def training_loss(weights, idx=0):
# Training loss is the negative log-likelihood of the training labels.
t_idx_ = batch_indices(idx)
preds = model_(weights, X[t_idx_, :])
loglik = -np.sum(np.log(preds + esp) * y_ohe[t_idx_, :])
num_unpack = 3
reg = 0
# reg_l1 = np.sum(np.abs(flattened)) * 1.
for idx_ in range(0, len(weights), num_unpack):
param_temp_ = weights[idx_:idx_ + num_unpack]
flattened, _ = weights_flatten(param_temp_[:2])
reg_l1 = np.sum(np.abs(flattened)) * 1.0
reg += reg_l1
return loglik + reg
def training_loss(weights, idx=0):
# Training loss is the negative log-likelihood of the training labels.
t_idx_ = batch_indices(idx)
preds = sigmoid(model_(weights, X[t_idx_, :]))
label_probabilities = preds * y[t_idx_] + (1 - preds) * (1 -
y[t_idx_])
# print(label_probabilities)
loglik = -np.sum(np.log(label_probabilities))
num_unpack = 3
reg = 0
# reg_l1 = np.sum(np.abs(flattened)) * 1.
for idx_ in range(0, len(weights), num_unpack):
param_temp_ = weights[idx_:idx_ + num_unpack]
flattened, _ = weights_flatten(param_temp_[:2])
reg_l1 = np.sum(np.abs(flattened)) * 1.0
reg += reg_l1
return loglik + reg
def l2_norm(params):
"""Computes l2 norm of params by flattening them into a vector."""
flattened, _ = weights_flatten(params)
return np.dot(flattened, flattened)