def test_forward(self):
x_val = np.random.random((3, 4))
x = ad.variable(x_val)
y = ad.square(x)
actual = y.forward()
expect = x_val * x_val
self.assertEqual(expect.shape, y.shape)
self.assertTrue(np.allclose(expect, actual), (expect, actual))
def gen_linear_model(config: dict, verbose=False):
"""Generate a linear model.
:param config: Configuration.
:param verbose: Print loss and gradients if it is True.
:return: Model, loss, placeholders and variables.
"""
x = ad.placeholder(shape=(None, config['input_len']), name='X')
y = ad.placeholder(shape=(None, ), name='Y')
w1 = ad.variable(
initializer=ad.inits.random_normal(),
shape=(config['input_len'], config['hidden_dim']),
name='W1',
)
b1 = ad.variable(
initializer=ad.inits.zeros,
shape=config['hidden_dim'],
name='b1',
)
v = ad.acts.leaky_relu(ad.dot(x, w1) + b1)
w2 = ad.variable(
initializer=ad.inits.random_normal(),
shape=(config['hidden_dim'], 2),
name='W2',
)
b2 = ad.variable(
initializer=ad.inits.zeros,
shape=2,
name='b2',
)
y_pred = ad.acts.softmax(ad.dot(v, w2) + b2)
loss = ad.square(y - y_pred).mean()
if verbose:
print('Loss:', loss)
return y_pred, loss, [x, y], [w1, b1, w2, b2]
def gen_linear_model(config: dict, verbose=False):
"""Generate a linear model.
:param config: Configuration.
:param verbose: Print loss and gradients if it is True.
:return: Model, loss, placeholders and variables.
"""
x = ad.placeholder(shape=(None, config['input_len']), name='X')
y = ad.placeholder(shape=(None, ), name='Y')
w = ad.variable(
initializer=ad.inits.random_normal(),
shape=config['input_len'],
name='W',
)
b = ad.variable(0.0, name='b')
y_pred = ad.dot(x, w) + b
loss = ad.square(y - y_pred).mean()
if verbose:
print('Loss:', loss)
return y_pred, loss, [x, y], [w, b]
def test_backward(self):
x_val = np.random.random((3, 4))
x = ad.variable(x_val)
y = ad.square(x)
self.numeric_gradient_check(y, {}, [x])
def mean_square_error(y_true: ad.Operation,
y_pred: ad.Operation) -> ad.Operation:
return ad.sum(ad.square(y_true - y_pred), axis=-1)