def test_init_bias_target_marginals():
"""
Test `Softmax` layer instantiation with `init_bias_target_marginals`.
"""
batch_size = 5
n_features = 5
n_classes = 3
n_targets = 3
irange = 0.1
learning_rate = 0.1
X_data = np.random.random(size=(batch_size, n_features))
Y_categorical = np.asarray([[0],[1],[1],[2],[2]])
class_frequencies = np.asarray([.2, .4, .4])
categorical_dataset = DenseDesignMatrix(X_data,
y=Y_categorical,
y_labels=n_classes)
Y_continuous = np.random.random(size=(batch_size, n_targets))
Y_means = np.mean(Y_continuous, axis=0)
continuous_dataset = DenseDesignMatrix(X_data,
y=Y_continuous)
Y_multiclass = np.random.randint(n_classes,
size=(batch_size, n_targets))
multiclass_dataset = DenseDesignMatrix(X_data,
y=Y_multiclass,
y_labels=n_classes)
def softmax_layer(dataset):
return Softmax(n_classes, 'h0', irange=irange,
init_bias_target_marginals=dataset)
valid_categorical_mlp = MLP(
layers=[softmax_layer(categorical_dataset)],
nvis=n_features
)
actual = valid_categorical_mlp.layers[0].b.get_value()
expected = pseudoinverse_softmax_numpy(class_frequencies)
assert np.allclose(actual, expected)
valid_continuous_mlp = MLP(
layers=[softmax_layer(continuous_dataset)],
nvis=n_features
)
actual = valid_continuous_mlp.layers[0].b.get_value()
expected = pseudoinverse_softmax_numpy(Y_means)
assert np.allclose(actual, expected)
def invalid_multiclass_mlp():
return MLP(
layers=[softmax_layer(multiclass_dataset)],
nvis=n_features
)
assert_raises(AssertionError, invalid_multiclass_mlp)
def test_init_bias_target_marginals():
"""
Test `Softmax` layer instantiation with `init_bias_target_marginals`.
"""
batch_size = 5
n_features = 5
n_classes = 3
n_targets = 3
irange = 0.1
learning_rate = 0.1
X_data = np.random.random(size=(batch_size, n_features))
Y_categorical = np.asarray([[0], [1], [1], [2], [2]])
class_frequencies = np.asarray([.2, .4, .4])
categorical_dataset = DenseDesignMatrix(X_data,
y=Y_categorical,
y_labels=n_classes)
Y_continuous = np.random.random(size=(batch_size, n_targets))
Y_means = np.mean(Y_continuous, axis=0)
continuous_dataset = DenseDesignMatrix(X_data,
y=Y_continuous)
Y_multiclass = np.random.randint(n_classes,
size=(batch_size, n_targets))
multiclass_dataset = DenseDesignMatrix(X_data,
y=Y_multiclass,
y_labels=n_classes)
def softmax_layer(dataset):
return Softmax(n_classes, 'h0', irange=irange,
init_bias_target_marginals=dataset)
valid_categorical_mlp = MLP(
layers=[softmax_layer(categorical_dataset)],
nvis=n_features
)
actual = valid_categorical_mlp.layers[0].b.get_value()
expected = pseudoinverse_softmax_numpy(class_frequencies)
assert np.allclose(actual, expected)
valid_continuous_mlp = MLP(
layers=[softmax_layer(continuous_dataset)],
nvis=n_features
)
actual = valid_continuous_mlp.layers[0].b.get_value()
expected = pseudoinverse_softmax_numpy(Y_means)
assert np.allclose(actual, expected)
def invalid_multiclass_mlp():
return MLP(
layers=[softmax_layer(multiclass_dataset)],
nvis=n_features
)
assert_raises(AssertionError, invalid_multiclass_mlp)
def test_pseudoinverse_softmax_numpy():
rng = np.random.RandomState([2013, 3, 28])
p = np.abs(rng.randn(5))
p /= p.sum()
z = pseudoinverse_softmax_numpy(p)
zbroad = z.reshape(1, z.size)
p2 = softmax_numpy(zbroad)
p2 = p2[0, :]
assert np.allclose(p, p2)
