def test(self, test_X=None):
log.maybeLog(self.logger, "\nTesting---------\n")
if test_X is None:
log.maybeLog(self.logger, "Testing using data given during initialization of GSN.\n")
test_X = self.test_X
if test_X is None:
log.maybeLog(self.logger, "\nPlease provide a test dataset!\n")
raise AssertionError("Please provide a test dataset")
else:
log.maybeLog(self.logger, "Testing using data provided to test function.\n")
###########
# TESTING #
###########
n_examples = 100
tests = test_X.get_value()[0:n_examples]
noisy_tests = self.f_noise(test_X.get_value()[0:n_examples])
cost, reconstructed = self.f_recon(noisy_tests)
# Concatenate stuff if it is an image
if self.is_image:
stacked = numpy.vstack([numpy.vstack([tests[i*10 : (i+1)*10], noisy_tests[i*10 : (i+1)*10], reconstructed[i*10 : (i+1)*10]]) for i in range(10)])
number_reconstruction = PIL.Image.fromarray(tile_raster_images(stacked, (self.image_height,self.image_width), (10,30)))
number_reconstruction.save(self.outdir+'gsn_image_reconstruction_test.png')
# Otherwise, save reconstructed numpy array as csv
else:
numpy.savetxt(self.outdir+'gsn_reconstruction_test.csv', reconstructed, delimiter=",")
log.maybeLog(self.logger, "----------------\n\nAverage test cost is "+str(cost)+"\n\n-----------------")
def plot_samples(self, epoch_number="", leading_text="", n_samples=400):
to_sample = time.time()
initial = self.test_X.get_value(borrow=True)[:1]
rand_idx = numpy.random.choice(range(self.test_X.get_value(borrow=True).shape[0]))
rand_init = self.test_X.get_value(borrow=True)[rand_idx:rand_idx+1]
V, _ = self.sample(initial, n_samples)
rand_V, _ = self.sample(rand_init, n_samples)
img_samples = PIL.Image.fromarray(tile_raster_images(V, (self.image_height, self.image_width), closest_to_square_factors(n_samples)))
rand_img_samples = PIL.Image.fromarray(tile_raster_images(rand_V, (self.image_height, self.image_width), closest_to_square_factors(n_samples)))
fname = self.outdir+leading_text+'samples_epoch_'+str(epoch_number)+'.png'
img_samples.save(fname)
rfname = self.outdir+leading_text+'samples_rand_epoch_'+str(epoch_number)+'.png'
rand_img_samples.save(rfname)
log.maybeLog(self.logger, 'Took ' + make_time_units_string(time.time() - to_sample) + ' to sample '+str(n_samples*2)+' numbers')
def plot_samples(self, epoch_number="", leading_text="", n_samples=400):
to_sample = time.time()
initial = self.test_X.get_value(borrow=True)[:1]
rand_idx = numpy.random.choice(range(self.test_X.get_value(borrow=True).shape[0]))
rand_init = self.test_X.get_value(borrow=True)[rand_idx:rand_idx+1]
V, _ = self.sample(initial, n_samples)
rand_V, _ = self.sample(rand_init, n_samples)
img_samples = PIL.Image.fromarray(tile_raster_images(V, (self.image_height, self.image_width), closest_to_square_factors(n_samples)))
rand_img_samples = PIL.Image.fromarray(tile_raster_images(rand_V, (self.image_height, self.image_width), closest_to_square_factors(n_samples)))
fname = self.outdir+leading_text+'samples_epoch_'+str(epoch_number)+'.png'
img_samples.save(fname)
rfname = self.outdir+leading_text+'samples_rand_epoch_'+str(epoch_number)+'.png'
rand_img_samples.save(rfname)
log.maybeLog(self.logger, 'Took ' + make_time_units_string(time.time() - to_sample) + ' to sample '+str(n_samples*2)+' numbers')
def plot_samples(self, epoch_number="", leading_text="", n_samples=400):
to_sample = time.time()
initial = self.test_X.get_value()[:1]
V = self.sample(initial, n_samples)
img_samples = PIL.Image.fromarray(tile_raster_images(V, (self.root_N_input,self.root_N_input), (ceil(sqrt(n_samples)), ceil(sqrt(n_samples)))))
fname = self.outdir+leading_text+'samples_epoch_'+str(epoch_number)+'.png'
img_samples.save(fname)
log.maybeLog(self.logger, 'Took ' + str(time.time() - to_sample) + ' to sample '+n_samples+' numbers')
def plot_samples(epoch_number, iteration):
to_sample = time.time()
if layers == 1:
# one layer model
V = sample_some_numbers_single_layer()
else:
V, H0 = sample_some_numbers()
img_samples = PIL.Image.fromarray(tile_raster_images(V, (root_N_input,root_N_input), (20,20)))
fname = outdir+'samples_iteration_'+str(iteration)+'_epoch_'+str(epoch_number)+'.png'
img_samples.save(fname)
print 'Took ' + str(time.time() - to_sample) + ' to sample 400 numbers'
def plot_samples(epoch_number, leading_text):
to_sample = time.time()
if layers == 1:
# one layer model
V = sample_some_numbers_single_layer()
else:
V, H0 = sample_some_numbers()
img_samples = PIL.Image.fromarray(tile_raster_images(V, (root_N_input,root_N_input), (20,20)))
fname = outdir+leading_text+'samples_epoch_'+str(epoch_number)+'.png'
img_samples.save(fname)
logger.log('Took ' + str(time.time() - to_sample) + ' to sample 400 numbers')
def plot_samples(epoch_number, iteration):
to_sample = time.time()
if layers == 1:
# one layer model
V = sample_some_numbers_single_layer()
else:
V, H0 = sample_some_numbers()
img_samples = PIL.Image.fromarray(
tile_raster_images(V, (root_N_input, root_N_input), (20, 20)))
fname = outdir + 'samples_iteration_' + str(
iteration) + '_epoch_' + str(epoch_number) + '.png'
img_samples.save(fname)
print 'Took ' + str(time.time() - to_sample) + ' to sample 400 numbers'
def plot_samples(self, epoch_number="", leading_text="", n_samples=400):
to_sample = time.time()
initial = self.test_X.get_value()[:1]
V = self.sample(initial, n_samples)
img_samples = PIL.Image.fromarray(
tile_raster_images(V, (self.root_N_input, self.root_N_input),
(ceil(sqrt(n_samples)), ceil(sqrt(n_samples)))))
fname = self.outdir + leading_text + 'samples_epoch_' + str(
epoch_number) + '.png'
img_samples.save(fname)
log.maybeLog(
self.logger, 'Took ' + str(time.time() - to_sample) +
' to sample ' + n_samples + ' numbers')
def plot_samples(epoch_number, leading_text):
to_sample = time.time()
if layers == 1:
# one layer model
V = sample_some_numbers_single_layer()
else:
V, H0 = sample_some_numbers()
img_samples = PIL.Image.fromarray(
tile_raster_images(V, (root_N_input, root_N_input), (20, 20)))
fname = outdir + leading_text + 'samples_epoch_' + str(
epoch_number) + '.png'
img_samples.save(fname)
logger.log('Took ' + str(time.time() - to_sample) +
' to sample 400 numbers')
def test(self, test_X=None):
log.maybeLog(self.logger, "\nTesting---------\n")
if test_X is None:
log.maybeLog(
self.logger,
"Testing using data given during initialization of GSN.\n")
test_X = self.test_X
if test_X is None:
log.maybeLog(self.logger, "\nPlease provide a test dataset!\n")
raise AssertionError("Please provide a test dataset")
else:
log.maybeLog(self.logger,
"Testing using data provided to test function.\n")
###########
# TESTING #
###########
n_examples = 100
tests = test_X.get_value()[0:n_examples]
noisy_tests = self.f_noise(test_X.get_value()[0:n_examples])
cost, reconstructed = self.f_recon(noisy_tests)
# Concatenate stuff if it is an image
if self.is_image:
stacked = numpy.vstack([
numpy.vstack([
tests[i * 10:(i + 1) * 10],
noisy_tests[i * 10:(i + 1) * 10],
reconstructed[i * 10:(i + 1) * 10]
]) for i in range(10)
])
number_reconstruction = PIL.Image.fromarray(
tile_raster_images(stacked,
(self.image_height, self.image_width),
(10, 30)))
number_reconstruction.save(self.outdir +
'gsn_image_reconstruction_test.png')
# Otherwise, save reconstructed numpy array as csv
else:
numpy.savetxt(self.outdir + 'gsn_reconstruction_test.csv',
reconstructed,
delimiter=",")
log.maybeLog(
self.logger, "----------------\n\nAverage test cost is " +
str(cost) + "\n\n-----------------")
def train_GSN(train_X, train_Y, valid_X, valid_Y, test_X, test_Y):
logger.log("\n-----------TRAINING GSN------------\n")
# TRAINING
n_epoch = state.n_epoch
batch_size = state.gsn_batch_size
STOP = False
counter = 0
learning_rate.set_value(cast32(state.learning_rate)) # learning rate
times = []
best_cost = float('inf')
best_params = None
patience = 0
logger.log(['train X size:',str(train_X.shape.eval())])
logger.log(['valid X size:',str(valid_X.shape.eval())])
logger.log(['test X size:',str(test_X.shape.eval())])
if state.vis_init:
bias_list[0].set_value(logit(numpy.clip(0.9,0.001,train_X.get_value().mean(axis=0))))
if state.test_model:
# If testing, do not train and go directly to generating samples, parzen window estimation, and inpainting
logger.log('Testing : skip training')
STOP = True
while not STOP:
counter += 1
t = time.time()
logger.append([counter,'\t'])
#shuffle the data
data.sequence_mnist_data(train_X, train_Y, valid_X, valid_Y, test_X, test_Y, dataset, rng)
#train
train_costs = []
for i in xrange(len(train_X.get_value(borrow=True)) / batch_size):
x = train_X.get_value()[i * batch_size : (i+1) * batch_size]
cost = f_learn_gsn(x)
train_costs.append([cost])
train_costs = numpy.mean(train_costs)
# record it
logger.append(['Train:',trunc(train_costs),'\t'])
with open(gsn_train_convergence,'a') as f:
f.write("{0!s},".format(train_costs))
f.write("\n")
#valid
valid_costs = []
for i in xrange(len(valid_X.get_value(borrow=True)) / batch_size):
x = valid_X.get_value()[i * batch_size : (i+1) * batch_size]
cost = f_cost_gsn(x)
valid_costs.append([cost])
valid_costs = numpy.mean(valid_costs)
# record it
logger.append(['Valid:',trunc(valid_costs), '\t'])
with open(gsn_valid_convergence,'a') as f:
f.write("{0!s},".format(valid_costs))
f.write("\n")
#test
test_costs = []
for i in xrange(len(test_X.get_value(borrow=True)) / batch_size):
x = test_X.get_value()[i * batch_size : (i+1) * batch_size]
cost = f_cost_gsn(x)
test_costs.append([cost])
test_costs = numpy.mean(test_costs)
# record it
logger.append(['Test:',trunc(test_costs), '\t'])
with open(gsn_test_convergence,'a') as f:
f.write("{0!s},".format(test_costs))
f.write("\n")
#check for early stopping
cost = numpy.sum(valid_costs)
if cost < best_cost*state.early_stop_threshold:
patience = 0
best_cost = cost
# save the parameters that made it the best
best_params = save_params(gsn_params)
else:
patience += 1
if counter >= n_epoch or patience >= state.early_stop_length:
STOP = True
if best_params is not None:
restore_params(gsn_params, best_params)
save_params_to_file('gsn', counter, gsn_params)
timing = time.time() - t
times.append(timing)
logger.append('time: '+make_time_units_string(timing)+'\t')
logger.log('remaining: '+make_time_units_string((n_epoch - counter) * numpy.mean(times)))
if (counter % state.save_frequency) == 0 or STOP is True:
n_examples = 100
random_idx = numpy.array(R.sample(range(len(test_X.get_value(borrow=True))), n_examples))
numbers = test_X.get_value(borrow=True)[random_idx]
noisy_numbers = f_noise(test_X.get_value(borrow=True)[random_idx])
reconstructed = f_recon_gsn(noisy_numbers)
# Concatenate stuff
stacked = numpy.vstack([numpy.vstack([numbers[i*10 : (i+1)*10], noisy_numbers[i*10 : (i+1)*10], reconstructed[i*10 : (i+1)*10]]) for i in range(10)])
number_reconstruction = PIL.Image.fromarray(tile_raster_images(stacked, (root_N_input,root_N_input), (10,30)))
number_reconstruction.save(outdir+'gsn_number_reconstruction_epoch_'+str(counter)+'.png')
#sample_numbers(counter, 'seven')
plot_samples(counter, 'gsn')
#save gsn_params
save_params_to_file('gsn', counter, gsn_params)
# ANNEAL!
new_lr = learning_rate.get_value() * annealing
learning_rate.set_value(new_lr)
# 10k samples
print 'Generating 10,000 samples'
samples, _ = sample_some_numbers(N=10000)
f_samples = outdir+'samples.npy'
numpy.save(f_samples, samples)
print 'saved digits'
def train_regression(iteration, train_X, train_Y, valid_X, valid_Y, test_X, test_Y):
print '-------------------------------------------'
print 'TRAINING RECURRENT REGRESSION FOR ITERATION',iteration
with open(logfile,'a') as f:
f.write("--------------------------\nTRAINING RECURRENT REGRESSION FOR ITERATION {0!s}\n".format(iteration))
# TRAINING
# TRAINING
n_epoch = state.n_epoch
batch_size = state.batch_size
STOP = False
counter = 0
if iteration == 0:
recurrent_learning_rate.set_value(cast32(state.learning_rate)) # learning rate
times = []
best_cost = float('inf')
patience = 0
print 'learning rate:',recurrent_learning_rate.get_value()
print 'train X size:',str(train_X.shape.eval())
print 'valid X size:',str(valid_X.shape.eval())
print 'test X size:',str(test_X.shape.eval())
train_costs = []
valid_costs = []
test_costs = []
if state.vis_init:
bias_list[0].set_value(logit(numpy.clip(0.9,0.001,train_X.get_value().mean(axis=0))))
if state.test_model:
# If testing, do not train and go directly to generating samples, parzen window estimation, and inpainting
print 'Testing : skip training'
STOP = True
while not STOP:
counter += 1
t = time.time()
print counter,'\t',
#shuffle the data
data.sequence_mnist_data(train_X, train_Y, valid_X, valid_Y, test_X, test_Y, dataset, rng)
#train
#init recurrent hiddens as zero
recurrent_hiddens = [T.zeros((batch_size,recurrent_layer_size)).eval() for recurrent_layer_size in recurrent_layer_sizes]
train_cost = []
for i in range(len(train_X.get_value(borrow=True)) / batch_size):
x = train_X.get_value()[i * batch_size : (i+1) * batch_size]
x1 = train_X.get_value()[(i * batch_size) + 1 : ((i+1) * batch_size) + 1]
[x,x1], recurrent_hiddens = fix_input_size([x,x1], recurrent_hiddens)
_ins = recurrent_hiddens + [x,x1]
_outs = recurrent_f_learn(*_ins)
recurrent_hiddens = _outs[:len(recurrent_hiddens)]
cost = _outs[-1]
train_cost.append(cost)
train_cost = numpy.mean(train_cost)
train_costs.append(train_cost)
print 'rTrain : ',trunc(train_cost), '\t',
with open(logfile,'a') as f:
f.write("rTrain : {0!s}\t".format(trunc(train_cost)))
with open(recurrent_train_convergence,'a') as f:
f.write("{0!s},".format(train_cost))
#valid
#init recurrent hiddens as zero
recurrent_hiddens = [T.zeros((batch_size,recurrent_layer_size)).eval() for recurrent_layer_size in recurrent_layer_sizes]
valid_cost = []
for i in range(len(valid_X.get_value(borrow=True)) / batch_size):
x = valid_X.get_value()[i * batch_size : (i+1) * batch_size]
x1 = valid_X.get_value()[(i * batch_size) + 1 : ((i+1) * batch_size) + 1]
[x,x1], recurrent_hiddens = fix_input_size([x,x1], recurrent_hiddens)
_ins = recurrent_hiddens + [x,x1]
_outs = f_cost(*_ins)
recurrent_hiddens = _outs[:len(recurrent_hiddens)]
cost = _outs[-1]
valid_cost.append(cost)
valid_cost = numpy.mean(valid_cost)
valid_costs.append(valid_cost)
print 'rValid : ', trunc(valid_cost), '\t',
with open(logfile,'a') as f:
f.write("rValid : {0!s}\t".format(trunc(valid_cost)))
with open(recurrent_valid_convergence,'a') as f:
f.write("{0!s},".format(valid_cost))
#test
recurrent_hiddens = [T.zeros((batch_size,recurrent_layer_size)).eval() for recurrent_layer_size in recurrent_layer_sizes]
test_cost = []
for i in range(len(test_X.get_value(borrow=True)) / batch_size):
x = test_X.get_value()[i * batch_size : (i+1) * batch_size]
x1 = test_X.get_value()[(i * batch_size) + 1 : ((i+1) * batch_size) + 1]
[x,x1], recurrent_hiddens = fix_input_size([x,x1], recurrent_hiddens)
_ins = recurrent_hiddens + [x,x1]
_outs = f_cost(*_ins)
recurrent_hiddens = _outs[:len(recurrent_hiddens)]
cost = _outs[-1]
test_cost.append(cost)
test_cost = numpy.mean(test_cost)
test_costs.append(test_cost)
print 'rTest : ', trunc(test_cost), '\t',
with open(logfile,'a') as f:
f.write("rTest : {0!s}\t".format(trunc(test_cost)))
with open(recurrent_test_convergence,'a') as f:
f.write("{0!s},".format(test_cost))
#check for early stopping
cost = train_cost
if iteration != 0:
cost = cost + train_cost
if cost < best_cost*state.early_stop_threshold:
patience = 0
best_cost = cost
else:
patience += 1
timing = time.time() - t
times.append(timing)
print 'time : ', trunc(timing),
print 'remaining: ', trunc((n_epoch - counter) * numpy.mean(times) / 60 / 60), 'hrs'
with open(logfile,'a') as f:
f.write("B : {0!s}\t".format(str([trunc(vb.get_value().mean()) for vb in recurrent_bias_list])))
with open(logfile,'a') as f:
f.write("W : {0!s}\t".format(str([trunc(abs(v.get_value(borrow=True)).mean()) for v in recurrent_weights_list_encode])))
with open(logfile,'a') as f:
f.write("V : {0!s}\t".format(str([trunc(abs(v.get_value(borrow=True)).mean()) for v in recurrent_weights_list_decode])))
with open(logfile,'a') as f:
f.write("Time : {0!s} seconds\n".format(trunc(timing)))
if (counter % state.save_frequency) == 0:
# Checking reconstruction
nums = test_X.get_value()[range(100)]
noisy_nums = f_noise(test_X.get_value()[range(100)])
reconstructed = []
reconstructed_prediction = []
#init recurrent hiddens as zero
recurrent_hiddens = [T.zeros((batch_size,recurrent_layer_size)).eval() for recurrent_layer_size in recurrent_layer_sizes]
for num in noisy_nums:
_ins = recurrent_hiddens + [num]
_outs = f_recon(*_ins)
recurrent_hiddens = _outs[:len(recurrent_hiddens)]
[recon,recon_pred] = _outs[len(recurrent_hiddens):]
reconstructed.append(recon)
reconstructed_prediction.append(recon_pred)
# Concatenate stuff
stacked = numpy.vstack([numpy.vstack([nums[i*10 : (i+1)*10], noisy_nums[i*10 : (i+1)*10], reconstructed[i*10 : (i+1)*10], reconstructed_prediction[i*10 : (i+1)*10]]) for i in range(10)])
number_reconstruction = PIL.Image.fromarray(tile_raster_images(stacked, (root_N_input,root_N_input), (10,40)))
#epoch_number = reduce(lambda x,y : x + y, ['_'] * (4-len(str(counter)))) + str(counter)
number_reconstruction.save(outdir+'recurrent_number_reconstruction_iteration_'+str(iteration)+'_epoch_'+str(counter)+'.png')
#sample_numbers(counter, 'seven')
plot_samples(counter, iteration)
#save params
save_params_to_file('recurrent', counter, params, iteration)
# ANNEAL!
new_r_lr = recurrent_learning_rate.get_value() * annealing
recurrent_learning_rate.set_value(new_r_lr)
# if test
# 10k samples
print 'Generating 10,000 samples'
samples, _ = sample_some_numbers(N=10000)
f_samples = outdir+'samples.npy'
numpy.save(f_samples, samples)
print 'saved digits'
def train_regression(iteration, train_X, train_Y, valid_X, valid_Y, test_X, test_Y):
logger.log('-------------TRAINING REGRESSION FOR ITERATION {0!s}-------------'.format(iteration))
# TRAINING
n_epoch = state.n_epoch
batch_size = state.batch_size
STOP = False
counter = 0
best_cost = float('inf')
best_params = None
patience = 0
if iteration == 0:
regression_learning_rate.set_value(cast32(state.learning_rate)) # learning rate
times = []
logger.log(['learning rate:', regression_learning_rate.get_value()])
logger.log(['train X size:', str(train_X.shape.eval())])
logger.log(['valid X size:', str(valid_X.shape.eval())])
logger.log(['test X size:', str(test_X.shape.eval())])
if state.test_model:
# If testing, do not train and go directly to generating samples, parzen window estimation, and inpainting
logger.log('Testing : skip training')
STOP = True
while not STOP:
counter += 1
t = time.time()
logger.append([counter, '\t'])
# shuffle the data
# data.sequence_mnist_data(train_X, train_Y, valid_X, valid_Y, test_X, test_Y, dataset, rng)
# train
train_costs = []
train_errors = []
for i in range(len(train_X.get_value(borrow=True)) / batch_size):
xs = [train_X.get_value(borrow=True)[
(i * batch_size) + sequence_idx: ((i + 1) * batch_size) + sequence_idx] for sequence_idx in
range(len(Xs))]
xs, _ = fix_input_size(xs)
_ins = xs # + [sequence_window_size]
cost, error = regression_f_learn(*_ins)
# print trunc(cost)
# print [numpy.asarray(a) for a in f_check(*_ins)]
train_costs.append(cost)
train_errors.append(error)
train_costs = numpy.mean(train_costs)
train_errors = numpy.mean(train_errors)
logger.append(['rTrain: ', trunc(train_costs), trunc(train_errors), '\t'])
with open(regression_train_convergence, 'a') as f:
f.write("{0!s},".format(train_costs))
f.write("\n")
# valid
valid_costs = []
for i in range(len(valid_X.get_value(borrow=True)) / batch_size):
xs = [valid_X.get_value(borrow=True)[
(i * batch_size) + sequence_idx: ((i + 1) * batch_size) + sequence_idx] for sequence_idx in
range(len(Xs))]
xs, _ = fix_input_size(xs)
_ins = xs # + [sequence_window_size]
cost, _ = regression_f_cost(*_ins)
valid_costs.append(cost)
valid_costs = numpy.mean(valid_costs)
logger.append(['rValid: ', trunc(valid_costs), '\t'])
with open(regression_valid_convergence, 'a') as f:
f.write("{0!s},".format(valid_costs))
f.write("\n")
# test
test_costs = []
test_errors = []
for i in range(len(test_X.get_value(borrow=True)) / batch_size):
xs = [test_X.get_value(borrow=True)[
(i * batch_size) + sequence_idx: ((i + 1) * batch_size) + sequence_idx] for sequence_idx in
range(len(Xs))]
xs, _ = fix_input_size(xs)
_ins = xs # + [sequence_window_size]
cost, error = regression_f_cost(*_ins)
test_costs.append(cost)
test_errors.append(error)
test_costs = numpy.mean(test_costs)
test_errors = numpy.mean(test_errors)
logger.append(['rTest: ', trunc(test_costs), trunc(test_errors), '\t'])
with open(regression_test_convergence, 'a') as f:
f.write("{0!s},".format(test_costs))
f.write("\n")
# check for early stopping
cost = numpy.sum(valid_costs)
if cost < best_cost * state.early_stop_threshold:
patience = 0
best_cost = cost
# keep the best params so far
best_params = save_params(regression_params)
else:
patience += 1
if counter >= n_epoch or patience >= state.early_stop_length:
STOP = True
if best_params is not None:
restore_params(regression_params, best_params)
save_params_to_file('regression', counter, regression_params, iteration)
logger.log(["next learning rate should be", regression_learning_rate.get_value() * annealing])
timing = time.time() - t
times.append(timing)
logger.append('time: ' + make_time_units_string(timing))
logger.log('remaining: ' + make_time_units_string((n_epoch - counter) * numpy.mean(times)))
if (counter % state.save_frequency) == 0 or STOP is True:
n_examples = 100 + sequence_window_size
# Checking reconstruction
# grab 100 numbers in the sequence from the test set
nums = test_X.get_value()[range(n_examples)]
noisy_nums = f_noise(test_X.get_value()[range(n_examples)])
reconstructed_prediction = []
reconstructed = []
for i in range(n_examples):
if i >= sequence_window_size:
xs = [noisy_nums[i - x] for x in range(len(Xs))]
xs.reverse()
_ins = xs # + [sequence_window_size]
_outs = f_recon(*_ins)
prediction = _outs[0]
reconstruction = _outs[1]
reconstructed_prediction.append(prediction)
reconstructed.append(reconstruction)
nums = nums[sequence_window_size:]
noisy_nums = noisy_nums[sequence_window_size:]
reconstructed_prediction = numpy.array(reconstructed_prediction)
reconstructed = numpy.array(reconstructed)
# Concatenate stuff
stacked = numpy.vstack([numpy.vstack([nums[i * 10: (i + 1) * 10], noisy_nums[i * 10: (i + 1) * 10],
reconstructed_prediction[i * 10: (i + 1) * 10],
reconstructed[i * 10: (i + 1) * 10]]) for i in range(10)])
number_reconstruction = PIL.Image.fromarray(
tile_raster_images(stacked, (root_N_input, root_N_input), (10, 40)))
# epoch_number = reduce(lambda x,y : x + y, ['_'] * (4-len(str(counter)))) + str(counter)
number_reconstruction.save(
outdir + 'regression_number_reconstruction_iteration_' + str(iteration) + '_epoch_' + str(
counter) + '.png')
# save gsn_params
save_params_to_file('regression', counter, regression_params, iteration)
# ANNEAL!
new_r_lr = regression_learning_rate.get_value() * annealing
regression_learning_rate.set_value(new_r_lr)
def train(self, train_X=None, valid_X=None, test_X=None, continue_training=False):
log.maybeLog(self.logger, "\nTraining---------\n")
if train_X is None:
log.maybeLog(self.logger, "Training using data given during initialization of GSN.\n")
train_X = self.train_X
if train_X is None:
log.maybeLog(self.logger, "\nPlease provide a training dataset!\n")
raise AssertionError("Please provide a training dataset!")
else:
log.maybeLog(self.logger, "Training using data provided to training function.\n")
if valid_X is None:
valid_X = self.valid_X
if test_X is None:
test_X = self.test_X
train_X = raise_data_to_list(train_X)
valid_X = raise_data_to_list(valid_X)
test_X = raise_data_to_list(test_X)
############
# TRAINING #
############
log.maybeLog(self.logger, "-----------TRAINING GSN FOR {0!s} EPOCHS-----------".format(self.n_epoch))
STOP = False
counter = 0
if not continue_training:
self.learning_rate.set_value(self.init_learn_rate) # learning rate
times = []
best_cost = float('inf')
best_params = None
patience = 0
log.maybeLog(self.logger, ['train X size:',str(train_X[0].shape.eval())])
if valid_X is not None:
log.maybeLog(self.logger, ['valid X size:',str(valid_X[0].shape.eval())])
if test_X is not None:
log.maybeLog(self.logger, ['test X size:',str(test_X[0].shape.eval())])
if self.vis_init:
self.bias_list[0].set_value(logit(numpy.clip(0.9,0.001,train_X[0].get_value().mean(axis=0))))
while not STOP:
counter += 1
t = time.time()
log.maybeAppend(self.logger, [counter,'\t'])
#train
train_costs = data.apply_cost_function_to_dataset(self.f_learn, train_X, self.batch_size)
log.maybeAppend(self.logger, ['Train:',trunc(numpy.mean(train_costs)), '\t'])
#valid
if valid_X is not None:
valid_costs = data.apply_cost_function_to_dataset(self.f_cost, valid_X, self.batch_size)
log.maybeAppend(self.logger, ['Valid:',trunc(numpy.mean(valid_costs)), '\t'])
#test
if test_X is not None:
test_costs = data.apply_cost_function_to_dataset(self.f_cost, test_X, self.batch_size)
log.maybeAppend(self.logger, ['Test:',trunc(numpy.mean(test_costs)), '\t'])
#check for early stopping
if valid_X is not None:
cost = numpy.sum(valid_costs)
else:
cost = numpy.sum(train_costs)
if cost < best_cost*self.early_stop_threshold:
patience = 0
best_cost = cost
# save the parameters that made it the best
best_params = save_params(self.params)
else:
patience += 1
if counter >= self.n_epoch or patience >= self.early_stop_length:
STOP = True
if best_params is not None:
restore_params(self.params, best_params)
save_params_to_file(counter, self.params, self.outdir, self.logger)
timing = time.time() - t
times.append(timing)
log.maybeAppend(self.logger, 'time: '+make_time_units_string(timing)+'\t')
log.maybeLog(self.logger, 'remaining: '+make_time_units_string((self.n_epoch - counter) * numpy.mean(times)))
if (counter % self.save_frequency) == 0 or STOP is True:
if self.is_image:
n_examples = 100
tests = test_X.get_value()[0:n_examples]
noisy_tests = self.f_noise(test_X.get_value()[0:n_examples])
_, reconstructed = self.f_recon(noisy_tests)
# Concatenate stuff if it is an image
stacked = numpy.vstack([numpy.vstack([tests[i*10 : (i+1)*10], noisy_tests[i*10 : (i+1)*10], reconstructed[i*10 : (i+1)*10]]) for i in range(10)])
number_reconstruction = PIL.Image.fromarray(tile_raster_images(stacked, (self.image_height,self.image_width), (10,30)))
number_reconstruction.save(self.outdir+'gsn_image_reconstruction_epoch_'+str(counter)+'.png')
#save gsn_params
save_params_to_file(counter, self.params, self.outdir, self.logger)
# ANNEAL!
new_lr = self.learning_rate.get_value() * self.annealing
self.learning_rate.set_value(new_lr)
new_hidden_sigma = self.hidden_add_noise_sigma.get_value() * self.noise_annealing
self.hidden_add_noise_sigma.set_value(new_hidden_sigma)
new_salt_pepper = self.input_salt_and_pepper.get_value() * self.noise_annealing
self.input_salt_and_pepper.set_value(new_salt_pepper)
def train_GSN(train_X, train_Y, valid_X, valid_Y, test_X, test_Y):
logger.log("\n-----------TRAINING GSN------------\n")
# TRAINING
n_epoch = state.n_epoch
batch_size = state.gsn_batch_size
STOP = False
counter = 0
learning_rate.set_value(cast32(state.learning_rate)) # learning rate
times = []
best_cost = float('inf')
best_params = None
patience = 0
logger.log(['train X size:', str(train_X.shape.eval())])
logger.log(['valid X size:', str(valid_X.shape.eval())])
logger.log(['test X size:', str(test_X.shape.eval())])
if state.vis_init:
bias_list[0].set_value(
logit(numpy.clip(0.9, 0.001,
train_X.get_value().mean(axis=0))))
if state.test_model:
# If testing, do not train and go directly to generating samples, parzen window estimation, and inpainting
logger.log('Testing : skip training')
STOP = True
while not STOP:
counter += 1
t = time.time()
logger.append([counter, '\t'])
#shuffle the data
data.sequence_mnist_data(train_X, train_Y, valid_X, valid_Y,
test_X, test_Y, dataset, rng)
#train
train_costs = []
for i in xrange(len(train_X.get_value(borrow=True)) / batch_size):
x = train_X.get_value()[i * batch_size:(i + 1) * batch_size]
cost = f_learn_gsn(x)
train_costs.append([cost])
train_costs = numpy.mean(train_costs)
# record it
logger.append(['Train:', trunc(train_costs), '\t'])
with open(gsn_train_convergence, 'a') as f:
f.write("{0!s},".format(train_costs))
f.write("\n")
#valid
valid_costs = []
for i in xrange(len(valid_X.get_value(borrow=True)) / batch_size):
x = valid_X.get_value()[i * batch_size:(i + 1) * batch_size]
cost = f_cost_gsn(x)
valid_costs.append([cost])
valid_costs = numpy.mean(valid_costs)
# record it
logger.append(['Valid:', trunc(valid_costs), '\t'])
with open(gsn_valid_convergence, 'a') as f:
f.write("{0!s},".format(valid_costs))
f.write("\n")
#test
test_costs = []
for i in xrange(len(test_X.get_value(borrow=True)) / batch_size):
x = test_X.get_value()[i * batch_size:(i + 1) * batch_size]
cost = f_cost_gsn(x)
test_costs.append([cost])
test_costs = numpy.mean(test_costs)
# record it
logger.append(['Test:', trunc(test_costs), '\t'])
with open(gsn_test_convergence, 'a') as f:
f.write("{0!s},".format(test_costs))
f.write("\n")
#check for early stopping
cost = numpy.sum(valid_costs)
if cost < best_cost * state.early_stop_threshold:
patience = 0
best_cost = cost
# save the parameters that made it the best
best_params = save_params(gsn_params)
else:
patience += 1
if counter >= n_epoch or patience >= state.early_stop_length:
STOP = True
if best_params is not None:
restore_params(gsn_params, best_params)
save_params_to_file('gsn', counter, gsn_params)
timing = time.time() - t
times.append(timing)
logger.append('time: ' + make_time_units_string(timing) + '\t')
logger.log('remaining: ' +
make_time_units_string((n_epoch - counter) *
numpy.mean(times)))
if (counter % state.save_frequency) == 0 or STOP is True:
n_examples = 100
random_idx = numpy.array(
R.sample(range(len(test_X.get_value(borrow=True))),
n_examples))
numbers = test_X.get_value(borrow=True)[random_idx]
noisy_numbers = f_noise(
test_X.get_value(borrow=True)[random_idx])
reconstructed = f_recon_gsn(noisy_numbers)
# Concatenate stuff
stacked = numpy.vstack([
numpy.vstack([
numbers[i * 10:(i + 1) * 10],
noisy_numbers[i * 10:(i + 1) * 10],
reconstructed[i * 10:(i + 1) * 10]
]) for i in range(10)
])
number_reconstruction = PIL.Image.fromarray(
tile_raster_images(stacked, (root_N_input, root_N_input),
(10, 30)))
number_reconstruction.save(outdir +
'gsn_number_reconstruction_epoch_' +
str(counter) + '.png')
#sample_numbers(counter, 'seven')
plot_samples(counter, 'gsn')
#save gsn_params
save_params_to_file('gsn', counter, gsn_params)
# ANNEAL!
new_lr = learning_rate.get_value() * annealing
learning_rate.set_value(new_lr)
# 10k samples
print 'Generating 10,000 samples'
samples, _ = sample_some_numbers(N=10000)
f_samples = outdir + 'samples.npy'
numpy.save(f_samples, samples)
print 'saved digits'
def train_RNN_GSN(train_X, train_Y, valid_X, valid_Y, test_X, test_Y):
# If we are using Hessian-free training
if state.hessian_free == 1:
pass
# gradient_dataset = hf_sequence_dataset([train_X.get_value()], batch_size=None, number_batches=5000)
# cg_dataset = hf_sequence_dataset([train_X.get_value()], batch_size=None, number_batches=1000)
# valid_dataset = hf_sequence_dataset([valid_X.get_value()], batch_size=None, number_batches=1000)
#
# s = x_samples
# costs = [cost, show_cost]
# hf_optimizer(params, [Xs], s, costs, u, ua).train(gradient_dataset, cg_dataset, initial_lambda=1.0, preconditioner=True, validation=valid_dataset)
# If we are using SGD training
else:
# Define the re-used loops for f_learn and f_cost
def apply_cost_function_to_dataset(function, dataset):
costs = []
for i in xrange(
len(dataset.get_value(borrow=True)) / batch_size):
xs = dataset.get_value(
borrow=True)[i * batch_size:(i + 1) * batch_size]
cost = function(xs)
costs.append([cost])
return numpy.mean(costs)
logger.log("\n-----------TRAINING RNN-GSN------------\n")
# TRAINING
n_epoch = state.n_epoch
batch_size = state.batch_size
STOP = False
counter = 0
learning_rate.set_value(cast32(
state.learning_rate)) # learning rate
times = []
best_cost = float('inf')
best_params = None
patience = 0
logger.log(['train X size:', str(train_X.shape.eval())])
logger.log(['valid X size:', str(valid_X.shape.eval())])
logger.log(['test X size:', str(test_X.shape.eval())])
if state.vis_init:
bias_list[0].set_value(
logit(
numpy.clip(0.9, 0.001,
train_X.get_value().mean(axis=0))))
if state.test_model:
# If testing, do not train and go directly to generating samples, parzen window estimation, and inpainting
logger.log('Testing : skip training')
STOP = True
while not STOP:
counter += 1
t = time.time()
logger.append([counter, '\t'])
#shuffle the data
data.sequence_mnist_data(train_X, train_Y, valid_X, valid_Y,
test_X, test_Y, dataset, rng)
#train
train_costs = apply_cost_function_to_dataset(f_learn, train_X)
# record it
logger.append(['Train:', trunc(train_costs), '\t'])
with open(train_convergence, 'a') as f:
f.write("{0!s},".format(train_costs))
f.write("\n")
#valid
valid_costs = apply_cost_function_to_dataset(f_cost, valid_X)
# record it
logger.append(['Valid:', trunc(valid_costs), '\t'])
with open(valid_convergence, 'a') as f:
f.write("{0!s},".format(valid_costs))
f.write("\n")
#test
test_costs = apply_cost_function_to_dataset(f_cost, test_X)
# record it
logger.append(['Test:', trunc(test_costs), '\t'])
with open(test_convergence, 'a') as f:
f.write("{0!s},".format(test_costs))
f.write("\n")
#check for early stopping
cost = numpy.sum(valid_costs)
if cost < best_cost * state.early_stop_threshold:
patience = 0
best_cost = cost
# save the parameters that made it the best
best_params = save_params(params)
else:
patience += 1
if counter >= n_epoch or patience >= state.early_stop_length:
STOP = True
if best_params is not None:
restore_params(params, best_params)
save_params_to_file('all', counter, params)
timing = time.time() - t
times.append(timing)
logger.append('time: ' + make_time_units_string(timing) + '\t')
logger.log('remaining: ' +
make_time_units_string((n_epoch - counter) *
numpy.mean(times)))
if (counter % state.save_frequency) == 0 or STOP is True:
n_examples = 100
nums = test_X.get_value(borrow=True)[range(n_examples)]
noisy_nums = f_noise(
test_X.get_value(borrow=True)[range(n_examples)])
reconstructions = []
for i in xrange(0, len(noisy_nums)):
recon = f_recon(noisy_nums[max(0, (i + 1) -
batch_size):i + 1])
reconstructions.append(recon)
reconstructed = numpy.array(reconstructions)
# Concatenate stuff
stacked = numpy.vstack([
numpy.vstack([
nums[i * 10:(i + 1) * 10],
noisy_nums[i * 10:(i + 1) * 10],
reconstructed[i * 10:(i + 1) * 10]
]) for i in range(10)
])
number_reconstruction = PIL.Image.fromarray(
tile_raster_images(stacked,
(root_N_input, root_N_input),
(10, 30)))
number_reconstruction.save(
outdir + 'rnngsn_number_reconstruction_epoch_' +
str(counter) + '.png')
#sample_numbers(counter, 'seven')
plot_samples(counter, 'rnngsn')
#save params
save_params_to_file('all', counter, params)
# ANNEAL!
new_lr = learning_rate.get_value() * annealing
learning_rate.set_value(new_lr)
# 10k samples
print 'Generating 10,000 samples'
samples, _ = sample_some_numbers(N=10000)
f_samples = outdir + 'samples.npy'
numpy.save(f_samples, samples)
print 'saved digits'
def train(self,
train_X=None,
valid_X=None,
test_X=None,
continue_training=False):
log.maybeLog(self.logger, "\nTraining---------\n")
if train_X is None:
log.maybeLog(
self.logger,
"Training using data given during initialization of GSN.\n")
train_X = self.train_X
if train_X is None:
log.maybeLog(self.logger,
"\nPlease provide a training dataset!\n")
raise AssertionError("Please provide a training dataset!")
else:
log.maybeLog(
self.logger,
"Training using data provided to training function.\n")
if valid_X is None:
valid_X = self.valid_X
if test_X is None:
test_X = self.test_X
train_X = raise_data_to_list(train_X)
valid_X = raise_data_to_list(valid_X)
test_X = raise_data_to_list(test_X)
############
# TRAINING #
############
log.maybeLog(
self.logger,
"-----------TRAINING GSN FOR {0!s} EPOCHS-----------".format(
self.n_epoch))
STOP = False
counter = 0
if not continue_training:
self.learning_rate.set_value(self.init_learn_rate) # learning rate
times = []
best_cost = float('inf')
best_params = None
patience = 0
log.maybeLog(
self.logger,
['train X size:', str(train_X[0].shape.eval())])
if valid_X is not None:
log.maybeLog(self.logger,
['valid X size:',
str(valid_X[0].shape.eval())])
if test_X is not None:
log.maybeLog(
self.logger,
['test X size:', str(test_X[0].shape.eval())])
if self.vis_init:
self.bias_list[0].set_value(
logit(
numpy.clip(0.9, 0.001,
train_X[0].get_value().mean(axis=0))))
while not STOP:
counter += 1
t = time.time()
log.maybeAppend(self.logger, [counter, '\t'])
#train
train_costs = data.apply_cost_function_to_dataset(
self.f_learn, train_X, self.batch_size)
log.maybeAppend(
self.logger,
['Train:', trunc(numpy.mean(train_costs)), '\t'])
#valid
if valid_X is not None:
valid_costs = data.apply_cost_function_to_dataset(
self.f_cost, valid_X, self.batch_size)
log.maybeAppend(
self.logger,
['Valid:', trunc(numpy.mean(valid_costs)), '\t'])
#test
if test_X is not None:
test_costs = data.apply_cost_function_to_dataset(
self.f_cost, test_X, self.batch_size)
log.maybeAppend(
self.logger,
['Test:', trunc(numpy.mean(test_costs)), '\t'])
#check for early stopping
if valid_X is not None:
cost = numpy.sum(valid_costs)
else:
cost = numpy.sum(train_costs)
if cost < best_cost * self.early_stop_threshold:
patience = 0
best_cost = cost
# save the parameters that made it the best
best_params = save_params(self.params)
else:
patience += 1
if counter >= self.n_epoch or patience >= self.early_stop_length:
STOP = True
if best_params is not None:
restore_params(self.params, best_params)
save_params_to_file(counter, self.params, self.outdir,
self.logger)
timing = time.time() - t
times.append(timing)
log.maybeAppend(self.logger,
'time: ' + make_time_units_string(timing) + '\t')
log.maybeLog(
self.logger, 'remaining: ' + make_time_units_string(
(self.n_epoch - counter) * numpy.mean(times)))
if (counter % self.save_frequency) == 0 or STOP is True:
if self.is_image:
n_examples = 100
tests = test_X.get_value()[0:n_examples]
noisy_tests = self.f_noise(
test_X.get_value()[0:n_examples])
_, reconstructed = self.f_recon(noisy_tests)
# Concatenate stuff if it is an image
stacked = numpy.vstack([
numpy.vstack([
tests[i * 10:(i + 1) * 10],
noisy_tests[i * 10:(i + 1) * 10],
reconstructed[i * 10:(i + 1) * 10]
]) for i in range(10)
])
number_reconstruction = PIL.Image.fromarray(
tile_raster_images(
stacked, (self.image_height, self.image_width),
(10, 30)))
number_reconstruction.save(
self.outdir + 'gsn_image_reconstruction_epoch_' +
str(counter) + '.png')
#save gsn_params
save_params_to_file(counter, self.params, self.outdir,
self.logger)
# ANNEAL!
new_lr = self.learning_rate.get_value() * self.annealing
self.learning_rate.set_value(new_lr)
new_hidden_sigma = self.hidden_add_noise_sigma.get_value(
) * self.noise_annealing
self.hidden_add_noise_sigma.set_value(new_hidden_sigma)
new_salt_pepper = self.input_salt_and_pepper.get_value(
) * self.noise_annealing
self.input_salt_and_pepper.set_value(new_salt_pepper)
def train(self, train_X=None, train_Y=None, valid_X=None, valid_Y=None, test_X=None, test_Y=None, is_artificial=False, artificial_sequence=1, continue_training=False):
log.maybeLog(self.logger, "\nTraining---------\n")
if train_X is None:
log.maybeLog(self.logger, "Training using data given during initialization of RNN-GSN.\n")
train_X = self.train_X
train_Y = self.train_Y
if train_X is None:
log.maybeLog(self.logger, "\nPlease provide a training dataset!\n")
raise AssertionError("Please provide a training dataset!")
else:
log.maybeLog(self.logger, "Training using data provided to training function.\n")
if valid_X is None:
valid_X = self.valid_X
valid_Y = self.valid_Y
if test_X is None:
test_X = self.test_X
test_Y = self.test_Y
# Input data - make sure it is a list of shared datasets
train_X = raise_to_list(train_X)
train_Y = raise_to_list(train_Y)
valid_X = raise_to_list(valid_X)
valid_Y = raise_to_list(valid_Y)
test_X = raise_to_list(test_X)
test_Y = raise_to_list(test_Y)
##########################################################
# Train the GSN first to get good weights initialization #
##########################################################
if self.train_gsn_first:
log.maybeLog(self.logger, "\n\n----------Initially training the GSN---------\n\n")
# init_gsn = GSN(train_X=train_X, valid_X=valid_X, test_X=test_X, state=self.gsn_args, logger=self.logger)
# init_gsn.train()
print "NOT IMPLEMENTED"
#########################################
# If we are using Hessian-free training #
#########################################
if self.hessian_free:
pass
# gradient_dataset = hf_sequence_dataset([train_X.get_value()], batch_size=None, number_batches=5000)
# cg_dataset = hf_sequence_dataset([train_X.get_value()], batch_size=None, number_batches=1000)
# valid_dataset = hf_sequence_dataset([valid_X.get_value()], batch_size=None, number_batches=1000)
#
# s = x_samples
# costs = [cost, show_cost]
# hf_optimizer(params, [Xs], s, costs, u, ua).train(gradient_dataset, cg_dataset, initial_lambda=1.0, preconditioner=True, validation=valid_dataset)
################################
# If we are using SGD training #
################################
else:
log.maybeLog(self.logger, "\n-----------TRAINING RNN-GSN------------\n")
# TRAINING
STOP = False
counter = 0
if not continue_training:
self.learning_rate.set_value(self.init_learn_rate) # learning rate
times = []
best_cost = float('inf')
best_params = None
patience = 0
log.maybeLog(self.logger, ['train X size:',str(train_X[0].get_value(borrow=True).shape)])
if valid_X is not None:
log.maybeLog(self.logger, ['valid X size:',str(valid_X[0].get_value(borrow=True).shape)])
if test_X is not None:
log.maybeLog(self.logger, ['test X size:',str(test_X[0].get_value(borrow=True).shape)])
if self.vis_init:
self.bias_list[0].set_value(logit(numpy.clip(0.9,0.001,train_X[0].get_value(borrow=True).mean(axis=0))))
start_time = time.time()
while not STOP:
counter += 1
t = time.time()
log.maybeAppend(self.logger, [counter,'\t'])
# if is_artificial:
# data.sequence_mnist_data(train_X[0], train_Y[0], valid_X[0], valid_Y[0], test_X[0], test_Y[0], artificial_sequence, rng)
#train
train_costs = []
train_errors = []
for train_data in train_X:
costs_and_errors = data.apply_cost_function_to_dataset(self.f_learn, train_data, self.batch_size)
train_costs.extend([cost for (cost, error) in costs_and_errors])
train_errors.extend([error for (cost, error) in costs_and_errors])
log.maybeAppend(self.logger, ['Train:',trunc(numpy.mean(train_costs)),trunc(numpy.mean(train_errors)),'\t'])
#valid
if valid_X is not None:
valid_costs = []
for valid_data in valid_X:
cs = data.apply_cost_function_to_dataset(self.f_cost, valid_data, self.batch_size)
valid_costs.extend([c for c,e in cs])
log.maybeAppend(self.logger, ['Valid:',trunc(numpy.mean(valid_costs)), '\t'])
#test
if test_X is not None:
test_costs = []
test_errors = []
for test_data in test_X:
costs_and_errors = data.apply_cost_function_to_dataset(self.f_cost, test_data, self.batch_size)
test_costs.extend([cost for (cost, error) in costs_and_errors])
test_errors.extend([error for (cost, error) in costs_and_errors])
log.maybeAppend(self.logger, ['Test:',trunc(numpy.mean(test_costs)),trunc(numpy.mean(test_errors)), '\t'])
#check for early stopping
if valid_X is not None:
cost = numpy.sum(valid_costs)
else:
cost = numpy.sum(train_costs)
if cost < best_cost*self.early_stop_threshold:
patience = 0
best_cost = cost
# save the parameters that made it the best
best_params = copy_params(self.params)
else:
patience += 1
if counter >= self.n_epoch or patience >= self.early_stop_length:
STOP = True
if best_params is not None:
restore_params(self.params, best_params)
self.save_params('all', counter, self.params)
timing = time.time() - t
times.append(timing)
log.maybeAppend(self.logger, 'time: '+make_time_units_string(timing)+'\t')
log.maybeLog(self.logger, 'remaining: '+make_time_units_string((self.n_epoch - counter) * numpy.mean(times)))
if (counter % self.save_frequency) == 0 or STOP is True:
n_examples = 100
xs_test = test_X[0].get_value(borrow=True)[range(n_examples)]
noisy_xs_test = self.f_noise(test_X[0].get_value(borrow=True)[range(n_examples)])
reconstructions = []
for i in xrange(0, len(noisy_xs_test)):
recon, recon_cost = self.f_recon(noisy_xs_test[max(0,(i+1)-self.batch_size):i+1])
reconstructions.append(recon[-1])
reconstructed = numpy.array(reconstructions)
if (self.is_image):
# Concatenate stuff
stacked = numpy.vstack([numpy.vstack([xs_test[i*10 : (i+1)*10], noisy_xs_test[i*10 : (i+1)*10], reconstructed[i*10 : (i+1)*10]]) for i in range(10)])
number_reconstruction = PIL.Image.fromarray(tile_raster_images(stacked, (self.image_height, self.image_width), (10,30)))
number_reconstruction.save(self.outdir+'rnngsn_reconstruction_epoch_'+str(counter)+'.png')
#sample_numbers(counter, 'seven')
# plot_samples(counter, 'rnngsn')
#save params
self.save_params('all', counter, self.params)
# ANNEAL!
new_lr = self.learning_rate.get_value() * self.annealing
self.learning_rate.set_value(new_lr)
new_noise = self.input_salt_and_pepper.get_value() * self.noise_annealing
self.input_salt_and_pepper.set_value(new_noise)
log.maybeLog(self.logger, "\n------------TOTAL RNN-GSN TRAIN TIME TOOK {0!s}---------".format(make_time_units_string(time.time()-start_time)))
def train_GSN(iteration, train_X, train_Y, valid_X, valid_Y, test_X, test_Y):
logger.log('----------------TRAINING GSN FOR ITERATION ' + str(iteration) + "--------------\n")
# TRAINING
n_epoch = state.n_epoch
batch_size = state.batch_size
STOP = False
counter = 0
if iteration == 0:
learning_rate.set_value(cast32(state.learning_rate)) # learning rate
times = []
best_cost = float('inf')
best_params = None
patience = 0
logger.log(['learning rate:', learning_rate.get_value()])
logger.log(['train X size:', str(train_X.shape.eval())])
logger.log(['valid X size:', str(valid_X.shape.eval())])
logger.log(['test X size:', str(test_X.shape.eval())])
if state.vis_init:
bias_list[0].set_value(logit(numpy.clip(0.9, 0.001, train_X.get_value().mean(axis=0))))
if state.test_model:
# If testing, do not train and go directly to generating samples, parzen window estimation, and inpainting
logger.log('Testing : skip training')
STOP = True
while not STOP:
counter += 1
t = time.time()
logger.append([counter, '\t'])
# shuffle the data
# data.sequence_mnist_data(train_X, train_Y, valid_X, valid_Y, test_X, test_Y, dataset, rng)
# train
train_costs = []
train_errors = []
if iteration == 0:
for i in range(len(train_X.get_value(borrow=True)) / batch_size):
x = train_X.get_value(borrow=True)[i * batch_size: (i + 1) * batch_size]
cost, error = gsn_f_learn_init(x)
train_costs.append([cost])
train_errors.append([error])
else:
for i in range(len(train_X.get_value(borrow=True)) / batch_size):
xs = [train_X.get_value(borrow=True)[
(i * batch_size) + sequence_idx: ((i + 1) * batch_size) + sequence_idx] for sequence_idx in
range(len(Xs))]
xs, _ = fix_input_size(xs)
_ins = xs # + [sequence_window_size]
cost, error = gsn_f_learn(*_ins)
train_costs.append(cost)
train_errors.append(error)
train_costs = numpy.mean(train_costs)
train_errors = numpy.mean(train_errors)
logger.append(['Train: ', trunc(train_costs), trunc(train_errors), '\t'])
with open(train_convergence, 'a') as f:
f.write("{0!s},".format(train_costs))
f.write("\n")
# valid
valid_costs = []
if iteration == 0:
for i in range(len(valid_X.get_value(borrow=True)) / batch_size):
x = valid_X.get_value(borrow=True)[i * batch_size: (i + 1) * batch_size]
cost, _ = gsn_f_cost_init(x)
valid_costs.append([cost])
else:
for i in range(len(valid_X.get_value(borrow=True)) / batch_size):
xs = [valid_X.get_value(borrow=True)[
(i * batch_size) + sequence_idx: ((i + 1) * batch_size) + sequence_idx] for sequence_idx in
range(len(Xs))]
xs, _ = fix_input_size(xs)
_ins = xs # + [sequence_window_size]
costs, _ = gsn_f_cost(*_ins)
valid_costs.append(costs)
valid_costs = numpy.mean(valid_costs)
logger.append(['Valid: ', trunc(valid_costs), '\t'])
with open(valid_convergence, 'a') as f:
f.write("{0!s},".format(valid_costs))
f.write("\n")
# test
test_costs = []
test_errors = []
if iteration == 0:
for i in range(len(test_X.get_value(borrow=True)) / batch_size):
x = test_X.get_value(borrow=True)[i * batch_size: (i + 1) * batch_size]
cost, error = gsn_f_cost_init(x)
test_costs.append([cost])
test_errors.append([error])
else:
for i in range(len(test_X.get_value(borrow=True)) / batch_size):
xs = [test_X.get_value(borrow=True)[
(i * batch_size) + sequence_idx: ((i + 1) * batch_size) + sequence_idx] for sequence_idx in
range(len(Xs))]
xs, _ = fix_input_size(xs)
_ins = xs # + [sequence_window_size]
costs, errors = gsn_f_cost(*_ins)
test_costs.append(costs)
test_errors.append(errors)
test_costs = numpy.mean(test_costs)
test_errors = numpy.mean(test_errors)
logger.append(['Test: ', trunc(test_costs), trunc(test_errors), '\t'])
with open(test_convergence, 'a') as f:
f.write("{0!s},".format(test_costs))
f.write("\n")
# check for early stopping
cost = numpy.sum(valid_costs)
if cost < best_cost * state.early_stop_threshold:
patience = 0
best_cost = cost
# save the parameters that made it the best
best_params = save_params(gsn_params)
else:
patience += 1
if counter >= n_epoch or patience >= state.early_stop_length:
STOP = True
if best_params is not None:
restore_params(gsn_params, best_params)
save_params_to_file('gsn', counter, gsn_params, iteration)
logger.log(["next learning rate should be", learning_rate.get_value() * annealing])
timing = time.time() - t
times.append(timing)
logger.append('time: ' + make_time_units_string(timing))
logger.log('remaining: ' + make_time_units_string((n_epoch - counter) * numpy.mean(times)))
if (counter % state.save_frequency) == 0 or STOP is True:
n_examples = 100
if iteration == 0:
random_idx = numpy.array(R.sample(range(len(test_X.get_value())), n_examples))
numbers = test_X.get_value()[random_idx]
noisy_numbers = f_noise(test_X.get_value()[random_idx])
reconstructed = f_recon_init(noisy_numbers)
# Concatenate stuff
stacked = numpy.vstack([numpy.vstack(
[numbers[i * 10: (i + 1) * 10], noisy_numbers[i * 10: (i + 1) * 10],
reconstructed[i * 10: (i + 1) * 10]]) for i in range(10)])
number_reconstruction = PIL.Image.fromarray(
tile_raster_images(stacked, (root_N_input, root_N_input), (10, 30)))
else:
n_examples = n_examples + sequence_window_size
# Checking reconstruction
# grab 100 numbers in the sequence from the test set
nums = test_X.get_value()[range(n_examples)]
noisy_nums = f_noise(test_X.get_value()[range(n_examples)])
reconstructed_prediction = []
reconstructed = []
for i in range(n_examples):
if i >= sequence_window_size:
xs = [noisy_nums[i - x] for x in range(len(Xs))]
xs.reverse()
_ins = xs # + [sequence_window_size]
_outs = f_recon(*_ins)
prediction = _outs[0]
reconstruction = _outs[1]
reconstructed_prediction.append(prediction)
reconstructed.append(reconstruction)
nums = nums[sequence_window_size:]
noisy_nums = noisy_nums[sequence_window_size:]
reconstructed_prediction = numpy.array(reconstructed_prediction)
reconstructed = numpy.array(reconstructed)
# Concatenate stuff
stacked = numpy.vstack([numpy.vstack([nums[i * 10: (i + 1) * 10], noisy_nums[i * 10: (i + 1) * 10],
reconstructed_prediction[i * 10: (i + 1) * 10],
reconstructed[i * 10: (i + 1) * 10]]) for i in range(10)])
number_reconstruction = PIL.Image.fromarray(
tile_raster_images(stacked, (root_N_input, root_N_input), (10, 40)))
# epoch_number = reduce(lambda x,y : x + y, ['_'] * (4-len(str(counter)))) + str(counter)
number_reconstruction.save(
outdir + 'gsn_number_reconstruction_iteration_' + str(iteration) + '_epoch_' + str(
counter) + '.png')
# sample_numbers(counter, 'seven')
plot_samples(counter, iteration)
# save gsn_params
save_params_to_file('gsn', counter, gsn_params, iteration)
# ANNEAL!
new_lr = learning_rate.get_value() * annealing
learning_rate.set_value(new_lr)
# 10k samples
logger.log('Generating 10,000 samples')
samples, _ = sample_some_numbers(N=10000)
f_samples = outdir + 'samples.npy'
numpy.save(f_samples, samples)
logger.log('saved digits')
def train_RNN_GSN(train_X, train_Y, valid_X, valid_Y, test_X, test_Y):
# If we are using Hessian-free training
if state.hessian_free == 1:
pass
# gradient_dataset = hf_sequence_dataset([train_X.get_value()], batch_size=None, number_batches=5000)
# cg_dataset = hf_sequence_dataset([train_X.get_value()], batch_size=None, number_batches=1000)
# valid_dataset = hf_sequence_dataset([valid_X.get_value()], batch_size=None, number_batches=1000)
#
# s = x_samples
# costs = [cost, show_cost]
# hf_optimizer(params, [Xs], s, costs, u, ua).train(gradient_dataset, cg_dataset, initial_lambda=1.0, preconditioner=True, validation=valid_dataset)
# If we are using SGD training
else:
# Define the re-used loops for f_learn and f_cost
def apply_cost_function_to_dataset(function, dataset):
costs = []
for i in xrange(len(dataset.get_value(borrow=True)) / batch_size):
xs = dataset.get_value(borrow=True)[i * batch_size : (i+1) * batch_size]
cost = function(xs)
costs.append([cost])
return numpy.mean(costs)
logger.log("\n-----------TRAINING RNN-GSN------------\n")
# TRAINING
n_epoch = state.n_epoch
batch_size = state.batch_size
STOP = False
counter = 0
learning_rate.set_value(cast32(state.learning_rate)) # learning rate
times = []
best_cost = float('inf')
best_params = None
patience = 0
logger.log(['train X size:',str(train_X.shape.eval())])
logger.log(['valid X size:',str(valid_X.shape.eval())])
logger.log(['test X size:',str(test_X.shape.eval())])
if state.vis_init:
bias_list[0].set_value(logit(numpy.clip(0.9,0.001,train_X.get_value().mean(axis=0))))
if state.test_model:
# If testing, do not train and go directly to generating samples, parzen window estimation, and inpainting
logger.log('Testing : skip training')
STOP = True
while not STOP:
counter += 1
t = time.time()
logger.append([counter,'\t'])
#shuffle the data
data.sequence_mnist_data(train_X, train_Y, valid_X, valid_Y, test_X, test_Y, dataset, rng)
#train
train_costs = apply_cost_function_to_dataset(f_learn, train_X)
# record it
logger.append(['Train:',trunc(train_costs),'\t'])
with open(train_convergence,'a') as f:
f.write("{0!s},".format(train_costs))
f.write("\n")
#valid
valid_costs = apply_cost_function_to_dataset(f_cost, valid_X)
# record it
logger.append(['Valid:',trunc(valid_costs), '\t'])
with open(valid_convergence,'a') as f:
f.write("{0!s},".format(valid_costs))
f.write("\n")
#test
test_costs = apply_cost_function_to_dataset(f_cost, test_X)
# record it
logger.append(['Test:',trunc(test_costs), '\t'])
with open(test_convergence,'a') as f:
f.write("{0!s},".format(test_costs))
f.write("\n")
#check for early stopping
cost = numpy.sum(valid_costs)
if cost < best_cost*state.early_stop_threshold:
patience = 0
best_cost = cost
# save the parameters that made it the best
best_params = save_params(params)
else:
patience += 1
if counter >= n_epoch or patience >= state.early_stop_length:
STOP = True
if best_params is not None:
restore_params(params, best_params)
save_params_to_file('all', counter, params)
timing = time.time() - t
times.append(timing)
logger.append('time: '+make_time_units_string(timing)+'\t')
logger.log('remaining: '+make_time_units_string((n_epoch - counter) * numpy.mean(times)))
if (counter % state.save_frequency) == 0 or STOP is True:
n_examples = 100
nums = test_X.get_value(borrow=True)[range(n_examples)]
noisy_nums = f_noise(test_X.get_value(borrow=True)[range(n_examples)])
reconstructions = []
for i in xrange(0, len(noisy_nums)):
recon = f_recon(noisy_nums[max(0,(i+1)-batch_size):i+1])
reconstructions.append(recon)
reconstructed = numpy.array(reconstructions)
# Concatenate stuff
stacked = numpy.vstack([numpy.vstack([nums[i*10 : (i+1)*10], noisy_nums[i*10 : (i+1)*10], reconstructed[i*10 : (i+1)*10]]) for i in range(10)])
number_reconstruction = PIL.Image.fromarray(tile_raster_images(stacked, (root_N_input,root_N_input), (10,30)))
number_reconstruction.save(outdir+'rnngsn_number_reconstruction_epoch_'+str(counter)+'.png')
#sample_numbers(counter, 'seven')
plot_samples(counter, 'rnngsn')
#save params
save_params_to_file('all', counter, params)
# ANNEAL!
new_lr = learning_rate.get_value() * annealing
learning_rate.set_value(new_lr)
# 10k samples
print 'Generating 10,000 samples'
samples, _ = sample_some_numbers(N=10000)
f_samples = outdir+'samples.npy'
numpy.save(f_samples, samples)
print 'saved digits'
def train(self, train_X=None, train_Y=None, valid_X=None, valid_Y=None, test_X=None, test_Y=None, is_artificial=False, artificial_sequence=1, continue_training=False):
log.maybeLog(self.logger, "\nTraining---------\n")
if train_X is None:
log.maybeLog(self.logger, "Training using data given during initialization of RNN-GSN.\n")
train_X = self.train_X
train_Y = self.train_Y
if train_X is None:
log.maybeLog(self.logger, "\nPlease provide a training dataset!\n")
raise AssertionError("Please provide a training dataset!")
else:
log.maybeLog(self.logger, "Training using data provided to training function.\n")
if valid_X is None:
valid_X = self.valid_X
valid_Y = self.valid_Y
if test_X is None:
test_X = self.test_X
test_Y = self.test_Y
##########################################################
# Train the GSN first to get good weights initialization #
##########################################################
if self.train_gsn_first:
log.maybeLog(self.logger, "\n\n----------Initially training the GSN---------\n\n")
init_gsn = generative_stochastic_network.GSN(train_X=train_X, valid_X=valid_X, test_X=test_X, args=self.gsn_args, logger=self.logger)
init_gsn.train()
#############################
# Save the model parameters #
#############################
def save_params_to_file(name, n, gsn_params):
pass
print 'saving parameters...'
save_path = self.outdir+name+'_params_epoch_'+str(n)+'.pkl'
f = open(save_path, 'wb')
try:
cPickle.dump(gsn_params, f, protocol=cPickle.HIGHEST_PROTOCOL)
finally:
f.close()
def save_params(params):
values = [param.get_value(borrow=True) for param in params]
return values
def restore_params(params, values):
for i in range(len(params)):
params[i].set_value(values[i])
#########################################
# If we are using Hessian-free training #
#########################################
if self.hessian_free:
pass
# gradient_dataset = hf_sequence_dataset([train_X.get_value()], batch_size=None, number_batches=5000)
# cg_dataset = hf_sequence_dataset([train_X.get_value()], batch_size=None, number_batches=1000)
# valid_dataset = hf_sequence_dataset([valid_X.get_value()], batch_size=None, number_batches=1000)
#
# s = x_samples
# costs = [cost, show_cost]
# hf_optimizer(params, [Xs], s, costs, u, ua).train(gradient_dataset, cg_dataset, initial_lambda=1.0, preconditioner=True, validation=valid_dataset)
################################
# If we are using SGD training #
################################
else:
log.maybeLog(self.logger, "\n-----------TRAINING RNN-GSN------------\n")
# TRAINING
STOP = False
counter = 0
if not continue_training:
self.learning_rate.set_value(self.init_learn_rate) # learning rate
times = []
best_cost = float('inf')
best_params = None
patience = 0
log.maybeLog(self.logger, ['train X size:',str(train_X.shape.eval())])
if valid_X is not None:
log.maybeLog(self.logger, ['valid X size:',str(valid_X.shape.eval())])
if test_X is not None:
log.maybeLog(self.logger, ['test X size:',str(test_X.shape.eval())])
if self.vis_init:
self.bias_list[0].set_value(logit(numpy.clip(0.9,0.001,train_X.get_value().mean(axis=0))))
while not STOP:
counter += 1
t = time.time()
log.maybeAppend(self.logger, [counter,'\t'])
if is_artificial:
data.sequence_mnist_data(train_X, train_Y, valid_X, valid_Y, test_X, test_Y, artificial_sequence, rng)
#train
train_costs = data.apply_cost_function_to_dataset(self.f_learn, train_X, self.batch_size)
# record it
log.maybeAppend(self.logger, ['Train:',trunc(train_costs),'\t'])
#valid
valid_costs = data.apply_cost_function_to_dataset(self.f_cost, valid_X, self.batch_size)
# record it
log.maybeAppend(self.logger, ['Valid:',trunc(valid_costs), '\t'])
#test
test_costs = data.apply_cost_function_to_dataset(self.f_cost, test_X, self.batch_size)
# record it
log.maybeAppend(self.logger, ['Test:',trunc(test_costs), '\t'])
#check for early stopping
cost = numpy.sum(valid_costs)
if cost < best_cost*self.early_stop_threshold:
patience = 0
best_cost = cost
# save the parameters that made it the best
best_params = save_params(self.params)
else:
patience += 1
if counter >= self.n_epoch or patience >= self.early_stop_length:
STOP = True
if best_params is not None:
restore_params(self.params, best_params)
save_params_to_file('all', counter, self.params)
timing = time.time() - t
times.append(timing)
log.maybeAppend(self.logger, 'time: '+make_time_units_string(timing)+'\t')
log.maybeLog(self.logger, 'remaining: '+make_time_units_string((self.n_epoch - counter) * numpy.mean(times)))
if (counter % self.save_frequency) == 0 or STOP is True:
n_examples = 100
nums = test_X.get_value(borrow=True)[range(n_examples)]
noisy_nums = self.f_noise(test_X.get_value(borrow=True)[range(n_examples)])
reconstructions = []
for i in xrange(0, len(noisy_nums)):
recon = self.f_recon(noisy_nums[max(0,(i+1)-self.batch_size):i+1])
reconstructions.append(recon)
reconstructed = numpy.array(reconstructions)
# Concatenate stuff
stacked = numpy.vstack([numpy.vstack([nums[i*10 : (i+1)*10], noisy_nums[i*10 : (i+1)*10], reconstructed[i*10 : (i+1)*10]]) for i in range(10)])
number_reconstruction = PIL.Image.fromarray(tile_raster_images(stacked, (self.root_N_input,self.root_N_input), (10,30)))
number_reconstruction.save(self.outdir+'rnngsn_number_reconstruction_epoch_'+str(counter)+'.png')
#save params
save_params_to_file('all', counter, self.params)
# ANNEAL!
new_lr = self.learning_rate.get_value() * self.annealing
self.learning_rate.set_value(new_lr)
def train(self,
train_X=None,
train_Y=None,
valid_X=None,
valid_Y=None,
test_X=None,
test_Y=None,
is_artificial=False,
artificial_sequence=1,
continue_training=False):
log.maybeLog(self.logger, "\nTraining---------\n")
if train_X is None:
log.maybeLog(
self.logger,
"Training using data given during initialization of RNN-GSN.\n"
)
train_X = self.train_X
train_Y = self.train_Y
if train_X is None:
log.maybeLog(self.logger,
"\nPlease provide a training dataset!\n")
raise AssertionError("Please provide a training dataset!")
else:
log.maybeLog(
self.logger,
"Training using data provided to training function.\n")
if valid_X is None:
valid_X = self.valid_X
valid_Y = self.valid_Y
if test_X is None:
test_X = self.test_X
test_Y = self.test_Y
##########################################################
# Train the GSN first to get good weights initialization #
##########################################################
if self.train_gsn_first:
log.maybeLog(
self.logger,
"\n\n----------Initially training the GSN---------\n\n")
init_gsn = generative_stochastic_network.GSN(train_X=train_X,
valid_X=valid_X,
test_X=test_X,
args=self.gsn_args,
logger=self.logger)
init_gsn.train()
#############################
# Save the model parameters #
#############################
def save_params_to_file(name, n, gsn_params):
pass
print 'saving parameters...'
save_path = self.outdir + name + '_params_epoch_' + str(n) + '.pkl'
f = open(save_path, 'wb')
try:
cPickle.dump(gsn_params, f, protocol=cPickle.HIGHEST_PROTOCOL)
finally:
f.close()
def save_params(params):
values = [param.get_value(borrow=True) for param in params]
return values
def restore_params(params, values):
for i in range(len(params)):
params[i].set_value(values[i])
#########################################
# If we are using Hessian-free training #
#########################################
if self.hessian_free:
pass
# gradient_dataset = hf_sequence_dataset([train_X.get_value()], batch_size=None, number_batches=5000)
# cg_dataset = hf_sequence_dataset([train_X.get_value()], batch_size=None, number_batches=1000)
# valid_dataset = hf_sequence_dataset([valid_X.get_value()], batch_size=None, number_batches=1000)
#
# s = x_samples
# costs = [cost, show_cost]
# hf_optimizer(params, [Xs], s, costs, u, ua).train(gradient_dataset, cg_dataset, initial_lambda=1.0, preconditioner=True, validation=valid_dataset)
################################
# If we are using SGD training #
################################
else:
log.maybeLog(self.logger,
"\n-----------TRAINING RNN-GSN------------\n")
# TRAINING
STOP = False
counter = 0
if not continue_training:
self.learning_rate.set_value(
self.init_learn_rate) # learning rate
times = []
best_cost = float('inf')
best_params = None
patience = 0
log.maybeLog(
self.logger,
['train X size:', str(train_X.shape.eval())])
if valid_X is not None:
log.maybeLog(self.logger,
['valid X size:',
str(valid_X.shape.eval())])
if test_X is not None:
log.maybeLog(
self.logger,
['test X size:', str(test_X.shape.eval())])
if self.vis_init:
self.bias_list[0].set_value(
logit(
numpy.clip(0.9, 0.001,
train_X.get_value().mean(axis=0))))
while not STOP:
counter += 1
t = time.time()
log.maybeAppend(self.logger, [counter, '\t'])
if is_artificial:
data.sequence_mnist_data(train_X, train_Y, valid_X,
valid_Y, test_X, test_Y,
artificial_sequence, rng)
#train
train_costs = data.apply_cost_function_to_dataset(
self.f_learn, train_X, self.batch_size)
# record it
log.maybeAppend(self.logger,
['Train:', trunc(train_costs), '\t'])
#valid
valid_costs = data.apply_cost_function_to_dataset(
self.f_cost, valid_X, self.batch_size)
# record it
log.maybeAppend(self.logger,
['Valid:', trunc(valid_costs), '\t'])
#test
test_costs = data.apply_cost_function_to_dataset(
self.f_cost, test_X, self.batch_size)
# record it
log.maybeAppend(self.logger,
['Test:', trunc(test_costs), '\t'])
#check for early stopping
cost = numpy.sum(valid_costs)
if cost < best_cost * self.early_stop_threshold:
patience = 0
best_cost = cost
# save the parameters that made it the best
best_params = save_params(self.params)
else:
patience += 1
if counter >= self.n_epoch or patience >= self.early_stop_length:
STOP = True
if best_params is not None:
restore_params(self.params, best_params)
save_params_to_file('all', counter, self.params)
timing = time.time() - t
times.append(timing)
log.maybeAppend(
self.logger,
'time: ' + make_time_units_string(timing) + '\t')
log.maybeLog(
self.logger, 'remaining: ' + make_time_units_string(
(self.n_epoch - counter) * numpy.mean(times)))
if (counter % self.save_frequency) == 0 or STOP is True:
n_examples = 100
nums = test_X.get_value(borrow=True)[range(n_examples)]
noisy_nums = self.f_noise(
test_X.get_value(borrow=True)[range(n_examples)])
reconstructions = []
for i in xrange(0, len(noisy_nums)):
recon = self.f_recon(
noisy_nums[max(0, (i + 1) - self.batch_size):i +
1])
reconstructions.append(recon)
reconstructed = numpy.array(reconstructions)
# Concatenate stuff
stacked = numpy.vstack([
numpy.vstack([
nums[i * 10:(i + 1) * 10],
noisy_nums[i * 10:(i + 1) * 10],
reconstructed[i * 10:(i + 1) * 10]
]) for i in range(10)
])
number_reconstruction = PIL.Image.fromarray(
tile_raster_images(
stacked, (self.root_N_input, self.root_N_input),
(10, 30)))
number_reconstruction.save(
self.outdir + 'rnngsn_number_reconstruction_epoch_' +
str(counter) + '.png')
#save params
save_params_to_file('all', counter, self.params)
# ANNEAL!
new_lr = self.learning_rate.get_value() * self.annealing
self.learning_rate.set_value(new_lr)