def make_funcs(config, dbg_out={}):
net_in, net_out = hybrid_network(config['num_inputs'], config['num_outputs'],
config['num_units'], config['num_sto'],
dbg_out=dbg_out)
if not config['dbg_out_full']: dbg_out = {}
# def f_sample(_inputs, num_samples=1, flatten=False):
# _mean, _var = f_step(_inputs)
# _samples = []
# for _m, _v in zip(_mean, _var):
# _s = np.random.multivariate_normal(_m, np.diag(np.sqrt(_v)), num_samples)
# if flatten: _samples.extend(_s)
# else: _samples.append(_s)
# return np.array(_samples)
Y_gt = cgt.matrix("Y")
Y_prec = cgt.tensor3('V', fixed_shape=(None, config['num_inputs'], config['num_inputs']))
params = nn.get_parameters(net_out)
size_batch, size_out = net_out.shape
inputs, outputs = [net_in], [net_out]
if config['no_bias']:
print "Excluding bias"
params = [p for p in params if not p.name.endswith(".b")]
loss_vec = dist.gaussian.logprob(Y_gt, net_out, Y_prec)
if config['weight_decay'] > 0.:
print "Applying penalty on parameter norm"
params_flat = cgt.concatenate([p.flatten() for p in params])
loss_param = config['weight_decay'] * cgt.sum(params_flat ** 2)
loss_vec -= loss_param # / size_batch
loss = cgt.sum(loss_vec) / size_batch
# TODO_TZ f_step seems not to fail if X has wrong dim
f_step = cgt.function(inputs, outputs)
f_surr = get_surrogate_func(inputs + [Y_prec, Y_gt], outputs,
[loss_vec], params, _dbg_out=dbg_out)
return params, f_step, None, None, None, f_surr
def make_funcs(config, dbg_out={}):
net_in, net_out = hybrid_network(config['num_inputs'],
config['num_outputs'],
config['num_units'],
config['num_sto'],
dbg_out=dbg_out)
if not config['dbg_out_full']: dbg_out = {}
# def f_sample(_inputs, num_samples=1, flatten=False):
# _mean, _var = f_step(_inputs)
# _samples = []
# for _m, _v in zip(_mean, _var):
# _s = np.random.multivariate_normal(_m, np.diag(np.sqrt(_v)), num_samples)
# if flatten: _samples.extend(_s)
# else: _samples.append(_s)
# return np.array(_samples)
Y_gt = cgt.matrix("Y")
Y_prec = cgt.tensor3('V',
fixed_shape=(None, config['num_inputs'],
config['num_inputs']))
params = nn.get_parameters(net_out)
size_batch, size_out = net_out.shape
inputs, outputs = [net_in], [net_out]
if config['no_bias']:
print "Excluding bias"
params = [p for p in params if not p.name.endswith(".b")]
loss_vec = dist.gaussian.logprob(Y_gt, net_out, Y_prec)
if config['weight_decay'] > 0.:
print "Applying penalty on parameter norm"
params_flat = cgt.concatenate([p.flatten() for p in params])
loss_param = config['weight_decay'] * cgt.sum(params_flat**2)
loss_vec -= loss_param # / size_batch
loss = cgt.sum(loss_vec) / size_batch
# TODO_TZ f_step seems not to fail if X has wrong dim
f_step = cgt.function(inputs, outputs)
f_surr = get_surrogate_func(inputs + [Y_prec, Y_gt],
outputs, [loss_vec],
params,
_dbg_out=dbg_out)
return params, f_step, None, None, None, f_surr
def make_funcs(net_in, net_out, config, dbg_out=None):
def f_grad (*x):
out = f_surr(*x)
return out['loss'], out['surr_loss'], out['surr_grad']
Y = cgt.matrix("Y")
params = nn.get_parameters(net_out)
if 'no_bias' in config and config['no_bias']:
print "Excluding bias"
params = [p for p in params if not p.name.endswith(".b")]
size_out, size_batch = Y.shape[1], net_in.shape[0]
f_step = cgt.function([net_in], [net_out])
# loss_raw of shape (size_batch, 1); loss should be a scalar
# sum-of-squares loss
sigma = 0.1
loss_raw = -cgt.sum((net_out - Y) ** 2, axis=1, keepdims=True) / sigma
# negative log-likelihood
# out_sigma = cgt.exp(net_out[:, size_out:]) + 1.e-6 # positive sigma
# loss_raw = -gaussian_diagonal.logprob(
# Y, net_out,
# out_sigma
# cgt.fill(.01, [size_batch, size_out])
# )
if 'param_penal_wt' in config:
print "Applying penalty on parameter norm"
assert config['param_penal_wt'] > 0
params_flat = cgt.concatenate([p.flatten() for p in params])
loss_param = cgt.fill(cgt.sum(params_flat ** 2), [size_batch, 1])
loss_param *= config['param_penal_wt']
loss_raw += loss_param
loss = cgt.sum(loss_raw) / size_batch
# end of loss definition
f_loss = cgt.function([net_in, Y], [net_out, loss])
f_surr = get_surrogate_func([net_in, Y],
[net_out] + dbg_out,
[loss_raw], params)
return params, f_step, f_loss, f_grad, f_surr
def make_funcs(net_in, net_out, config, dbg_out=None):
def f_grad(*x):
out = f_surr(*x)
return out['loss'], out['surr_loss'], out['surr_grad']
Y = cgt.matrix("Y")
params = nn.get_parameters(net_out)
if 'no_bias' in config and config['no_bias']:
print "Excluding bias"
params = [p for p in params if not p.name.endswith(".b")]
size_out, size_batch = Y.shape[1], net_in.shape[0]
f_step = cgt.function([net_in], [net_out])
# loss_raw of shape (size_batch, 1); loss should be a scalar
# sum-of-squares loss
sigma = 0.1
loss_raw = -cgt.sum((net_out - Y)**2, axis=1, keepdims=True) / sigma
# negative log-likelihood
# out_sigma = cgt.exp(net_out[:, size_out:]) + 1.e-6 # positive sigma
# loss_raw = -gaussian_diagonal.logprob(
# Y, net_out,
# out_sigma
# cgt.fill(.01, [size_batch, size_out])
# )
if 'param_penal_wt' in config:
print "Applying penalty on parameter norm"
assert config['param_penal_wt'] > 0
params_flat = cgt.concatenate([p.flatten() for p in params])
loss_param = cgt.fill(cgt.sum(params_flat**2), [size_batch, 1])
loss_param *= config['param_penal_wt']
loss_raw += loss_param
loss = cgt.sum(loss_raw) / size_batch
# end of loss definition
f_loss = cgt.function([net_in, Y], [net_out, loss])
f_surr = get_surrogate_func([net_in, Y], [net_out] + dbg_out, [loss_raw],
params)
return params, f_step, f_loss, f_grad, f_surr
