||
return verify_gradient(f=grad_layer_wrapper, x=x, eps=eps, tol=tol, layer=layer)
|
||||
|
||||
|
||||
def test_conv_linear_fprop(layer, kernel_order='ioxy', kernels_first=True,
|
||||
dtype=np.float):
|
||||
"""
|
||||
Tests forward propagation method of a convolutional layer.
|
||||
|
||||
Checks the outputs of `fprop` method for a fixed input against known
|
||||
reference values for the outputs and raises an AssertionError if
|
||||
the outputted values are not consistent with the reference values. If
|
||||
tests are all passed returns True.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
layer : instance of Layer subclass
|
||||
Convolutional (linear only) layer implementation. It must implement
|
||||
the methods `get_params`, `set_params` and `fprop`.
|
||||
kernel_order : string
|
||||
Specifes dimension ordering assumed for convolutional kernels
|
||||
passed to `layer`. Default is `ioxy` which corresponds to:
|
||||
input channels, output channels, image x, image y
|
||||
The other option is 'oixy' which corresponds to
|
||||
output channels, input channels, image x, image y
|
||||
Any other value will raise a ValueError exception.
|
||||
kernels_first : boolean
|
||||
Specifies order in which parameters are passed to and returned from
|
||||
`get_params` and `set_params`. Default is True which corresponds
|
||||
to signatures of `get_params` and `set_params` being:
|
||||
kernels, biases = layer.get_params()
|
||||
layer.set_params([kernels, biases])
|
||||
If False this corresponds to signatures of `get_params` and
|
||||
`set_params` being:
|
||||
biases, kernels = layer.get_params()
|
||||
layer.set_params([biases, kernels])
|
||||
dtype : numpy data type
|
||||
Data type to use in numpy arrays passed to layer methods. Default
|
||||
is `numpy.float`.
|
||||
|
||||
Raises
|
||||
------
|
||||
AssertionError
|
||||
Raised if output of `layer.fprop` is inconsistent with reference
|
||||
values either in shape or values.
|
||||
ValueError
|
||||
Raised if `kernel_order` is not a valid order string.
|
||||
"""
|
||||
inputs = np.arange(96).reshape((2, 3, 4, 4)).astype(dtype)
|
||||
kernels = np.arange(-12, 12).reshape((3, 2, 2, 2)).astype(dtype)
|
||||
if kernel_order == 'oixy':
|
||||
kernels = kernels.swapaxes(0, 1)
|
||||
elif kernel_order != 'ioxy':
|
||||
raise ValueError('kernel_order must be one of "ioxy" and "oixy"')
|
||||
biases = np.arange(2).astype(dtype)
|
||||
true_output = np.array(
|
||||
[[[[ 496., 466., 436.],
|
||||
[ 376., 346., 316.],
|
||||
[ 256., 226., 196.]],
|
||||
[[ 1385., 1403., 1421.],
|
||||
[ 1457., 1475., 1493.],
|
||||
[ 1529., 1547., 1565.]]],
|
||||
[[[ -944., -974., -1004.],
|
||||
[-1064., -1094., -1124.],
|
||||
[-1184., -1214., -1244.]],
|
||||
[[ 2249., 2267., 2285.],
|
||||
[ 2321., 2339., 2357.],
|
||||
[ 2393., 2411., 2429.]]]], dtype=dtype)
|
||||
try:
|
||||
orig_params = layer.get_params()
|
||||
if kernels_first:
|
||||
layer.set_params([kernels, biases])
|
||||
else:
|
||||
layer.set_params([biases, kernels])
|
||||
layer_output = layer.fprop(inputs)
|
||||
assert layer_output.shape == true_output.shape, (
|
||||
'Layer fprop gives incorrect shaped output. '
|
||||
'Correct shape is {0} but returned shape is {1}.'
|
||||
.format(true_output.shape, layer_output.shape)
|
||||
)
|
||||
assert np.allclose(layer_output, true_output), (
|
||||
'Layer fprop does not give correct output. '
|
||||
'Correct output is {0}\n but returned output is {1}.'
|
||||
.format(true_output, layer_output)
|
||||
)
|
||||
finally:
|
||||
layer.set_params(orig_params)
|
||||
return True
|
||||
|
||||
|
||||
def test_conv_linear_bprop(layer, kernel_order='ioxy', kernels_first=True,
|
||||
dtype=np.float):
|
||||
"""
|
||||
Tests input gradients backpropagation method of a convolutional layer.
|
||||
|
||||
Checks the outputs of `bprop` method for a fixed input against known
|
||||
reference values for the outputs and raises an AssertionError if
|
||||
the outputted values are not consistent with the reference values. If
|
||||
tests are all passed returns True.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
layer : instance of Layer subclass
|
||||
Convolutional (linear only) layer implementation. It must implement
|
||||
the methods `get_params`, `set_params` and `bprop`.
|
||||
kernel_order : string
|
||||
Specifes dimension ordering assumed for convolutional kernels
|
||||
passed to `layer`. Default is `ioxy` which corresponds to:
|
||||
input channels, output channels, image x, image y
|
||||
The other option is 'oixy' which corresponds to
|
||||
output channels, input channels, image x, image y
|
||||
Any other value will raise a ValueError exception.
|
||||
kernels_first : boolean
|
||||
Specifies order in which parameters are passed to and returned from
|
||||
`get_params` and `set_params`. Default is True which corresponds
|
||||
to signatures of `get_params` and `set_params` being:
|
||||
kernels, biases = layer.get_params()
|
||||
layer.set_params([kernels, biases])
|
||||
If False this corresponds to signatures of `get_params` and
|
||||
`set_params` being:
|
||||
biases, kernels = layer.get_params()
|
||||
layer.set_params([biases, kernels])
|
||||
dtype : numpy data type
|
||||
Data type to use in numpy arrays passed to layer methods. Default
|
||||
is `numpy.float`.
|
||||
|
||||
Raises
|
||||
------
|
||||
AssertionError
|
||||
Raised if output of `layer.bprop` is inconsistent with reference
|
||||
values either in shape or values.
|
||||
ValueError
|
||||
Raised if `kernel_order` is not a valid order string.
|
||||
"""
|
||||
inputs = np.arange(96).reshape((2, 3, 4, 4)).astype(dtype)
|
||||
kernels = np.arange(-12, 12).reshape((3, 2, 2, 2)).astype(dtype)
|
||||
if kernel_order == 'oixy':
|
||||
kernels = kernels.swapaxes(0, 1)
|
||||
elif kernel_order != 'ioxy':
|
||||
raise ValueError('kernel_order must be one of "ioxy" and "oixy"')
|
||||
biases = np.arange(2).astype(dtype)
|
||||
igrads = np.arange(-20, 16).reshape((2, 2, 3, 3)).astype(dtype)
|
||||
true_ograds = np.array(
|
||||
[[[[ 328., 605., 567., 261.],
|
||||
[ 534., 976., 908., 414.],
|
||||
[ 426., 772., 704., 318.],
|
||||
[ 170., 305., 275., 123.]],
|
||||
[[ 80., 125., 119., 45.],
|
||||
[ 86., 112., 108., 30.],
|
||||
[ 74., 100., 96., 30.],
|
||||
[ 18., 17., 19., 3.]],
|
||||
[[-168., -355., -329., -171.],
|
||||
[-362., -752., -692., -354.],
|
||||
[-278., -572., -512., -258.],
|
||||
[-134., -271., -237., -117.]]],
|
||||
[[[ -32., -79., -117., -63.],
|
||||
[-114., -248., -316., -162.],
|
||||
[-222., -452., -520., -258.],
|
||||
[-118., -235., -265., -129.]],
|
||||
[[ 8., 17., 11., 9.],
|
||||
[ 14., 40., 36., 30.],
|
||||
[ 2., 28., 24., 30.],
|
||||
[ 18., 53., 55., 39.]],
|
||||
[[ 48., 113., 139., 81.],
|
||||
[ 142., 328., 388., 222.],
|
||||
[ 226., 508., 568., 318.],
|
||||
[ 154., 341., 375., 207.]]]], dtype=dtype)
|
||||
try:
|
||||
orig_params = layer.get_params()
|
||||
if kernels_first:
|
||||
layer.set_params([kernels, biases])
|
||||
else:
|
||||
layer.set_params([biases, kernels])
|
||||
layer_deltas, layer_ograds = layer.bprop(None, igrads)
|
||||
assert layer_deltas.shape == igrads.shape, (
|
||||
'Layer bprop give incorrectly shaped deltas output.'
|
||||
'Correct shape is {0} but returned shape is {1}.'
|
||||
.format(igrads.shape, layer_deltas.shape)
|
||||
)
|
||||
assert np.allclose(layer_deltas, igrads), (
|
||||
'Layer bprop does not give correct deltas output. '
|
||||
'Correct output is {0}\n but returned output is {1}.'
|
||||
.format(igrads, layer_deltas)
|
||||
)
|
||||
assert layer_ograds.shape == true_ograds.shape, (
|
||||
'Layer bprop gives incorrect shaped ograds output. '
|
||||
'Correct shape is {0} but returned shape is {1}.'
|
||||
.format(true_ograds.shape, layer_ograds.shape)
|
||||
)
|
||||
assert np.allclose(layer_ograds, true_ograds), (
|
||||
'Layer bprop does not give correct ograds output. '
|
||||
'Correct output is {0}\n but returned output is {1}.'
|
||||
.format(true_ograds, layer_ograds)
|
||||
)
|
||||
finally:
|
||||
layer.set_params(orig_params)
|
||||
return True
|
||||
|
||||
|
||||
def test_conv_linear_pgrads(layer, kernel_order='ioxy', kernels_first=True,
|
||||
dtype=np.float):
|
||||
"""
|
||||
Tests parameter gradients backpropagation method of a convolutional layer.
|
||||
|
||||
Checks the outputs of `pgrads` method for a fixed input against known
|
||||
reference values for the outputs and raises an AssertionError if
|
||||
the outputted values are not consistent with the reference values. If
|
||||
tests are all passed returns True.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
layer : instance of Layer subclass
|
||||
Convolutional (linear only) layer implementation. It must implement
|
||||
the methods `get_params`, `set_params` and `pgrads`.
|
||||
kernel_order : string
|
||||
Specifes dimension ordering assumed for convolutional kernels
|
||||
passed to `layer`. Default is `ioxy` which corresponds to:
|
||||
input channels, output channels, image x, image y
|
||||
The other option is 'oixy' which corresponds to
|
||||
output channels, input channels, image x, image y
|
||||
Any other value will raise a ValueError exception.
|
||||
kernels_first : boolean
|
||||
Specifies order in which parameters are passed to and returned from
|
||||
`get_params` and `set_params`. Default is True which corresponds
|
||||
to signatures of `get_params` and `set_params` being:
|
||||
kernels, biases = layer.get_params()
|
||||
layer.set_params([kernels, biases])
|
||||
If False this corresponds to signatures of `get_params` and
|
||||
`set_params` being:
|
||||
biases, kernels = layer.get_params()
|
||||
layer.set_params([biases, kernels])
|
||||
dtype : numpy data type
|
||||
Data type to use in numpy arrays passed to layer methods. Default
|
||||
is `numpy.float`.
|
||||
|
||||
Raises
|
||||
------
|
||||
AssertionError
|
||||
Raised if output of `layer.pgrads` is inconsistent with reference
|
||||
values either in shape or values.
|
||||
ValueError
|
||||
Raised if `kernel_order` is not a valid order string.
|
||||
"""
|
||||
inputs = np.arange(96).reshape((2, 3, 4, 4)).astype(dtype)
|
||||
kernels = np.arange(-12, 12).reshape((3, 2, 2, 2)).astype(dtype)
|
||||
biases = np.arange(2).astype(dtype)
|
||||
deltas = np.arange(-20, 16).reshape((2, 2, 3, 3)).astype(dtype)
|
||||
true_kernel_grads = np.array(
|
||||
[[[[ 390., 264.],
|
||||
[ -114., -240.]],
|
||||
[[ 5088., 5124.],
|
||||
[ 5232., 5268.]]],
|
||||
[[[-1626., -1752.],
|
||||
[-2130., -2256.]],
|
||||
[[ 5664., 5700.],
|
||||
[ 5808., 5844.]]],
|
||||
[[[-3642., -3768.],
|
||||
[-4146., -4272.]],
|
||||
[[ 6240., 6276.],
|
||||
[ 6384., 6420.]]]], dtype=dtype)
|
||||
if kernel_order == 'oixy':
|
||||
kernels = kernels.swapaxes(0, 1)
|
||||
true_kernel_grads = true_kernel_grads.swapaxes(0, 1)
|
||||
elif kernel_order != 'ioxy':
|
||||
raise ValueError('kernel_order must be one of "ioxy" and "oixy"')
|
||||
true_bias_grads = np.array([-126., 36.], dtype=dtype)
|
||||
try:
|
||||
orig_params = layer.get_params()
|
||||
if kernels_first:
|
||||
layer.set_params([kernels, biases])
|
||||
else:
|
||||
layer.set_params([biases, kernels])
|
||||
layer_kernel_grads, layer_bias_grads = layer.pgrads(inputs, deltas)
|
||||
assert layer_kernel_grads.shape == true_kernel_grads.shape, (
|
||||
'Layer pgrads gives incorrect shaped kernel gradients output. '
|
||||
'Correct shape is {0} but returned shape is {1}.'
|
||||
.format(true_kernel_grads.shape, layer_kernel_grads.shape)
|
||||
)
|
||||
assert np.allclose(layer_kernel_grads, true_kernel_grads), (
|
||||
'Layer pgrads does not give correct kernel gradients output. '
|
||||
'Correct output is {0}\n but returned output is {1}.'
|
||||
.format(true_kernel_grads, layer_kernel_grads)
|
||||
)
|
||||
assert layer_bias_grads.shape == true_bias_grads.shape, (
|
||||
'Layer pgrads gives incorrect shaped bias gradients output. '
|
||||
'Correct shape is {0} but returned shape is {1}.'
|
||||
.format(true_kernel_grads.shape, layer_kernel_grads.shape)
|
||||
)
|
||||
assert np.allclose(layer_bias_grads, true_bias_grads), (
|
||||
'Layer pgrads does not give correct bias gradients output. '
|
||||
'Correct output is {0}\n but returned output is {1}.'
|
||||
.format(true_bias_grads, layer_bias_grads)
|
||||
)
|
||||
finally:
|
||||
layer.set_params(orig_params)
|
||||
return True
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user