def primal_loss(W, hyperparam_vect, i_primal, reg_penalty=True):
RS = RandomState((seed, i_hyper, i_primal))
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data, idxs)
loss = reg_loss_fun(W, minibatch, hyperparam_vect, reg_penalty)
if verbose and i_primal % 10 == 0: print "Iter {0}, loss, {1}".format(i_primal, getval(loss))
return loss
def primal_loss(W, hyperparam_vect, i_primal, reg_penalty=True):
RS = RandomState((seed, i_hyper, i_primal))
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data, idxs)
loss = reg_loss_fun(W, minibatch, hyperparam_vect, reg_penalty)
if verbose and i_primal % 10 == 0: print "Iter {0}, loss, {1}".format(i_primal, getval(loss))
return loss
def sub_primal_stochastic_loss(z_vect, transform_vect, i_primal, i_script):
RS = RandomState((seed, i_hyper, i_primal, i_script))
N_train = train_data[i_script]['X'].shape[0]
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data[i_script], idxs)
loss = loss_from_latents(z_vect, transform_vect, i_script, minibatch)
reg = regularization(z_vect) if i_script == 0 else 0.0
if i_primal % N_thin == 0 and i_script == 0:
print "Iter {0}, full losses: train: {1}, valid: {2}, reg: {3}".format(
i_primal,
total_loss(train_data, getval(z_vect)),
total_loss(valid_data, getval(z_vect)),
getval(reg) / N_scripts_per_iter)
return loss + reg
def sub_primal_stochastic_loss(z_vect, transform_vect, i_primal,
i_script):
RS = RandomState((seed, i_hyper, i_primal, i_script))
N_train = train_data[i_script]['X'].shape[0]
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data[i_script], idxs)
loss = loss_from_latents(z_vect, transform_vect, i_script,
minibatch)
reg = regularization(z_vect) if i_script == 0 else 0.0
if i_primal % N_thin == 0 and i_script == 0:
print "Iter {0}, full losses: train: {1}, valid: {2}, reg: {3}".format(
i_primal, total_loss(train_data, getval(z_vect)),
total_loss(valid_data, getval(z_vect)),
getval(reg) / N_scripts_per_iter)
return loss + reg
def primal_stochastic_loss(z_vect, transform_vect, i_primal):
RS = RandomState((seed, i_hyper, i_primal))
loss = 0.0
for _ in range(N_scripts_per_iter):
i_script = RS.randint(N_scripts)
N_train = train_data[i_script]['X'].shape[0]
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data[i_script], idxs)
loss += loss_from_latents(z_vect, transform_vect, i_script, minibatch)
reg = regularization(z_vect)
if i_primal % 20 == 0:
print "Iter {0}, loss {1}, reg {2}".format(i_primal, getval(loss), getval(reg))
print "Full losses: train: {0}, valid: {1}".format(
total_loss(train_data, getval(z_vect)),
total_loss(valid_data, getval(z_vect)))
return loss + reg
def primal_stochastic_loss(z_vect, transform_vect, i_primal):
RS = RandomState((seed, i_hyper, i_primal))
loss = 0.0
for _ in range(N_scripts_per_iter):
i_script = RS.randint(N_scripts)
N_train = train_data[i_script]['X'].shape[0]
idxs = RS.permutation(N_train)[:batch_size]
minibatch = dictslice(train_data[i_script], idxs)
loss += loss_from_latents(z_vect, transform_vect, i_script, minibatch)
reg = regularization(z_vect)
if i_primal % 1 == 0:
print "Iter {0}, loss {1}, reg {2}".format(i_primal, getval(loss), getval(reg))
print "Full losses: train: {0}, valid: {1}".format(
total_loss(train_data, getval(z_vect)),
total_loss(valid_data, getval(z_vect)))
return loss + reg
