def test_models(nnet_type):
batch_size = 6
n_epoch = 1
data = random.Random('probability')
nnet = nnet_for_testing(nnet_type)
nnet = initializers.init_standard(nnet, data)
optimizer = optimizers.SGD()
trainer = training.CD(nnet, nb_pos_steps=2, nb_neg_steps=2)
model = Model(nnet, optimizer, trainer)
# test train_on_batch
out = model.train_on_batch(data.train.data)
assert out.size == 1
# predict_on_batch
out = model.predict_on_batch(data.valid.data)
assert out.size == 1
# test fit
out = model.fit(data.train.data, n_epoch=n_epoch, batch_size=batch_size)
assert isinstance(out, History)
# test validation data
out = model.fit(data.train.data, n_epoch=n_epoch, batch_size=batch_size,
validation_data=data.valid.data)
assert isinstance(out, History)
def run_rbm(nnet_type='rbm'):
# parameters
batch_size = 100
n_epoch = 2
results_folder = 'fast_demo'
# just makes a tiny neural network
if nnet_type == 'rbm':
layer_size_list = [100, 10]
topology_dict = {0: {1}}
elif nnet_type == 'dbm':
layer_size_list = [100, 10, 5]
topology_dict = {0: {1}, 1: {2}}
elif nnet_type == 'dbm_complex':
layer_size_list = [100, 10, 5, 2]
topology_dict = {0: {1, 3}, 1: {2}, 2: {3}}
else:
raise NotImplementedError
# this is a small dataset useful for demos
data = fast.Fast('probability')
# create and initialize the rbm
dbm = DBM(layer_size_list, topology_dict)
dbm = init_standard(dbm, data)
# make the model
optimizer = optimizers.SGD()
trainer = training.CD(dbm)
model = Model(dbm, optimizer, trainer)
# prepare output paths
filepath = os.path.dirname(os.path.abspath(__file__))
save_folder = os.path.abspath(
os.path.join(filepath, '..', 'results', results_folder))
save_dict = standard_save_folders(save_folder, overwrite=True)
# these callbacks monitor progress
cb_csv = callbacks.CSVLogger(save_dict['csv'], separator='\t')
cb_ais = callbacks.AISCallback(dbm, 100, 1000, epoch_ls=[0, 1])
cb_period = callbacks.PeriodicSave(save_dict['weights'], [0, 1],
opt_path=save_dict['opt_weights'])
cb_plot = callbacks.PlotCallback(save_dict['plots'], save_dict['csv'])
cb_summary = callbacks.SummaryCallback(save_folder, save_dict['csv'])
callbacks_ls = [cb_csv, cb_ais, cb_period, cb_plot, cb_summary]
# do the actual training
history = model.fit(data.train.data,
batch_size=batch_size,
n_epoch=n_epoch,
callbacks=callbacks_ls,
validation_data=data.valid.data)
def test_regularization_fit(nnet_type):
batch_size = 100
n_epoch = 1
W_reg_type = 'l1_l2'
b_reg_type = 'l1_l2'
data = random.Random('probability')
nnet = nnet_for_testing(nnet_type, W_reg_type, b_reg_type)
nnet = initializers.init_standard(nnet, data)
optimizer = optimizers.SGD()
trainer = training.CD(nnet)
model = Model(nnet, optimizer, trainer)
# test fit
out = model.fit(data.train.data, n_epoch=n_epoch, batch_size=batch_size)
assert isinstance(out, History)
def test_init_standard(nnet_type):
nnet = nnet_for_testing(nnet_type)
dataset = random.Random('probability')
b_ls = []
for layer in nnet.layers:
b_ls.append(B.eval(layer.b.shape))
W_ls = []
for synapse in nnet.synapses:
W_ls.append(B.eval(synapse.W.shape))
nnet = initializers.init_standard(nnet, dataset)
for size, layer in zip(b_ls, nnet.layers):
assert size == B.eval(layer.b.shape)
for size, synapse in zip(W_ls, nnet.synapses):
assert_allclose(size, B.eval(synapse.W.shape))
def test_ais_vs_exact():
n_runs = 5
n_betas = 5
nnet = nnet_for_testing('rbm')
dataset = Random('probability')
data = B.get_value(dataset.train.data)
nnet = init_standard(nnet, dataset)
ais = estimators.AIS(nnet, data, n_runs, n_betas=n_betas)
ais.run_logZ()
logZ_out, logZ_low_out, logZ_high_out = ais.estimate_log_error_Z()
logZ = estimators.exact_logZ(nnet)
assert logZ >= logZ_low_out
assert logZ <= logZ_high_out
def run_rbm():
# parameters
batch_size = 100
n_epoch = 1000 # 1000 for true literature comparison, but 250 should be good
results_folder = 'long_demo'
# needed datasets, standard in field is probability for train, sampled for test
data_train = mnist.MNIST('probability')
data_valid = mnist.MNIST('sampled')
# this is a large neural network
layer_size_list = [784, 500]
topology_dict = {0: {1}}
# training parameters
# lr rate decays from 0.01 to 0.001 over training epochs
# momentum is ramped up (controlled by schedule_decay) to max
# and then ramped back down to a smaller value for the last 50 epochs
n_batch = np.floor(data_train.train.num_samples/batch_size)
decay = (1/0.1-1)/(n_epoch*data_train.train.num_samples/batch_size)
optimizer_kwargs = {'lr' : 0.01,
'momentum' : 0.9,
'nesterov' : True,
'decay' : decay,
'schedule_decay': 0.004,
'mom_iter_max': n_batch*(n_epoch-50)}
# create and initialize the rbm
W_regularizer = regularizers.l2(l=2e-4)
dbm = DBM(layer_size_list, topology_dict, W_regularizer=W_regularizer)
dbm = init_standard(dbm, data_train)
# make the model
optimizer = optimizers.SGD(**optimizer_kwargs)
trainer = training.PCD(dbm, nb_pos_steps=25, nb_neg_steps=25, batch_size=batch_size)
model = Model(dbm, optimizer, trainer)
# prepare output paths
filepath = os.path.dirname(os.path.abspath(__file__))
save_folder = os.path.abspath(os.path.join(filepath, '..', 'results', results_folder))
save_dict = standard_save_folders(save_folder, overwrite=True)
# epochs to monitor
temp = [0, 1, 5, 10]
fixed_ls = temp + [n_epoch-t for t in temp] + [n_epoch-2]
epoch_ls = list(set(list(range(0, n_epoch, 25)) + fixed_ls))
# these callbacks monitor progress
cb_csv = callbacks.CSVLogger(save_dict['csv'], separator='\t')
cb_ais = callbacks.AISCallback(dbm, 1000, 30000, epoch_ls=epoch_ls)
cb_period = callbacks.PeriodicSave(save_dict['weights'], epoch_ls,
opt_path=save_dict['opt_weights'])
cb_opt = callbacks.OptimizerSpy()
cb_plot = callbacks.PlotCallback(save_dict['plots'], save_dict['csv'])
cb_summary = callbacks.SummaryCallback(save_folder, save_dict['csv'])
callbacks_ls = [cb_csv, cb_ais, cb_period, cb_opt, cb_plot, cb_summary]
# do the actual training
history = model.fit(data_train.train.data,
batch_size=batch_size,
n_epoch=n_epoch,
callbacks=callbacks_ls,
validation_data=data_valid.valid.data)
def train_check():
"""
This is a full implementation test. On the fast dataset with the training
setup below, the final log likelihood on the validation data should be
around -34.5 nats.
I left this out of the automatic pytests since this takes around
2 minutes to run on a CPU and really should be run only if all the pytest
tests were successful.
"""
# needed datasets, standard in field is probability for train, sampled for test
data_prob = fast.Fast('probability')
data_sampled = fast.Fast('sampled')
# rbm setup
layer_size_list = [data_prob.train.num_pixels, 16]
topology_dict = {0: {1}}
W_regularizer = regularizers.l1_l2(l1=1e-5, l2=1e-5)
# training parameters
batch_size = 100
n_epoch = 100
n_batch = np.floor(data_prob.train.num_samples / batch_size)
optimizer_kwargs = {
'lr': 0.01,
'momentum': 0.9,
'nesterov': True,
'decay': 1.8e-5,
'schedule_decay': 0.004,
'mom_iter_max': n_batch * (n_epoch - 50)
}
# initialize the model
dbm = DBM(layer_size_list, topology_dict, W_regularizer=W_regularizer)
dbm = init_standard(dbm, data_prob)
optimizer = optimizers.SGD(**optimizer_kwargs)
trainer = training.PCD(dbm, batch_size=batch_size)
model = Model(dbm, optimizer, trainer)
# list of epochs to check on performance, want a few at start/end
temp = [0, 1]
fixed_ls = temp + [n_epoch - t for t in temp] + [n_epoch - 2]
n_runs = 100
n_betas = 10000
# prep the callbacks
filepath = os.path.dirname(os.path.abspath(__file__))
save_folder = os.path.abspath(
os.path.join(filepath, '..', '..', 'results', 'fast_checks'))
save_dict = standard_save_folders(save_folder, overwrite=True)
cb_csv = callbacks.CSVLogger(save_dict['csv'], separator='\t')
cb_ais = callbacks.AISCallback(dbm, n_runs, n_betas, epoch_ls=fixed_ls)
callbacks_ls = [cb_csv, cb_ais]
# do the actual training
history = model.fit(data_prob.train.data,
batch_size=batch_size,
n_epoch=n_epoch,
callbacks=callbacks_ls,
validation_data=data_sampled.valid.data)
# check how training went
val_prob = history.history['val_prob']
best_val_prob = np.nanmax(val_prob)
# Bare minimum goal
assert best_val_prob >= -40.0
# Ultimate goal
assert best_val_prob >= -35.0