def check_grad(self, x, y=None):
"""Check gradients of the model using data x and label y if available
"""
if y is not None:
# encode labels
y = self._transform_labels([y])[0]
print("Checking gradient... ", end='')
diff = scipy_check_grad(self._get_loss_check_grad,
self._get_grad_check_grad,
[np.random.random()], x, y)
print("diff = %.8f" % diff)
return diff
def check_grad(self, x, y=None):
"""Check gradients of the model using data x and label y if available
"""
self._init()
if y is not None:
# encode labels
y = self._encode_labels(y)
# initialize weights
self._init_params(x)
print("Checking gradient... ", end='')
diff = scipy_check_grad(self._get_loss_check_grad,
self._get_grad_check_grad, self._roll_params(),
x, y)
print("diff = %.8f" % diff)
return diff
def check_grad(model, param_name, f, g):
from scipy.optimize import check_grad as scipy_check_grad
p = ModelParameterAcessor(model, param_name)
def eval_f(param_as_list):
old_value = p.get() # Save old
p.set_flattened(param_as_list) # Set new
f_val = f()
p.set(old_value) # Restore old value
return f_val
def eval_g(param_as_list):
old_value = p.get() # Save old
p.set_flattened(param_as_list) # Set new
g_val = ravel(g())
p.set(old_value) # Restore old value
return g_val
x0 = ravel(p.get())
return scipy_check_grad(eval_f, eval_g, x0)
def check_grad(model, param_name, f, g):
from scipy.optimize import check_grad as scipy_check_grad
p = ModelParameterAcessor(model, param_name)
def eval_f(param_as_list):
old_value = p.get() # Save old
p.set_flattened(param_as_list) # Set new
f_val = f()
p.set(old_value) # Restore old value
return f_val
def eval_g(param_as_list):
old_value = p.get() # Save old
p.set_flattened(param_as_list) # Set new
g_val = ravel(g())
p.set(old_value) # Restore old value
return g_val
x0 = ravel(p.get())
return scipy_check_grad(eval_f, eval_g, x0)