Adding ConvLinearLayer test functions.
This commit is contained in:
Matt Graham
parent
162e28b498
commit
eef54933c1
297
mlp/utils.py
297
mlp/utils.py
@ -63,4 +63,299 @@ def verify_layer_gradient(layer, x, eps=1e-4, tol=1e-6):
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deltas, ograds = layer.bprop(h=h, igrads=numpy.ones_like(h))
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deltas, ograds = layer.bprop(h=h, igrads=numpy.ones_like(h))
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return numpy.sum(h), ograds
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return numpy.sum(h), ograds
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return verify_gradient(f=grad_layer_wrapper, x=x, eps=eps, tol=tol, layer=layer)
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return verify_gradient(f=grad_layer_wrapper, x=x, eps=eps, tol=tol, layer=layer)
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def test_conv_linear_fprop(layer, kernel_order='ioxy', kernels_first=True,
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dtype=np.float):
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"""
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Tests forward propagation method of a convolutional layer.
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Checks the outputs of `fprop` method for a fixed input against known
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reference values for the outputs and raises an AssertionError if
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the outputted values are not consistent with the reference values. If
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tests are all passed returns True.
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Parameters
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----------
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layer : instance of Layer subclass
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Convolutional (linear only) layer implementation. It must implement
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the methods `get_params`, `set_params` and `fprop`.
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kernel_order : string
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Specifes dimension ordering assumed for convolutional kernels
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passed to `layer`. Default is `ioxy` which corresponds to:
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input channels, output channels, image x, image y
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The other option is 'oixy' which corresponds to
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output channels, input channels, image x, image y
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Any other value will raise a ValueError exception.
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kernels_first : boolean
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Specifies order in which parameters are passed to and returned from
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`get_params` and `set_params`. Default is True which corresponds
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to signatures of `get_params` and `set_params` being:
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kernels, biases = layer.get_params()
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layer.set_params([kernels, biases])
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If False this corresponds to signatures of `get_params` and
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`set_params` being:
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biases, kernels = layer.get_params()
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layer.set_params([biases, kernels])
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dtype : numpy data type
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Data type to use in numpy arrays passed to layer methods. Default
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is `numpy.float`.
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Raises
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------
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AssertionError
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Raised if output of `layer.fprop` is inconsistent with reference
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values either in shape or values.
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ValueError
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Raised if `kernel_order` is not a valid order string.
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"""
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inputs = np.arange(96).reshape((2, 3, 4, 4)).astype(dtype)
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kernels = np.arange(-12, 12).reshape((3, 2, 2, 2)).astype(dtype)
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if kernel_order == 'oixy':
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kernels = kernels.swapaxes(0, 1)
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elif kernel_order != 'ioxy':
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raise ValueError('kernel_order must be one of "ioxy" and "oixy"')
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biases = np.arange(2).astype(dtype)
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true_output = np.array(
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[[[[ 496., 466., 436.],
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[ 376., 346., 316.],
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[ 256., 226., 196.]],
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[[ 1385., 1403., 1421.],
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[ 1457., 1475., 1493.],
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[ 1529., 1547., 1565.]]],
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[[[ -944., -974., -1004.],
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[-1064., -1094., -1124.],
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[-1184., -1214., -1244.]],
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[[ 2249., 2267., 2285.],
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[ 2321., 2339., 2357.],
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[ 2393., 2411., 2429.]]]], dtype=dtype)
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try:
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orig_params = layer.get_params()
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if kernels_first:
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layer.set_params([kernels, biases])
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else:
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layer.set_params([biases, kernels])
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layer_output = layer.fprop(inputs)
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assert layer_output.shape == true_output.shape, (
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'Layer fprop gives incorrect shaped output. '
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'Correct shape is {0} but returned shape is {1}.'
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.format(true_output.shape, layer_output.shape)
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)
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assert np.allclose(layer_output, true_output), (
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'Layer fprop does not give correct output. '
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'Correct output is {0}\n but returned output is {1}.'
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.format(true_output, layer_output)
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)
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finally:
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layer.set_params(orig_params)
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return True
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def test_conv_linear_bprop(layer, kernel_order='ioxy', kernels_first=True,
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dtype=np.float):
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"""
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Tests input gradients backpropagation method of a convolutional layer.
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Checks the outputs of `bprop` method for a fixed input against known
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reference values for the outputs and raises an AssertionError if
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the outputted values are not consistent with the reference values. If
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tests are all passed returns True.
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Parameters
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----------
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layer : instance of Layer subclass
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Convolutional (linear only) layer implementation. It must implement
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the methods `get_params`, `set_params` and `bprop`.
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kernel_order : string
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Specifes dimension ordering assumed for convolutional kernels
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passed to `layer`. Default is `ioxy` which corresponds to:
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input channels, output channels, image x, image y
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The other option is 'oixy' which corresponds to
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output channels, input channels, image x, image y
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Any other value will raise a ValueError exception.
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kernels_first : boolean
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Specifies order in which parameters are passed to and returned from
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`get_params` and `set_params`. Default is True which corresponds
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to signatures of `get_params` and `set_params` being:
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kernels, biases = layer.get_params()
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layer.set_params([kernels, biases])
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If False this corresponds to signatures of `get_params` and
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`set_params` being:
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biases, kernels = layer.get_params()
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layer.set_params([biases, kernels])
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dtype : numpy data type
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Data type to use in numpy arrays passed to layer methods. Default
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is `numpy.float`.
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Raises
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------
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AssertionError
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Raised if output of `layer.bprop` is inconsistent with reference
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values either in shape or values.
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ValueError
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Raised if `kernel_order` is not a valid order string.
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"""
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inputs = np.arange(96).reshape((2, 3, 4, 4)).astype(dtype)
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kernels = np.arange(-12, 12).reshape((3, 2, 2, 2)).astype(dtype)
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if kernel_order == 'oixy':
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kernels = kernels.swapaxes(0, 1)
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elif kernel_order != 'ioxy':
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raise ValueError('kernel_order must be one of "ioxy" and "oixy"')
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biases = np.arange(2).astype(dtype)
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igrads = np.arange(-20, 16).reshape((2, 2, 3, 3)).astype(dtype)
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true_ograds = np.array(
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[[[[ 328., 605., 567., 261.],
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[ 534., 976., 908., 414.],
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[ 426., 772., 704., 318.],
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[ 170., 305., 275., 123.]],
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[[ 80., 125., 119., 45.],
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[ 86., 112., 108., 30.],
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[ 74., 100., 96., 30.],
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[ 18., 17., 19., 3.]],
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[[-168., -355., -329., -171.],
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[-362., -752., -692., -354.],
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[-278., -572., -512., -258.],
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[-134., -271., -237., -117.]]],
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[[[ -32., -79., -117., -63.],
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[-114., -248., -316., -162.],
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[-222., -452., -520., -258.],
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[-118., -235., -265., -129.]],
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[[ 8., 17., 11., 9.],
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[ 14., 40., 36., 30.],
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[ 2., 28., 24., 30.],
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[ 18., 53., 55., 39.]],
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[[ 48., 113., 139., 81.],
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[ 142., 328., 388., 222.],
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[ 226., 508., 568., 318.],
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[ 154., 341., 375., 207.]]]], dtype=dtype)
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try:
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orig_params = layer.get_params()
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if kernels_first:
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layer.set_params([kernels, biases])
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else:
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layer.set_params([biases, kernels])
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layer_deltas, layer_ograds = layer.bprop(None, igrads)
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assert layer_deltas.shape == igrads.shape, (
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'Layer bprop give incorrectly shaped deltas output.'
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'Correct shape is {0} but returned shape is {1}.'
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.format(igrads.shape, layer_deltas.shape)
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)
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assert np.allclose(layer_deltas, igrads), (
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'Layer bprop does not give correct deltas output. '
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'Correct output is {0}\n but returned output is {1}.'
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.format(igrads, layer_deltas)
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)
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assert layer_ograds.shape == true_ograds.shape, (
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'Layer bprop gives incorrect shaped ograds output. '
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'Correct shape is {0} but returned shape is {1}.'
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.format(true_ograds.shape, layer_ograds.shape)
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)
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assert np.allclose(layer_ograds, true_ograds), (
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'Layer bprop does not give correct ograds output. '
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'Correct output is {0}\n but returned output is {1}.'
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.format(true_ograds, layer_ograds)
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)
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finally:
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layer.set_params(orig_params)
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return True
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def test_conv_linear_pgrads(layer, kernel_order='ioxy', kernels_first=True,
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dtype=np.float):
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"""
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Tests parameter gradients backpropagation method of a convolutional layer.
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Checks the outputs of `pgrads` method for a fixed input against known
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reference values for the outputs and raises an AssertionError if
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the outputted values are not consistent with the reference values. If
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tests are all passed returns True.
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Parameters
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----------
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layer : instance of Layer subclass
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Convolutional (linear only) layer implementation. It must implement
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the methods `get_params`, `set_params` and `pgrads`.
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kernel_order : string
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Specifes dimension ordering assumed for convolutional kernels
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passed to `layer`. Default is `ioxy` which corresponds to:
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input channels, output channels, image x, image y
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The other option is 'oixy' which corresponds to
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output channels, input channels, image x, image y
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Any other value will raise a ValueError exception.
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kernels_first : boolean
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Specifies order in which parameters are passed to and returned from
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`get_params` and `set_params`. Default is True which corresponds
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to signatures of `get_params` and `set_params` being:
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kernels, biases = layer.get_params()
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layer.set_params([kernels, biases])
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If False this corresponds to signatures of `get_params` and
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`set_params` being:
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biases, kernels = layer.get_params()
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layer.set_params([biases, kernels])
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dtype : numpy data type
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Data type to use in numpy arrays passed to layer methods. Default
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is `numpy.float`.
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Raises
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------
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AssertionError
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Raised if output of `layer.pgrads` is inconsistent with reference
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values either in shape or values.
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ValueError
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Raised if `kernel_order` is not a valid order string.
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"""
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inputs = np.arange(96).reshape((2, 3, 4, 4)).astype(dtype)
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kernels = np.arange(-12, 12).reshape((3, 2, 2, 2)).astype(dtype)
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biases = np.arange(2).astype(dtype)
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deltas = np.arange(-20, 16).reshape((2, 2, 3, 3)).astype(dtype)
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true_kernel_grads = np.array(
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[[[[ 390., 264.],
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[ -114., -240.]],
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[[ 5088., 5124.],
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[ 5232., 5268.]]],
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[[[-1626., -1752.],
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[-2130., -2256.]],
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[[ 5664., 5700.],
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[ 5808., 5844.]]],
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[[[-3642., -3768.],
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[-4146., -4272.]],
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[[ 6240., 6276.],
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[ 6384., 6420.]]]], dtype=dtype)
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if kernel_order == 'oixy':
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kernels = kernels.swapaxes(0, 1)
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true_kernel_grads = true_kernel_grads.swapaxes(0, 1)
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elif kernel_order != 'ioxy':
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raise ValueError('kernel_order must be one of "ioxy" and "oixy"')
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true_bias_grads = np.array([-126., 36.], dtype=dtype)
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try:
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orig_params = layer.get_params()
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if kernels_first:
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layer.set_params([kernels, biases])
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else:
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layer.set_params([biases, kernels])
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layer_kernel_grads, layer_bias_grads = layer.pgrads(inputs, deltas)
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assert layer_kernel_grads.shape == true_kernel_grads.shape, (
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'Layer pgrads gives incorrect shaped kernel gradients output. '
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'Correct shape is {0} but returned shape is {1}.'
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.format(true_kernel_grads.shape, layer_kernel_grads.shape)
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)
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assert np.allclose(layer_kernel_grads, true_kernel_grads), (
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'Layer pgrads does not give correct kernel gradients output. '
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'Correct output is {0}\n but returned output is {1}.'
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.format(true_kernel_grads, layer_kernel_grads)
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)
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assert layer_bias_grads.shape == true_bias_grads.shape, (
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'Layer pgrads gives incorrect shaped bias gradients output. '
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'Correct shape is {0} but returned shape is {1}.'
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.format(true_kernel_grads.shape, layer_kernel_grads.shape)
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)
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assert np.allclose(layer_bias_grads, true_bias_grads), (
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'Layer pgrads does not give correct bias gradients output. '
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'Correct output is {0}\n but returned output is {1}.'
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.format(true_bias_grads, layer_bias_grads)
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)
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finally:
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layer.set_params(orig_params)
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return True
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