def _grad(self, values):
"""Gives the (sub/super)gradient of the atom w.r.t. each argument.
Matrix expressions are vectorized, so the gradient is a matrix.
Args:
values: A list of numeric values for the arguments.
Returns:
A list of SciPy CSC sparse matrices or None.
"""
rows = self.args[0].size[0]*self.args[0].size[1]
cols = self.size[0]*self.size[1]
if self.p == 0:
# All zeros.
return [sp.csc_matrix((rows, cols), dtype='float64')]
# Outside domain or on boundary.
if not is_power2(self.p) and np.min(values[0]) <= 0:
if self.p < 1:
# Non-differentiable.
return [None]
else:
# Round up to zero.
values[0] = np.maximum(values[0], 0)
grad_vals = self.p*np.power(values[0], self.p-1)
return [Elementwise.elemwise_grad_to_diag(grad_vals, rows, cols)]
def _grad(self, values):
"""Gives the (sub/super)gradient of the atom w.r.t. each argument.
Matrix expressions are vectorized, so the gradient is a matrix.
Args:
values: A list of numeric values for the arguments.
Returns:
A list of SciPy CSC sparse matrices or None.
"""
rows = self.args[0].size[0] * self.args[0].size[1]
cols = self.size[0] * self.size[1]
if self.p == 0:
# All zeros.
return [sp.csc_matrix((rows, cols), dtype='float64')]
# Outside domain or on boundary.
if not is_power2(self.p) and np.min(values[0]) <= 0:
if self.p < 1:
# Non-differentiable.
return [None]
else:
# Round up to zero.
values[0] = np.maximum(values[0], 0)
grad_vals = self.p * np.power(values[0], self.p - 1)
return [Elementwise.elemwise_grad_to_diag(grad_vals, rows, cols)]
def _grad(self, values):
"""Gives the (sub/super)gradient of the atom w.r.t. each argument.
Matrix expressions are vectorized, so the gradient is a matrix.
Args:
values: A list of numeric values for the arguments.
Returns:
A list of SciPy CSC sparse matrices or None.
"""
rows = self.args[0].size[0] * self.args[0].size[1]
cols = self.size[0] * self.size[1]
# Outside domain or on boundary.
if np.min(values[0]) <= 0:
# Non-differentiable.
return [None]
else:
grad_vals = 1.0 / values[0]
return [Elementwise.elemwise_grad_to_diag(grad_vals, rows, cols)]
def _grad(self, values):
"""Gives the (sub/super)gradient of the atom w.r.t. each argument.
Matrix expressions are vectorized, so the gradient is a matrix.
Args:
values: A list of numeric values for the arguments.
Returns:
A list of SciPy CSC sparse matrices or None.
"""
rows = self.args[0].size[0]*self.args[0].size[1]
cols = self.size[0]*self.size[1]
# Outside domain or on boundary.
if np.min(values[0]) <= 0:
# Non-differentiable.
return [None]
else:
grad_vals = 1.0/values[0]
return [Elementwise.elemwise_grad_to_diag(grad_vals, rows, cols)]
