def test_max_pooling():
x = tensor.tensor4("x")
num_channels = 4
batch_size = 5
x_size = 17
y_size = 13
pool_size = 3
pool = MaxPooling((pool_size, pool_size))
y = pool.apply(x)
func = function([x], y)
x_val = numpy.ones((batch_size, num_channels, x_size, y_size), dtype=theano.config.floatX)
assert_allclose(func(x_val), numpy.ones((batch_size, num_channels, x_size / pool_size, y_size / pool_size)))
pool.input_dim = (x_size, y_size)
pool.get_dim("output") == (num_channels, x_size / pool_size + 1, y_size / pool_size + 1)
def test_max_pooling():
x = tensor.tensor4('x')
num_channels = 4
batch_size = 5
x_size = 17
y_size = 13
pool_size = 3
pool = MaxPooling((pool_size, pool_size))
y = pool.apply(x)
func = function([x], y)
x_val = numpy.ones((batch_size, num_channels, x_size, y_size),
dtype=theano.config.floatX)
assert_allclose(
func(x_val),
numpy.ones((batch_size, num_channels, x_size / pool_size + 1,
y_size / pool_size + 1)))
pool.input_dim = (x_size, y_size)
pool.get_dim('output') == (num_channels, x_size / pool_size + 1,
y_size / pool_size + 1)
