def test_cosineconvolution_2d_correctness():
if data_format == 'channels_first':
X = np.random.randn(1, 3, 5, 5)
input_dim = (3, 5, 5)
W0 = X[:, :, ::-1, ::-1]
elif data_format == 'channels_last':
X = np.random.randn(1, 5, 5, 3)
input_dim = (5, 5, 3)
W0 = X[0, :, :, :, None]
model = Sequential()
model.add(
convolutional.CosineConvolution2D(1, (5, 5),
use_bias=True,
input_shape=input_dim,
data_format=data_format))
model.compile(loss='mse', optimizer='rmsprop')
W = model.get_weights()
W[0] = W0
W[1] = np.asarray([1.])
model.set_weights(W)
out = model.predict(X)
assert_allclose(out, np.ones((1, 1, 1, 1), dtype=K.floatx()), atol=1e-5)
model = Sequential()
model.add(
convolutional.CosineConvolution2D(1, (5, 5),
use_bias=False,
input_shape=input_dim,
data_format=data_format))
model.compile(loss='mse', optimizer='rmsprop')
W = model.get_weights()
W[0] = -2 * W0
model.set_weights(W)
out = model.predict(X)
assert_allclose(out, -np.ones((1, 1, 1, 1), dtype=K.floatx()), atol=1e-5)
def test_cosineconvolution_2d():
num_samples = 2
num_filter = 2
stack_size = 3
num_row = 10
num_col = 6
if K.backend() == 'theano':
data_format = 'channels_first'
elif K.backend() == 'tensorflow':
data_format = 'channels_last'
for border_mode in _convolution_border_modes:
for subsample in [(1, 1), (2, 2)]:
for use_bias_mode in [True, False]:
if border_mode == 'same' and subsample != (1, 1):
continue
layer_test(convolutional.CosineConvolution2D,
kwargs={
'filters': num_filter,
'kernel_size': (3, 3),
'padding': border_mode,
'strides': subsample,
'use_bias': use_bias_mode,
'data_format': data_format
},
input_shape=(num_samples, num_row, num_col,
stack_size))
layer_test(convolutional.CosineConvolution2D,
kwargs={
'filters': num_filter,
'kernel_size': (3, 3),
'padding': border_mode,
'strides': subsample,
'use_bias': use_bias_mode,
'data_format': data_format,
'kernel_regularizer': 'l2',
'bias_regularizer': 'l2',
'activity_regularizer': 'l2'
},
input_shape=(num_samples, num_row, num_col,
stack_size))
if data_format == 'channels_first':
X = np.random.randn(1, 3, 5, 5)
input_dim = (3, 5, 5)
W0 = X[:, :, ::-1, ::-1]
elif data_format == 'channels_last':
X = np.random.randn(1, 5, 5, 3)
input_dim = (5, 5, 3)
W0 = X[0, :, :, :, None]
model = Sequential()
model.add(
convolutional.CosineConvolution2D(1, (5, 5),
use_bias=True,
input_shape=input_dim,
data_format=data_format))
model.compile(loss='mse', optimizer='rmsprop')
W = model.get_weights()
W[0] = W0
W[1] = np.asarray([1.])
model.set_weights(W)
out = model.predict(X)
assert_allclose(out, np.ones((1, 1, 1, 1), dtype=K.floatx()), atol=1e-5)
model = Sequential()
model.add(
convolutional.CosineConvolution2D(1, (5, 5),
use_bias=False,
input_shape=input_dim,
data_format=data_format))
model.compile(loss='mse', optimizer='rmsprop')
W = model.get_weights()
W[0] = -2 * W0
model.set_weights(W)
out = model.predict(X)
assert_allclose(out, -np.ones((1, 1, 1, 1), dtype=K.floatx()), atol=1e-5)