def _make_loss_functions(self, mode=None):
"""Return pair `f_loss, f_d_loss` of functions.
- f_loss returns the current loss,
- f_d_loss returns the gradient of that loss wrt parameters,
matrix of the loss.
"""
d_loss = self._d_loss()
if self.gradient_clip:
d_loss = project_into_l2_ball(d_loss, self.gradient_clip)
args = list(self.data_arguments)
f_loss = self.function(args, 'loss', explicit_pars=True, mode=mode)
f_d_loss = self.function(args, d_loss, explicit_pars=True, mode=mode)
return f_loss, f_d_loss
def _make_loss_functions(self, mode=None):
"""Return pair `f_loss, f_d_loss` of functions.
- f_loss returns the current loss,
- f_d_loss returns the gradient of that loss wrt parameters,
matrix of the loss.
"""
d_loss = self._d_loss()
if self.gradient_clip:
d_loss = project_into_l2_ball(d_loss, self.gradient_clip)
args = list(self.data_arguments)
f_loss = self.function(args, 'loss', explicit_pars=True, mode=mode)
f_d_loss = self.function(args, d_loss, explicit_pars=True, mode=mode)
return f_loss, f_d_loss
def test_project_into_l2_ball_batch():
x = T.matrix()
x_projected = project_into_l2_ball(x, 1)
f = theano.function([x], x_projected)
x = np.array([[.1, .1, .1], [0, 1, 0]]).astype(theano.config.floatX)
y = f(x)
desired = np.array([
[.1, .1, .1],
[0., 1, 0],
])
print y
assert np.allclose(desired, y)
def test_project_into_l2_ball_batch():
x = T.matrix()
x_projected = project_into_l2_ball(x, 1)
f = theano.function([x], x_projected)
x = np.array([[.1, .1, .1],
[0, 1, 0]])
y = f(x)
desired = np.array([
[.1, .1, .1],
[0., 1, 0],
])
print y
assert np.allclose(desired, y)
def test_project_into_l2_ball_single():
x = T.vector()
x_projected = project_into_l2_ball(x, 1)
f = theano.function([x], x_projected)
x = np.array([.1, .1, .1])
y = f(x)
assert np.allclose(np.array([.1, .1, .1]), y)
x = np.array([2, 1, 1])
y = f(x)
assert np.allclose(np.array([0.81649658, 0.40824829, 0.40824829]), y)
x = np.array([0, 1, 0])
y = f(x)
assert np.allclose(np.array([0., 1, 0]), y)
def test_project_into_l2_ball_single():
x = T.vector()
x_projected = project_into_l2_ball(x, 1)
f = theano.function([x], x_projected)
x = np.array([.1, .1, .1]).astype(theano.config.floatX)
y = f(x)
assert np.allclose(np.array([.1, .1, .1]), y)
x = np.array([2, 1, 1]).astype(theano.config.floatX)
y = f(x)
assert np.allclose(np.array([0.81649658, 0.40824829, 0.40824829]), y)
x = np.array([0, 1, 0]).astype(theano.config.floatX)
y = f(x)
assert np.allclose(np.array([0., 1, 0]), y)
