def test_grad_crf(x, train, c, dimX, dimY, from_file=True):
model = CRFModel(dimX, dimY)
model.load_X(x, from_file=from_file)
W = model._W # column format
T = model._T # column format
reg = np.concatenate([W.T.reshape(-1), T.T.reshape(-1)])
g = grad_crf_wrapper(x, train, dimX, dimY, from_file=from_file)
g = -c * g + reg
return g
def crf_obj(x, train_data, c):
"""Compute the CRF objective and gradient on the list of words (word_list)
evaluated at the current model x (w_y and T, stored as a vector)
"""
print("Evaluating grad")
global iteration
iteration += 1
print(iteration)
# x is a vector as required by the solver.
logCrf = log_crf_wrapper(x, train_data, 128, 26, from_file=False)
model = CRFModel(128, 26)
model.load_X(x, from_file=False)
W = model._W # column format
T = model._T # column format
# Compute the objective value of CRF
f = (-c * logCrf) + (0.5 * np.sum(W * W)) + (
0.5 * np.sum(T * T)) # objective log-likelihood + regularizer
reg = np.concatenate([W.T.reshape(-1), T.T.reshape(-1)])
g = grad_crf_wrapper(x, train_data, 128, 26, from_file=False)
g = -c * g + reg
return [f, g]