def runAutoencoder():
ds = StockPrice()
# print ds.train[0][0]
data = np.random.randn(10, 5).astype(config.floatX)
# print data
print BinomialCorruptor(0.2)
ae = DenoisingAutoencoder(
BinomialCorruptor(corruption_level=0.2), 1000, 100, act_enc="sigmoid", act_dec="linear", tied_weights=False
)
trainer = sgd.SGD(
learning_rate=0.005,
batch_size=5,
termination_criterion=EpochCounter(3),
cost=cost_ae.MeanSquaredReconstructionError(),
monitoring_batches=5,
monitoring_dataset=ds,
)
trainer.setup(ae, ds)
while True:
trainer.train(dataset=ds)
ae.monitor()
ae.monitor.report_epoch()
if not trainer.continue_learning(ae):
break
# print ds.train[0][0]
# print ae.reconstruct(ds.train[0][0])
w = ae.weights.get_value()
# ae.hidbias.set_value(np.random.randn(1000).astype(config.floatX))
hb = ae.hidbias.get_value()
# ae.visbias.set_value(np.random.randn(100).astype(config.floatX))
vb = ae.visbias.get_value()
d = tensor.matrix()
result = np.dot(1.0 / (1 + np.exp(-hb - np.dot(ds.train[0][0], w))), w.T) + vb
def runAutoencoder():
ds = StockPrice()
#print ds.train[0][0]
data = np.random.randn(10, 5).astype(config.floatX)
#print data
print BinomialCorruptor(.2)
ae = DenoisingAutoencoder(BinomialCorruptor(corruption_level=.2), 1000, 100, act_enc='sigmoid', act_dec='linear',
tied_weights=False)
trainer = sgd.SGD(learning_rate=.005, batch_size=5, termination_criterion=EpochCounter(3), cost=cost_ae.MeanSquaredReconstructionError(), monitoring_batches=5, monitoring_dataset=ds)
trainer.setup(ae, ds)
while True:
trainer.train(dataset=ds)
ae.monitor()
ae.monitor.report_epoch()
if not trainer.continue_learning(ae):
break
#print ds.train[0][0]
#print ae.reconstruct(ds.train[0][0])
w = ae.weights.get_value()
#ae.hidbias.set_value(np.random.randn(1000).astype(config.floatX))
hb = ae.hidbias.get_value()
#ae.visbias.set_value(np.random.randn(100).astype(config.floatX))
vb = ae.visbias.get_value()
d = tensor.matrix()
result = np.dot(1. / (1 + np.exp(-hb - np.dot(ds.train[0][0], w))), w.T) + vb
def learn_manifold(data_train, model_fname):
arch = [30]
learning_rate = 0.005
max_iters = 1000
batch_size = 100
tied_weights = False
print('Training a denoising autoencoder...')
monitoring_dataset = {'train': data_train}
im_dim = data_train.X.shape[1]
model = DenoisingAutoencoder(nvis=im_dim, nhid=arch[0], irange=0.05,
corruptor=BinomialCorruptor(corruption_level=0.5),
act_enc="tanh",
act_dec="sigmoid", tied_weights=tied_weights)
algorithm = pylearn2.training_algorithms.sgd.\
SGD(learning_rate=learning_rate,
batch_size=batch_size,
monitoring_dataset=monitoring_dataset,
cost=MeanSquaredReconstructionError(),
termination_criterion=pylearn2.\
termination_criteria.And(criteria=[
MonitorBased(channel_name='train_objective',
prop_decrease=0.001, N=10),
EpochCounter(max_epochs=max_iters)])
)
extensions = [
pylearn2.train_extensions.best_params.\
MonitorBasedSaveBest(channel_name='train_objective',
save_path=model_fname)]
trainer = pylearn2.train.Train(dataset=data_train, model=model,
algorithm=algorithm,
extensions=extensions,
save_freq=0)
trainer.main_loop()
if False:
I1 = data_train.X[np.random.randint(0,data_train.X.shape[0])]
im_side = np.sqrt(I1.size)
I1[np.random.rand(I1.size)>0.8] = 0
I2 = (model.reconstruct(theano.shared(I1)).eval()>0.5).astype(int)
plt.subplot(121)
plt.imshow(I1.reshape((im_side, im_side)), cmap='Greys',
interpolation='nearest', vmin=0, vmax=f1)
plt.subplot(122)
plt.imshow(I2.reshape((im_side, im_side)), cmap='Greys',
interpolation='nearest', vmin=0, vmax=1)
plt.show()
def combine_sublayers(self):
print "Combining sub-layers"
# Create a large 2560 unit DAE. The model is considered trained by the concatenation
# of its 512 unit sub-layers. The 2560 hidden units weights will be initialized
# by these.
nvis = 3072
nhid = 2560
irange = 0.05
corruption = 0.2
corruptor = BinomialCorruptor(corruption_level=corruption)
activation_encoder = "tanh"
activation_decoder = None
# By default, the DAE initializes the weights at random
# Since we're using our own already pre-trained weights
# We will instead change those values.
large_dae = DenoisingAutoencoder(nvis=nvis, nhid=nhid, corruptor=corruptor, irange=irange, act_enc=activation_encoder, act_dec=activation_decoder)
# Do not need to change hidden or visible bias.
# They are static vars in theory.
large_dae._params = [
large_dae.visbias,
large_dae.hidbias,
# Here is where we change the weights.
large_dae.weights
]
numpy_array = np.zeros((3072, 2560))
# Load sub-layer models and get their weights.
for i in range (0,5):
fo = open(self.save_path+str(i)+".pkl", 'rb')
# 768 Vis, 512 hidden unit DAE.
small_dae = cPickle.load(fo)
fo.close()
# TODO: Create numpy array of proper values to set the large_dae.
# Get the weights from the small_dae's
# so that they can be appended together.
weights = small_dae.weights.get_value()
large_dae_weights.append(weights)
print "Successfully combined sub-layers"
def new_model(self, model_params, dataset):
corruptor = BinomialCorruptor(
corruption_level=model_params['noise_level'])
model = DenoisingAutoencoder(nvis=dataset.X.shape[1],
nhid=model_params['hidden_outputs'],
irange=model_params['irange'],
corruptor=corruptor,
act_enc='tanh',
act_dec=None)
return model
def get_denoising_autoencoder(structure):
n_input, n_output = structure
curruptor = BinomialCorruptor(corruption_level=0.5)
config = {
'corruptor': curruptor,
'nhid': n_output,
'nvis': n_input,
'tied_weights': True,
'act_enc': 'sigmoid',
'act_dec': 'sigmoid',
'irange': 0.001,
}
return DenoisingAutoencoder(**config)
def get_denoising_autoencoder(structure,corr_val):
n_input, n_output = structure
corruptor = BinomialCorruptor(corruption_level=corr_val)
#corruptor = GaussianCorruptor(stdev=0.25)
config = {
'corruptor': corruptor,
'nhid': n_output,
'nvis': n_input,
'tied_weights': True,
'act_enc': 'sigmoid',
'act_dec': 'sigmoid',
'irange': 4*np.sqrt(6. / (n_input + n_output)),
}
return DenoisingAutoencoder(**config)
def create_layer_one(self):
which_set = "train"
one_hot = True
start = 0
# Creating 5 random patch layers based on 8,000 samples (Saturation point where the objective no longer improves.
stop = 800
# GridPatchCIFAR10 Randomly selects 5 16x16 patches from each image, and we do this 5 times. This helps increase training time and captures more information. Similar to how the neurons in the eye are attached to a specific region in the image.
dataset = GridPatchCIFAR10(which_set=which_set, one_hot=one_hot, start=start, stop=stop)
# Denoising autoencoder model hyper-parameters
nvis = 768
nhid = 512
irange = 0.05
corruption_lvl = 0.2
corruptor = BinomialCorruptor(corruption_level=corruption_lvl)
activation_encoder = "tanh"
# Linear activation
activation_decoder = None
# Creating the denoising autoencoder
model = DenoisingAutoencoder(nvis=nvis, nhid=nhid, irange=irange, corruptor=corruptor, act_enc=activation_encoder, act_dec=activation_decoder)
# Parameters for SGD learning algorithm instantiated below
learning_rate = 0.001
batch_size = 100
monitoring_batches = 5
monitoring_dataset = dataset
cost = MeanSquaredReconstructionError()
max_epochs = 10
termination_criterion = EpochCounter(max_epochs=max_epochs)
# SGD Learning algorithm
algorithm = SGD(learning_rate=learning_rate, batch_size=batch_size, monitoring_batches=monitoring_batches, monitoring_dataset=dataset, cost=cost, termination_criterion=termination_criterion)
processes = []
for i in range(0,5):
print "Training DAE Sub-Layer: ", i
save_path = self.save_path+str(i)+".pkl"
save_freq = 1
train = Train(dataset=dataset,model=model,algorithm=algorithm, save_path=save_path, save_freq=save_freq)
p = Process(target=train.main_loop, args=())
p.start()
processes.append(p)
for process in processes:
process.join()
def main():
# Only the trainset is processed by this function.
print 'getting preprocessed data to train model'
pp_trainset, testset = get_processed_dataset()
# remember to change here when changing datasets
print 'loading unprocessed data for input displays'
trainset = cifar10.CIFAR10(which_set="train")
dmat = trainset.get_design_matrix()
nvis = dmat.shape[1]
model = DenoisingAutoencoder(
corruptor=BinomialCorruptor(corruption_level=0.5),
nhid=nhid,
nvis=nvis,
act_enc='sigmoid',
act_dec='sigmoid',
irange=.01)
algorithm = SGD(
learning_rate=0.1,
cost=MeanSquaredReconstructionError(),
batch_size=1000,
monitoring_batches=10,
monitoring_dataset=pp_trainset,
termination_criterion=EpochCounter(max_epochs=MAX_EPOCHS_UNSUPERVISED),
update_callbacks=None)
extensions = None
trainer = Train(model=model,
algorithm=algorithm,
save_path='testrun.pkl',
save_freq=1,
extensions=extensions,
dataset=pp_trainset)
trainer.main_loop()
def main():
# the data isn't going to be fully processed, so we may have to
# do some stuff to the testset still to make it work.
trainset, testset = get_processed_dataset()
# Creating the patch-pairs:
design_matrix = trainset.get_design_matrix()
processed_patch_size = design_matrix.shape[1]
num_images = train_size
examples_per_image = patches_per_image * (patches_per_image - 1)
num_examples = examples_per_image * num_images
stamps = trainset.stamps
max_stamp = input_width - patch_width
d_size = (2 * max_stamp + 1)**input_dim
patch_pairs = np.zeros((num_examples, 2 * processed_patch_size))
distances = np.zeros((num_examples, input_dim))
distances_onehot = np.zeros((num_examples, d_size))
examples = np.zeros((num_examples, 2 * processed_patch_size + d_size))
nvis = 2 * processed_patch_size + d_size
def flatten_encoding(encoding, max_stamp):
dims = len(encoding)
flat_encoding = 0
for i in xrange(dims - 1):
flat_encoding += encoding[i]
flat_encoding *= max_stamp
flat_encoding += encoding[-1]
# Can be done without (or with less) for loops?
print 'begin for loop'
for i in xrange(num_images):
if (i % 1000 == 0):
print i, '-th outer loop...'
for j in xrange(patches_per_image):
patch1_num = i * patches_per_image + j
patch1_pos = stamps[patch1_num, :]
for k in xrange(patches_per_image):
example_num = i * examples_per_image + j * (patches_per_image -
1) + k
if (k > j):
example_num -= 1
if (k != j):
patch2_num = i * patches_per_image + k
patch2_pos = stamps[patch2_num, :]
distance = patch1_pos - patch2_pos
distances[example_num] = distance
distance_encoding = distance + max_stamp
distance_encoding = flatten_encoding(
distance_encoding, max_stamp)
distances_onehot[example_num, distance_encoding] = 1
p1 = design_matrix[patch1_num]
p2 = design_matrix[patch2_num]
patch_pairs[example_num] = np.hstack((p1, p2))
examples[example_num] = np.hstack(
(patch_pairs[example_num],
distances_onehot[example_num]))
print 'end for loop'
trainset.set_design_matrix(examples)
model = DenoisingAutoencoder(
corruptor=BinomialCorruptor(corruption_level=0.5),
nhid=nhid,
nvis=nvis,
act_enc='sigmoid',
act_dec='sigmoid',
irange=.01)
algorithm = SGD(
learning_rate=0.1,
cost=MeanSquaredReconstructionError(),
batch_size=100,
monitoring_batches=10,
monitoring_dataset=trainset,
termination_criterion=EpochCounter(max_epochs=MAX_EPOCHS_UNSUPERVISED),
update_callbacks=None)
extensions = None
trainer = Train(model=model,
algorithm=algorithm,
save_path='run.pkl',
save_freq=1,
extensions=extensions,
dataset=trainset)
trainer.main_loop()
def main():
# Only the trainset is processed by this function.
print 'getting preprocessed data for training model'
pp_trainset, testset = get_processed_dataset()
# remember to change here when changing datasets
print 'loading unprocessed data for input displays'
trainset = cifar10.CIFAR10(which_set="train")
dmat = pp_trainset.get_design_matrix()
nvis = dmat.shape[1]
model = DenoisingAutoencoder(
corruptor=BinomialCorruptor(corruption_level=0.3),
nhid=nhid,
nvis=nvis,
act_enc='sigmoid',
act_dec='sigmoid',
irange=.01)
algorithm = SGD(
learning_rate=learning_rate,
cost=MeanSquaredReconstructionError(),
batch_size=100,
monitoring_batches=10,
monitoring_dataset=pp_trainset,
termination_criterion=EpochCounter(max_epochs=MAX_EPOCHS_UNSUPERVISED),
update_callbacks=None)
extensions = None
trainer = Train(model=model,
algorithm=algorithm,
save_path='run.pkl',
save_freq=1,
extensions=extensions,
dataset=pp_trainset)
trainer.main_loop()
####################
# Plot and Save:
# choose random patch-pairs to plot
stamps = pp_trainset.stamps
num_examples = stamps.shape[0]
to_plot = np.random.randint(0, num_examples, num2plot)
# use to_plot indices to extract data
stamps_data = stamps[to_plot]
image_numbers = stamps[to_plot, 0].astype(int)
X = trainset.X
images_data = trainset.get_topological_view(X[image_numbers])
p1x = stamps_data[:, 1]
p1y = stamps_data[:, 2]
p2x = stamps_data[:, 3]
p2y = stamps_data[:, 4]
# For input ppd's, once we've identified the patches, we just outline them and draw an arrow for d
# This might mess with original trainset (I dunno), in which case, we should make a copy
add_outlines(images_data, p1x, p1y, patch_width)
add_outlines(images_data, p2x, p2y, patch_width)
##################################################
# translating outputs back into things we can plot
dataset = pp_trainset
Xout = dataset.X.astype('float32')
max_stamp = input_width - patch_width
d_size = (2 * max_stamp + 1)**input_dim
# displacement
d_enc = Xout[:, -d_size:]
d_out_flat = np.argmax(d_enc, axis=1)
d_shape = [2 * max_stamp + 1, 2 * max_stamp + 1] # assumed 2D
d_out = flat_to_2D(d_out_flat, d_shape)
d_out[to_plot, ]
# patches
vc = dataset.view_converter
p_enc = Xout[:, :len(Xout.T) - d_size]
p_size = p_enc.shape[1] / 2
p1_enc = p_enc[:, :p_size]
p2_enc = p_enc[:, p_size:]
p1_enc = vc.design_mat_to_topo_view(p1_enc)
p2_enc = vc.design_mat_to_topo_view(p2_enc)
pp = dataset.preprocessor
gcn = pp.items[1]
means = gcn.means
normalizers = gcn.normalizers
toshape = (num_examples, )
for i in range(input_dim):
toshape += (1, )
if num_channels != 1:
toshape += (1, )
# When the number of patches and patch-pairs differs, this breaks.
# I need to match up normalizers/means with their corresponding patches
# undoing the PCA might be breaking too, but without errors...
normalizers1 = expand_p1(normalizers)
normalizers2 = expand_p2(normalizers)
means1 = expand_p1(means)
means2 = expand_p2(means)
p1_enc *= normalizers1.reshape(toshape)
p1_enc += means1.reshape(toshape)
p2_enc *= normalizers2.reshape(toshape)
p2_enc += means2.reshape(toshape)
# Now, we pull off the same examples from the data to compare to dAE inputs in plots
outputs = copy.deepcopy(images_data)
insertpatches(outputs, p1_enc[to_plot], p1x, p1y, patch_width)
insertpatches(outputs, p2_enc[to_plot], p2x, p2y, patch_width)
plt.figure()
for i in range(num2plot):
# Inputs
plt.subplot(num2plot, 2, 2 * i + 1)
plt.imshow(images_data[i], cmap=cm.Greys_r)
print stamps_data[i]
a = (stamps_data[i, 2] + patch_width / 2,
stamps_data[i, 1] + patch_width / 2, stamps_data[i, 6],
stamps_data[i, 5])
plt.arrow(a[0], a[1], a[2], a[3], head_width=1.0, head_length=0.6)
# Outputs
plt.subplot(num2plot, 2, 2 * i + 2)
plt.imshow(outputs[i], cmap=cm.Greys_r)
plt.arrow(a[0],
a[1],
d_out[to_plot[i], 1],
d_out[to_plot[i], 0],
head_width=1.0,
head_length=0.6)
plt.show()
savestr = 'cifar_ppd.png'
plt.savefig(savestr)
