def test_softmax_make_state():
# Verifies that BinaryVector.make_state creates
# a shared variable whose value passes check_multinomial_samples
n = 5
num_samples = 1000
tol = .04
layer = Softmax(n_classes = n, layer_name = 'y')
rng = np.random.RandomState([2012, 11, 1, 11])
z = 3 * rng.randn(n)
mean = np.exp(z)
mean /= mean.sum()
layer.set_biases(z.astype(config.floatX))
state = layer.make_state(num_examples=num_samples,
numpy_rng=rng)
value = state.get_value()
check_multinomial_samples(value, (num_samples, n), mean, tol)
def test_softmax_make_state():
# Verifies that BinaryVector.make_state creates
# a shared variable whose value passes check_multinomial_samples
n = 5
num_samples = 1000
tol = .04
layer = Softmax(n_classes = n, layer_name = 'y')
rng = np.random.RandomState([2012, 11, 1, 11])
z = 3 * rng.randn(n)
mean = np.exp(z)
mean /= mean.sum()
layer.set_biases(z.astype(config.floatX))
state = layer.make_state(num_examples=num_samples,
numpy_rng=rng)
value = state.get_value()
check_multinomial_samples(value, (num_samples, n), mean, tol)
