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Merge pull request #38 from matt-graham/master

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Fixing incorrect numpy references in ConvLinear tests.
This commit is contained in:
Pawel Swietojanski 2015-12-22 11:28:48 +00:00
commit 621bb95326
1 changed files with 23 additions and 23 deletions
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46
mlp/utils.py
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@ -67,7 +67,7 @@ def verify_layer_gradient(layer, x, eps=1e-4, tol=1e-6):
def test_conv_linear_fprop(layer, kernel_order='ioxy', kernels_first=True,
dtype=np.float):
dtype=numpy.float):
"""
Tests forward propagation method of a convolutional layer.
@ -110,14 +110,14 @@ def test_conv_linear_fprop(layer, kernel_order='ioxy', kernels_first=True,
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)
inputs = numpy.arange(96).reshape((2, 3, 4, 4)).astype(dtype)
kernels = numpy.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(
biases = numpy.arange(2).astype(dtype)
true_output = numpy.array(
[[[[ 496., 466., 436.],
[ 376., 346., 316.],
[ 256., 226., 196.]],
@ -142,7 +142,7 @@ def test_conv_linear_fprop(layer, kernel_order='ioxy', kernels_first=True,
'Correct shape is {0} but returned shape is {1}.'
.format(true_output.shape, layer_output.shape)
)
assert np.allclose(layer_output, true_output), (
assert numpy.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)
@ -153,7 +153,7 @@ def test_conv_linear_fprop(layer, kernel_order='ioxy', kernels_first=True,
def test_conv_linear_bprop(layer, kernel_order='ioxy', kernels_first=True,
dtype=np.float):
dtype=numpy.float):
"""
Tests input gradients backpropagation method of a convolutional layer.
@ -196,15 +196,15 @@ def test_conv_linear_bprop(layer, kernel_order='ioxy', kernels_first=True,
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)
inputs = numpy.arange(96).reshape((2, 3, 4, 4)).astype(dtype)
kernels = numpy.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(
biases = numpy.arange(2).astype(dtype)
igrads = numpy.arange(-20, 16).reshape((2, 2, 3, 3)).astype(dtype)
true_ograds = numpy.array(
[[[[ 328., 605., 567., 261.],
[ 534., 976., 908., 414.],
[ 426., 772., 704., 318.],
@ -241,7 +241,7 @@ def test_conv_linear_bprop(layer, kernel_order='ioxy', kernels_first=True,
'Correct shape is {0} but returned shape is {1}.'
.format(igrads.shape, layer_deltas.shape)
)
assert np.allclose(layer_deltas, igrads), (
assert numpy.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)
@ -251,7 +251,7 @@ def test_conv_linear_bprop(layer, kernel_order='ioxy', kernels_first=True,
'Correct shape is {0} but returned shape is {1}.'
.format(true_ograds.shape, layer_ograds.shape)
)
assert np.allclose(layer_ograds, true_ograds), (
assert numpy.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)
@ -262,7 +262,7 @@ def test_conv_linear_bprop(layer, kernel_order='ioxy', kernels_first=True,
def test_conv_linear_pgrads(layer, kernel_order='ioxy', kernels_first=True,
dtype=np.float):
dtype=numpy.float):
"""
Tests parameter gradients backpropagation method of a convolutional layer.
@ -305,11 +305,11 @@ def test_conv_linear_pgrads(layer, kernel_order='ioxy', kernels_first=True,
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(
inputs = numpy.arange(96).reshape((2, 3, 4, 4)).astype(dtype)
kernels = numpy.arange(-12, 12).reshape((3, 2, 2, 2)).astype(dtype)
biases = numpy.arange(2).astype(dtype)
deltas = numpy.arange(-20, 16).reshape((2, 2, 3, 3)).astype(dtype)
true_kernel_grads = numpy.array(
[[[[ 390., 264.],
[ -114., -240.]],
[[ 5088., 5124.],
@ -327,7 +327,7 @@ def test_conv_linear_pgrads(layer, kernel_order='ioxy', kernels_first=True,
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)
true_bias_grads = numpy.array([-126., 36.], dtype=dtype)
try:
orig_params = layer.get_params()
if kernels_first:
@ -340,7 +340,7 @@ def test_conv_linear_pgrads(layer, kernel_order='ioxy', kernels_first=True,
'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), (
assert numpy.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)
@ -350,7 +350,7 @@ def test_conv_linear_pgrads(layer, kernel_order='ioxy', kernels_first=True,
'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), (
assert numpy.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)