def main():
np.random.seed(1)
xtrain, ytrain, params_true = make_data_linreg_1d()
N = xtrain.shape[0]
Xtrain = np.c_[np.ones(N), xtrain] # add column of 1s
w_ols, loss_ols = LinregModel.ols_fit(Xtrain, ytrain)
expt_configs = make_expt_config(N)
nexpts = len(expt_configs)
print nexpts
nrows, ncols = nsubplots(nexpts)
#nrows, ncols = 4, 2
loss_trace_fig = plt.figure("loss trace fig")
param_trace_fig = plt.figure("param trace fig")
folder = 'figures'
for expt_num, config in enumerate(expt_configs):
logger = sgd.SGDLogger(print_freq=10)
np.random.seed(1)
batchifier = sgd.MiniBatcher(Xtrain, ytrain, config['batch_size'])
initial_params = np.zeros(2)
lr_fun = lambda(iter): sgd.get_learning_rate_exp_decay(iter,
config['init_lr'], config['lr_decay'])
ttl = config_to_str(config)
print '\nstarting experiment {}'.format(ttl)
print config
obj_fun = LinregModel.objective
#grad_fun = LinregModel.gradient
grad_fun = autograd.grad(obj_fun)
result = sgd.sgd_minimize(initial_params, obj_fun, grad_fun,
batchifier, config['n_steps'], lr_fun, config['momentum'],
callback=logger.update)
print result
plotnum = expt_num + 1
ax = loss_trace_fig.add_subplot(nrows, ncols, plotnum)
plot_loss_trace(logger.obj_trace, loss_ols, ax)
ax.set_title(ttl)
ax = param_trace_fig.add_subplot(nrows, ncols, plotnum)
loss_fun = lambda w0, w1: LinregModel.objective([w0, w1], xtrain, ytrain)
plot_error_surface(loss_fun, params_true, ax)
plot_param_trace(logger.param_trace, ax)
ax.set_title(ttl)
plt.figure("loss trace fig")
fname = os.path.join(folder, 'linreg_1d_sgd_loss_trace.png')
plt.savefig(fname)
plt.figure("param trace fig")
fname = os.path.join(folder, 'linreg_1d_sgd_param_trace.png')
plt.savefig(fname)
plt.show()
def run_expt(config, loss_opt=0):
ttl = config_to_str(config)
print '\nstarting experiment {}'.format(ttl)
print config
Xtrain, Ytrain, params_true, true_fun, fun_name = \
demo.make_data_linreg_1d(config['N'], config['fun_type'])
data_dim = Xtrain.shape[1]
N = Xtrain.shape[0]
Xtrain, Ytrain = opt.shuffle_data(Xtrain, Ytrain)
model_type = config['model_type']
if model_type == 'linear':
model = LinregModel(data_dim, add_ones=True)
params, loss = model.ols_fit(Xtrain, Ytrain)
elif model_type[0:3] == 'mlp':
_, layer_sizes = model_type.split(':')
layer_sizes = [int(n) for n in layer_sizes.split('-')]
model = MLP(layer_sizes, 'regression', L2_reg=0.001)
else:
raise ValueError('unknown model type {}'.format(model_type))
initial_params = model.init_params()
param_dim = len(initial_params)
plot_data = (data_dim == 1)
plot_params = (param_dim == 2)
nplots = 1
if plot_data:
nplots += 1
if plot_params:
nplots += 1
plot_rows, plot_cols = util.nsubplots(nplots)
if config['optimizer'] == 'BFGS':
obj_fun = lambda params: model.PNLL(params, Xtrain, Ytrain)
grad_fun = autograd.grad(obj_fun)
logger = opt.OptimLogger(lambda params: obj_fun(params), eval_freq=1,
store_freq=1, print_freq=1)
params, obj = opt.bfgs(obj_fun, grad_fun, initial_params, config['num_epochs'],
logger.callback)
if config['optimizer'] == 'SGD':
B = config['batch_size']
M = N / B # num_minibatches_per_epoch (num iter per epoch)
max_iters = config['num_epochs'] * M
grad_fun_with_iter = opt.build_batched_grad(model.gradient, config['batch_size'], Xtrain, Ytrain)
#obj_fun = opt.build_batched_grad(model.PNLL, config['batch_size'], Xtrain, Ytrain)
obj_fun = lambda params: model.PNLL(params, Xtrain, Ytrain)
sf = config.get('store_freq', M)
logger = opt.OptimLogger(obj_fun, eval_freq=sf, store_freq=sf, print_freq=0)
sgd_fun = config['sgd_fun']
if config['lr_tune']==True:
eval_fun = lambda params: model.PNLL(params, Xtrain, Ytrain)
lr, lrs, scores = opt.lr_tuner(eval_fun, 'grid', sgd_fun, grad_fun_with_iter,
initial_params, int(np.ceil(max_iters*0.1)))
print 'lr tuner chose lr {:0.3f}'.format(lr)
print lrs
print scores
config['lr_init'] = lr
lr_fun = lambda iter: opt.lr_exp_decay(iter, config['lr_init'],
config['lr_decay'], config['lr_step'])
params, obj = sgd_fun(obj_fun, grad_fun_with_iter, initial_params,
max_iters, logger.callback, lr_fun)
training_loss = model.PNLL(params, Xtrain, Ytrain)
print 'finished fitting, training loss {:0.3g}, {} obj calls, {} grad calls'.\
format(training_loss, model.num_obj_fun_calls, model.num_grad_fun_calls)
fig = plt.figure()
ax = fig.add_subplot(plot_rows, plot_cols, 1)
opt.plot_loss_trace(logger.eval_trace, loss_opt, ax)
ax.set_title('final objective {:0.3g}'.format(training_loss))
ax.set_xlabel('epochs')
if plot_data:
ax = fig.add_subplot(plot_rows, plot_cols, 2)
predict_fun = lambda X: model.predictions(params, X)
demo.plot_data_and_predictions_1d(Xtrain, Ytrain, true_fun, predict_fun, ax)
if plot_params:
ax = fig.add_subplot(plot_rows, plot_cols, 3)
loss_fun = lambda w0, w1: model.PNLL(np.array([w0, w1]), Xtrain, Ytrain)
demo.plot_error_surface_2d(loss_fun, params, params_true, config['fun_type'], ax)
demo.plot_param_trace_2d(logger.param_trace, ax)
ttl = config_to_str(config) # recompute in case lr has been estimated
fig.suptitle(ttl)
folder = 'figures/linreg-sgd'
fname = os.path.join(folder, 'linreg_1d_sgd_{}.png'.format(ttl))
plt.savefig(fname)
return training_loss
def main():
np.random.seed(1)
folder = 'figures/linreg-sgd'
N = 50
num_epochs = 100
#fun_type = 'linear'
fun_type = 'sine'
#fun_type = 'quad'
#model_type = 'linear'
model_type = 'mlp:1-10-1'
configs = []
# BFGS has to be the first config, in order to compute loss_opt
configs.append({
'fun_type': fun_type,
'N': N,
'model': model_type,
'optimizer': 'BFGS'
})
configs.append({
'fun_type': fun_type,
'N': N,
'model': model_type,
'optimizer': 'SGD',
'batch_size': 10,
'num_epochs': num_epochs,
'method': 'Rprop',
'improved': True
})
configs.append({
'fun_type': fun_type,
'N': N,
'model': model_type,
'optimizer': 'SGD',
'batch_size': 10,
'num_epochs': num_epochs,
'lr_fun': 'exp',
'init_lr': 0.05,
'lr_decay': 0.9,
'method': 'momentum',
'mass': 0.9
})
configs.append({
'fun_type': fun_type,
'N': N,
'model': model_type,
'optimizer': 'SGD',
'batch_size': 10,
'num_epochs': num_epochs,
'lr_fun': 'exp',
'init_lr': 0.05,
'lr_decay': 0.9,
'method': 'RMSprop',
'grad_sq_decay': 0.9
})
configs.append({
'fun_type': fun_type,
'N': N,
'model': model_type,
'optimizer': 'SGD',
'batch_size': 10,
'num_epochs': num_epochs,
'lr_fun': 'exp',
'init_lr': 0.05,
'lr_decay': 0.9,
'method': 'ADAM',
'grad_decay': 0.9,
'grad_sq_decay': 0.999
})
configs.append({
'fun_type': fun_type,
'N': N,
'model': model_type,
'optimizer': 'SGD',
'batch_size': 10,
'num_epochs': num_epochs,
'lr_fun': 'const',
'init_lr': 0.05,
'method': 'ADAM',
'grad_decay': 0.9,
'grad_sq_decay': 0.999
})
configs.append({
'fun_type': fun_type,
'N': N,
'model': model_type,
'optimizer': 'SGD',
'batch_size': 10,
'num_epochs': num_epochs,
'lr_fun': 'const',
'init_lr': 0.001,
'method': 'ADAM',
'grad_decay': 0.9,
'grad_sq_decay': 0.999
})
params_opt = None
loss_opt = None
for expt_num, config in enumerate(configs):
np.random.seed(1)
ttl = config_to_str(config)
print '\nstarting experiment {}'.format(ttl)
print config
Xtrain, Ytrain, params_true, true_fun, fun_name = make_data_linreg_1d(\
config['N'], config['fun_type'])
data_dim = Xtrain.shape[1]
if model_type == 'linear':
model = LinregModel(data_dim, add_ones=True)
params_opt, loss_opt = model.ols_fit(Xtrain, Ytrain)
elif model_type[0:3] == 'mlp':
_, layer_sizes = model_type.split(':')
layer_sizes = [int(n) for n in layer_sizes.split('-')]
model = MLP(layer_sizes, 'regression', L2_reg=0.001)
else:
raise ValueError('unknown model type {}'.format(model_type))
initial_params = model.init_params()
obj_fun = model.PNLL
grad_fun = model.gradient
param_dim = len(initial_params)
plot_data = (data_dim == 1)
plot_params = (param_dim == 2)
nplots = 2
if plot_data:
nplots += 1
if plot_params:
nplots += 1
plot_rows, plot_cols = util.nsubplots(nplots)
if config['optimizer'] == 'BFGS':
logger = sgd.MinimizeLogger(obj_fun,
grad_fun, (Xtrain, Ytrain),
print_freq=1,
store_params=True)
params, loss, n_fun_evals = sgd.bfgs_fit(initial_params, obj_fun,
grad_fun,
(Xtrain, Ytrain),
logger.update)
num_props = n_fun_evals * config['N']
loss_avg = loss
if params_opt is None:
params_opt = params
loss_opt = loss
if config['optimizer'] == 'SGD':
logger = sgd.SGDLogger(print_freq=20, store_params=True)
if config.has_key('lr_fun'):
if config['lr_fun'] == 'exp':
lr_fun = lambda iter, epoch: sgd.lr_exp_decay(
iter, config['init_lr'], config['lr_decay'])
if config['lr_fun'] == 'const':
lr_fun = lambda iter, epoch: config['init_lr']
else:
lr_fun = None
if config['method'] == 'momentum':
sgd_updater = sgd.SGDMomentum(lr_fun, config['mass'])
if config['method'] == 'RMSprop':
sgd_updater = sgd.RMSprop(lr_fun, config['grad_sq_decay'])
if config['method'] == 'ADAM':
sgd_updater = sgd.ADAM(lr_fun, config['grad_decay'],
config['grad_sq_decay'])
if config['method'] == 'Rprop':
sgd_updater = sgd.Rprop(improved_Rprop=config['improved'])
params, loss, num_minibatch_updates, params_avg, loss_avg = sgd.sgd_minimize(
initial_params, obj_fun, grad_fun, Xtrain, Ytrain,
config['batch_size'], config['num_epochs'], sgd_updater,
logger.update)
num_props = num_minibatch_updates * config['batch_size']
print 'finished fitting, {} obj, {} grad, {} props'.format(
model.num_obj_fun_calls, model.num_grad_fun_calls, num_props)
fig = plt.figure()
ax = fig.add_subplot(plot_rows, plot_cols, 1)
plot_loss_trace(logger.obj_trace, loss_opt, ax, num_props)
ax.set_title('final objective {:0.3f}, {:0.3f}'.format(loss, loss_avg))
ax = fig.add_subplot(plot_rows, plot_cols, 2)
ax.plot(logger.grad_norm_trace)
ax.set_title('gradient norm vs num updates')
if plot_data:
ax = fig.add_subplot(plot_rows, plot_cols, 3)
predict_fun = lambda X: model.predictions(params, X)
plot_data_and_predictions_1d(Xtrain, Ytrain, true_fun, predict_fun,
ax)
if plot_params:
ax = fig.add_subplot(plot_rows, plot_cols, 4)
loss_fun = lambda w0, w1: model.PNLL([w0, w1], Xtrain, Ytrain)
plot_error_surface_2d(loss_fun, params_opt, params_true, fun_type,
ax)
plot_param_trace_2d(logger.param_trace, ax)
fig.suptitle(ttl)
fname = os.path.join(folder, 'linreg_1d_sgd_{}.png'.format(ttl))
plt.savefig(fname)
plt.show()
def main():
np.random.seed(1)
xtrain, ytrain, params_true = make_data_linreg_1d()
N = xtrain.shape[0]
Xtrain = np.c_[np.ones(N), xtrain] # add column of 1s
w_ols, loss_ols = LinregModel.ols_fit(Xtrain, ytrain)
expt_configs = make_expt_config(N)
nexpts = len(expt_configs)
print nexpts
nrows, ncols = nsubplots(nexpts)
#nrows, ncols = 4, 2
loss_trace_fig = plt.figure("loss trace fig")
param_trace_fig = plt.figure("param trace fig")
folder = 'figures'
for expt_num, config in enumerate(expt_configs):
logger = sgd.SGDLogger(print_freq=10)
np.random.seed(1)
batchifier = sgd.MiniBatcher(Xtrain, ytrain, config['batch_size'])
initial_params = np.zeros(2)
lr_fun = lambda (iter): sgd.get_learning_rate_exp_decay(
iter, config['init_lr'], config['lr_decay'])
ttl = config_to_str(config)
print '\nstarting experiment {}'.format(ttl)
print config
obj_fun = LinregModel.objective
#grad_fun = LinregModel.gradient
grad_fun = autograd.grad(obj_fun)
result = sgd.sgd_minimize(initial_params,
obj_fun,
grad_fun,
batchifier,
config['n_steps'],
lr_fun,
config['momentum'],
callback=logger.update)
print result
plotnum = expt_num + 1
ax = loss_trace_fig.add_subplot(nrows, ncols, plotnum)
plot_loss_trace(logger.obj_trace, loss_ols, ax)
ax.set_title(ttl)
ax = param_trace_fig.add_subplot(nrows, ncols, plotnum)
loss_fun = lambda w0, w1: LinregModel.objective([w0, w1], xtrain,
ytrain)
plot_error_surface(loss_fun, params_true, ax)
plot_param_trace(logger.param_trace, ax)
ax.set_title(ttl)
plt.figure("loss trace fig")
fname = os.path.join(folder, 'linreg_1d_sgd_loss_trace.png')
plt.savefig(fname)
plt.figure("param trace fig")
fname = os.path.join(folder, 'linreg_1d_sgd_param_trace.png')
plt.savefig(fname)
plt.show()
def main():
np.random.seed(1)
folder = 'figures/linreg-sgd'
N = 50
num_epochs = 100
#fun_type = 'linear'
fun_type = 'sine'
#fun_type = 'quad'
#model_type = 'linear'
model_type = 'mlp:1-10-1'
configs = []
# BFGS has to be the first config, in order to compute loss_opt
configs.append({'fun_type': fun_type, 'N': N, 'model': model_type,
'optimizer': 'BFGS'})
configs.append({'fun_type': fun_type, 'N': N, 'model': model_type,
'optimizer': 'SGD', 'batch_size': 10, 'num_epochs': num_epochs,
'method': 'Rprop', 'improved': True})
configs.append({'fun_type': fun_type, 'N': N, 'model': model_type,
'optimizer': 'SGD', 'batch_size': 10, 'num_epochs': num_epochs,
'lr_fun': 'exp', 'init_lr': 0.05, 'lr_decay': 0.9,
'method': 'momentum', 'mass': 0.9})
configs.append({'fun_type': fun_type, 'N': N, 'model': model_type,
'optimizer': 'SGD', 'batch_size': 10, 'num_epochs': num_epochs,
'lr_fun': 'exp', 'init_lr': 0.05, 'lr_decay': 0.9,
'method': 'RMSprop', 'grad_sq_decay': 0.9})
configs.append({'fun_type': fun_type, 'N': N, 'model': model_type,
'optimizer': 'SGD', 'batch_size': 10, 'num_epochs': num_epochs,
'lr_fun': 'exp', 'init_lr': 0.05, 'lr_decay': 0.9,
'method': 'ADAM', 'grad_decay': 0.9, 'grad_sq_decay': 0.999})
configs.append({'fun_type': fun_type, 'N': N, 'model': model_type,
'optimizer': 'SGD', 'batch_size': 10, 'num_epochs': num_epochs,
'lr_fun': 'const', 'init_lr': 0.05,
'method': 'ADAM', 'grad_decay': 0.9, 'grad_sq_decay': 0.999})
configs.append({'fun_type': fun_type, 'N': N, 'model': model_type,
'optimizer': 'SGD', 'batch_size': 10, 'num_epochs': num_epochs,
'lr_fun': 'const', 'init_lr': 0.001,
'method': 'ADAM', 'grad_decay': 0.9, 'grad_sq_decay': 0.999})
params_opt = None
loss_opt = None
for expt_num, config in enumerate(configs):
np.random.seed(1)
ttl = config_to_str(config)
print '\nstarting experiment {}'.format(ttl)
print config
Xtrain, Ytrain, params_true, true_fun, fun_name = make_data_linreg_1d(\
config['N'], config['fun_type'])
data_dim = Xtrain.shape[1]
if model_type == 'linear':
model = LinregModel(data_dim, add_ones=True)
params_opt, loss_opt = model.ols_fit(Xtrain, Ytrain)
elif model_type[0:3] == 'mlp':
_, layer_sizes = model_type.split(':')
layer_sizes = [int(n) for n in layer_sizes.split('-')]
model = MLP(layer_sizes, 'regression', L2_reg=0.001)
else:
raise ValueError('unknown model type {}'.format(model_type))
initial_params = model.init_params()
obj_fun = model.PNLL
grad_fun = model.gradient
param_dim = len(initial_params)
plot_data = (data_dim == 1)
plot_params = (param_dim == 2)
nplots = 2
if plot_data:
nplots += 1
if plot_params:
nplots += 1
plot_rows, plot_cols = util.nsubplots(nplots)
if config['optimizer'] == 'BFGS':
logger = sgd.MinimizeLogger(obj_fun, grad_fun, (Xtrain, Ytrain), print_freq=1, store_params=True)
params, loss, n_fun_evals = sgd.bfgs_fit(initial_params, obj_fun, grad_fun, (Xtrain, Ytrain), logger.update)
num_props = n_fun_evals * config['N']
loss_avg = loss
if params_opt is None:
params_opt = params
loss_opt = loss
if config['optimizer'] == 'SGD':
logger = sgd.SGDLogger(print_freq=20, store_params=True)
if config.has_key('lr_fun'):
if config['lr_fun'] == 'exp':
lr_fun = lambda iter, epoch: sgd.lr_exp_decay(iter, config['init_lr'], config['lr_decay'])
if config['lr_fun'] == 'const':
lr_fun = lambda iter, epoch: config['init_lr']
else:
lr_fun = None
if config['method'] == 'momentum':
sgd_updater = sgd.SGDMomentum(lr_fun, config['mass'])
if config['method'] == 'RMSprop':
sgd_updater = sgd.RMSprop(lr_fun, config['grad_sq_decay'])
if config['method'] == 'ADAM':
sgd_updater = sgd.ADAM(lr_fun, config['grad_decay'], config['grad_sq_decay'])
if config['method'] == 'Rprop':
sgd_updater = sgd.Rprop(improved_Rprop = config['improved'])
params, loss, num_minibatch_updates, params_avg, loss_avg = sgd.sgd_minimize(initial_params, obj_fun, grad_fun,
Xtrain, Ytrain, config['batch_size'], config['num_epochs'], sgd_updater, logger.update)
num_props = num_minibatch_updates * config['batch_size']
print 'finished fitting, {} obj, {} grad, {} props'.format(model.num_obj_fun_calls, model.num_grad_fun_calls, num_props)
fig = plt.figure()
ax = fig.add_subplot(plot_rows, plot_cols, 1)
plot_loss_trace(logger.obj_trace, loss_opt, ax, num_props)
ax.set_title('final objective {:0.3f}, {:0.3f}'.format(loss, loss_avg))
ax = fig.add_subplot(plot_rows, plot_cols, 2)
ax.plot(logger.grad_norm_trace)
ax.set_title('gradient norm vs num updates')
if plot_data:
ax = fig.add_subplot(plot_rows, plot_cols, 3)
predict_fun = lambda X: model.predictions(params, X)
plot_data_and_predictions_1d(Xtrain, Ytrain, true_fun, predict_fun, ax)
if plot_params:
ax = fig.add_subplot(plot_rows, plot_cols, 4)
loss_fun = lambda w0, w1: model.PNLL([w0, w1], Xtrain, Ytrain)
plot_error_surface_2d(loss_fun, params_opt, params_true, fun_type, ax)
plot_param_trace_2d(logger.param_trace, ax)
fig.suptitle(ttl)
fname = os.path.join(folder, 'linreg_1d_sgd_{}.png'.format(ttl))
plt.savefig(fname)
plt.show()
def run_expt(config, loss_opt=0):
ttl = config_to_str(config)
print '\nstarting experiment {}'.format(ttl)
print config
Xtrain, Ytrain, params_true, true_fun, fun_name = demo.make_data_linreg_1d(config['N'], config['fun_type'])
data_dim = Xtrain.shape[1]
N = Xtrain.shape[0]
Xtrain, Ytrain = opt.shuffle_data(Xtrain, Ytrain)
model_type = config['model_type']
if model_type == 'linear':
model = LinregModel(data_dim, add_ones=True)
params, loss = model.ols_fit(Xtrain, Ytrain)
elif model_type[0:3] == 'mlp':
_, layer_sizes = model_type.split(':')
layer_sizes = [int(n) for n in layer_sizes.split('-')]
model = MLP(layer_sizes, 'regression', L2_reg=0.001, Ntrain=N)
else:
raise ValueError('unknown model type {}'.format(model_type))
initial_params = model.init_params()
param_dim = len(initial_params)
plot_data = (data_dim == 1)
plot_params = (param_dim == 2)
nplots = 2
if plot_data:
nplots += 1
if plot_params:
nplots += 1
plot_rows, plot_cols = util.nsubplots(nplots)
if config['optimizer'] == 'BFGS':
obj_fun = lambda params: model.PNLL(params, Xtrain, Ytrain)
logger = opt.OptimLogger(lambda params, iter: obj_fun(params), store_freq=1, print_freq=10)
params = opt.bfgs(autograd.value_and_grad(obj_fun), initial_params, logger.callback, config['num_epochs'])
if config['optimizer'] == 'SGD':
B = config['batch_size']
M = N / B # num_minibatches_per_epoch (num iter per epoch)
max_iters = config['num_epochs'] * M
grad_fun = opt.build_batched_grad(model.gradient, config['batch_size'], Xtrain, Ytrain)
#obj_fun = opt.build_batched_grad(model.PNLL, config['batch_size'], Xtrain, Ytrain)
obj_fun = lambda params, iter: model.PNLL(params, Xtrain, Ytrain)
logger = opt.OptimLogger(obj_fun, store_freq=M, print_freq=M*10, store_params=plot_params)
if config.has_key('lr_fun'):
if config['lr_fun'] == 'exp':
lr_fun = lambda iter: opt.lr_exp_decay(iter, config['init_lr'], config['lr_decay'])
elif config['lr_fun'] == 'const':
lr_fun = opt.const_lr(config['init_lr'])
else:
raise ValueError('Unknown lr-fun {}'.format(lr_fun))
#sgd_fun = config['sgd-fun']
#params = sgd_fun(grad_fun, initial_params, logger.callback, \
# max_iters, lr_fun, *config['args'])
if config['sgd-method'] == 'momentum':
params = opt.sgd(grad_fun, initial_params, logger.callback, \
max_iters, lr_fun, config['mass'])
elif config['sgd-method'] == 'RMSprop':
params = opt.rmsprop(grad_fun, initial_params, logger.callback, \
max_iters, lr_fun, config['grad_sq_decay'])
elif config['sgd-method'] == 'ADAM':
params = opt.adam(grad_fun, initial_params, logger.callback, \
max_iters, lr_fun, config['grad_decay'], config['grad_sq_decay'])
elif config['sgd-method'] == 'AutoADAM':
eval_fn = lambda params: model.PNLL(params, Xtrain, Ytrain)
params, lr, scores = opt.autoadam(grad_fun, initial_params, logger.callback, \
max_iters, eval_fn, config['auto-method'])
config['init_lr'] = lr
config['lr_fun'] = 'const'
ttl = config_to_str(config)
print 'autoadam: chose {:0.3f} as lr'.format(lr)
print scores
else:
raise ValueError('Unknown SGD method {}'.format(config['method']))
training_loss = model.PNLL(params, Xtrain, Ytrain)
print 'finished fitting, training loss {:0.3f}, {} obj calls, {} grad calls'.\
format(training_loss, model.num_obj_fun_calls, model.num_grad_fun_calls)
fig = plt.figure()
ax = fig.add_subplot(plot_rows, plot_cols, 1)
opt.plot_loss_trace(logger.obj_trace, loss_opt, ax)
ax.set_title('final objective {:0.3f}'.format(training_loss))
ax.set_xlabel('epochs')
ax = fig.add_subplot(plot_rows, plot_cols, 2)
ax.plot(logger.grad_norm_trace)
ax.set_title('gradient norm vs num updates')
if plot_data:
ax = fig.add_subplot(plot_rows, plot_cols, 3)
predict_fun = lambda X: model.predictions(params, X)
demo.plot_data_and_predictions_1d(Xtrain, Ytrain, true_fun, predict_fun, ax)
if plot_params:
ax = fig.add_subplot(plot_rows, plot_cols, 4)
loss_fun = lambda w0, w1: model.PNLL(np.array([w0, w1]), Xtrain, Ytrain)
demo.plot_error_surface_2d(loss_fun, params, params_true, config['fun_type'], ax)
demo.plot_param_trace_2d(logger.param_trace, ax)
fig.suptitle(ttl)
folder = 'figures/linreg-sgd'
fname = os.path.join(folder, 'linreg_1d_sgd_{}.png'.format(ttl))
plt.savefig(fname)
return training_loss
