def test_averagepooling_2d():
nb_samples = 9
stack_size = 7
input_nb_row = 11
input_nb_col = 12
pool_size = (3, 3)
input = np.ones((nb_samples, stack_size, input_nb_row, input_nb_col))
for strides in [(1, 1), (2, 2)]:
layer = convolutional.AveragePooling2D(strides=strides,
border_mode='valid',
pool_size=pool_size)
layer.input = K.variable(input)
for train in [True, False]:
K.eval(layer.get_output(train))
layer.get_config()
def test_averagepooling_2d():
nb_samples = 9
stack_size = 7
input_nb_row = 11
input_nb_col = 12
input = np.ones((nb_samples, stack_size, input_nb_row, input_nb_col))
for border_mode in ['valid', 'same']:
for pool_size in [(2, 2), (3, 3), (4, 4), (5, 5)]:
for strides in [(1, 1), (2, 2)]:
layer = convolutional.AveragePooling2D(strides=strides,
border_mode=border_mode,
pool_size=pool_size)
layer.input = K.variable(input)
for train in [True, False]:
out = K.eval(layer.get_output(train))
if border_mode == 'same' and strides == (1, 1):
assert input.shape == out.shape
layer.get_config()