class GMVAE:
def __init__(self, args):
self.num_epochs = args.epochs
self.cuda = args.cuda
self.verbose = args.verbose
self.batch_size = args.batch_size
self.batch_size_val = args.batch_size_val
self.learning_rate = args.learning_rate
self.decay_epoch = args.decay_epoch
self.lr_decay = args.lr_decay
self.w_cat = args.w_categ
self.w_gauss = args.w_gauss
self.w_rec = args.w_rec
self.rec_type = args.rec_type
self.num_classes = args.num_classes
self.gaussian_size = args.gaussian_size
self.input_size = args.input_size
# gumbel
self.init_temp = args.init_temp
self.decay_temp = args.decay_temp
self.hard_gumbel = args.hard_gumbel
self.min_temp = args.min_temp
self.decay_temp_rate = args.decay_temp_rate
self.gumbel_temp = self.init_temp
self.network = GMVAENet(self.input_size, self.gaussian_size,
self.num_classes)
self.losses = LossFunctions()
self.metrics = Metrics()
if self.cuda:
self.network = self.network.cuda()
def unlabeled_loss(self, data, out_net):
"""Method defining the loss functions derived from the variational lower bound
Args:
data: (array) corresponding array containing the input data
out_net: (dict) contains the graph operations or nodes of the network output
Returns:
loss_dic: (dict) contains the values of each loss function and predictions
"""
# obtain network variables
z, data_recon = out_net['gaussian'], out_net['x_rec']
logits, prob_cat = out_net['logits'], out_net['prob_cat']
y_mu, y_var = out_net['y_mean'], out_net['y_var']
mu, var = out_net['mean'], out_net['var']
# reconstruction loss
loss_rec = self.losses.reconstruction_loss(data, data_recon,
self.rec_type)
# gaussian loss
loss_gauss = self.losses.gaussian_loss(z, mu, var, y_mu, y_var)
# categorical loss
loss_cat = -self.losses.entropy(logits, prob_cat) - np.log(0.1)
# total loss
loss_total = self.w_rec * loss_rec + self.w_gauss * loss_gauss + self.w_cat * loss_cat
# obtain predictions
_, predicted_labels = torch.max(logits, dim=1)
loss_dic = {
'total': loss_total,
'predicted_labels': predicted_labels,
'reconstruction': loss_rec,
'gaussian': loss_gauss,
'categorical': loss_cat
}
return loss_dic
def train_epoch(self, optimizer, data_loader):
"""Train the model for one epoch
Args:
optimizer: (Optim) optimizer to use in backpropagation
data_loader: (DataLoader) corresponding loader containing the training data
Returns:
average of all loss values, accuracy, nmi
"""
self.network.train()
total_loss = 0.
recon_loss = 0.
cat_loss = 0.
gauss_loss = 0.
accuracy = 0.
nmi = 0.
num_batches = 0.
true_labels_list = []
predicted_labels_list = []
# iterate over the dataset
for (data, labels) in data_loader:
if self.cuda == 1:
data = data.cuda()
optimizer.zero_grad()
# flatten data
data = data.view(data.size(0), -1)
# forward call
out_net = self.network(data, self.gumbel_temp, self.hard_gumbel)
unlab_loss_dic = self.unlabeled_loss(data, out_net)
total = unlab_loss_dic['total']
# accumulate values
total_loss += total.item()
recon_loss += unlab_loss_dic['reconstruction'].item()
gauss_loss += unlab_loss_dic['gaussian'].item()
cat_loss += unlab_loss_dic['categorical'].item()
# perform backpropagation
total.backward()
optimizer.step()
# save predicted and true labels
predicted = unlab_loss_dic['predicted_labels']
true_labels_list.append(labels)
predicted_labels_list.append(predicted)
num_batches += 1.
# average per batch
total_loss /= num_batches
recon_loss /= num_batches
gauss_loss /= num_batches
cat_loss /= num_batches
# concat all true and predicted labels
true_labels = torch.cat(true_labels_list, dim=0).cpu().numpy()
predicted_labels = torch.cat(predicted_labels_list,
dim=0).cpu().numpy()
# compute metrics
accuracy = 100.0 * self.metrics.cluster_acc(predicted_labels,
true_labels)
nmi = 100.0 * self.metrics.nmi(predicted_labels, true_labels)
return total_loss, recon_loss, gauss_loss, cat_loss, accuracy, nmi
def test(self, data_loader, return_loss=False):
"""Test the model with new data
Args:
data_loader: (DataLoader) corresponding loader containing the test/validation data
return_loss: (boolean) whether to return the average loss values
Return:
accuracy and nmi for the given test data
"""
self.network.eval()
total_loss = 0.
recon_loss = 0.
cat_loss = 0.
gauss_loss = 0.
accuracy = 0.
nmi = 0.
num_batches = 0.
true_labels_list = []
predicted_labels_list = []
with torch.no_grad():
for data, labels in data_loader:
if self.cuda == 1:
data = data.cuda()
# flatten data
data = data.view(data.size(0), -1)
# forward call
out_net = self.network(data, self.gumbel_temp,
self.hard_gumbel)
unlab_loss_dic = self.unlabeled_loss(data, out_net)
# accumulate values
total_loss += unlab_loss_dic['total'].item()
recon_loss += unlab_loss_dic['reconstruction'].item()
gauss_loss += unlab_loss_dic['gaussian'].item()
cat_loss += unlab_loss_dic['categorical'].item()
# save predicted and true labels
predicted = unlab_loss_dic['predicted_labels']
true_labels_list.append(labels)
predicted_labels_list.append(predicted)
num_batches += 1.
# average per batch
if return_loss:
total_loss /= num_batches
recon_loss /= num_batches
gauss_loss /= num_batches
cat_loss /= num_batches
# concat all true and predicted labels
true_labels = torch.cat(true_labels_list, dim=0).cpu().numpy()
predicted_labels = torch.cat(predicted_labels_list,
dim=0).cpu().numpy()
# compute metrics
accuracy = 100.0 * self.metrics.cluster_acc(predicted_labels,
true_labels)
nmi = 100.0 * self.metrics.nmi(predicted_labels, true_labels)
if return_loss:
return total_loss, recon_loss, gauss_loss, cat_loss, accuracy, nmi
else:
return accuracy, nmi
def train(self, train_loader, val_loader):
"""Train the model
Args:
train_loader: (DataLoader) corresponding loader containing the training data
val_loader: (DataLoader) corresponding loader containing the validation data
Returns:
output: (dict) contains the history of train/val loss
"""
optimizer = optim.Adam(self.network.parameters(),
lr=self.learning_rate)
train_history_acc, val_history_acc = [], []
train_history_nmi, val_history_nmi = [], []
for epoch in range(1, self.num_epochs + 1):
train_loss, train_rec, train_gauss, train_cat, train_acc, train_nmi = self.train_epoch(
optimizer, train_loader)
val_loss, val_rec, val_gauss, val_cat, val_acc, val_nmi = self.test(
val_loader, True)
# if verbose then print specific information about training
if self.verbose == 1:
print("(Epoch %d / %d)" % (epoch, self.num_epochs))
print("Train - REC: %.5lf; Gauss: %.5lf; Cat: %.5lf;" % \
(train_rec, train_gauss, train_cat))
print("Valid - REC: %.5lf; Gauss: %.5lf; Cat: %.5lf;" % \
(val_rec, val_gauss, val_cat))
print("Accuracy=Train: %.5lf; Val: %.5lf NMI=Train: %.5lf; Val: %.5lf Total Loss=Train: %.5lf; Val: %.5lf" % \
(train_acc, val_acc, train_nmi, val_nmi, train_loss, val_loss))
else:
print('(Epoch %d / %d) Train_Loss: %.3lf; Val_Loss: %.3lf Train_ACC: %.3lf; Val_ACC: %.3lf Train_NMI: %.3lf; Val_NMI: %.3lf' % \
(epoch, self.num_epochs, train_loss, val_loss, train_acc, val_acc, train_nmi, val_nmi))
# decay gumbel temperature
if self.decay_temp == 1:
self.gumbel_temp = np.maximum(
self.init_temp * np.exp(-self.decay_temp_rate * epoch),
self.min_temp)
if self.verbose == 1:
print("Gumbel Temperature: %.3lf" % self.gumbel_temp)
train_history_acc.append(train_acc)
val_history_acc.append(val_acc)
train_history_nmi.append(train_nmi)
val_history_nmi.append(val_nmi)
return {
'train_history_nmi': train_history_nmi,
'val_history_nmi': val_history_nmi,
'train_history_acc': train_history_acc,
'val_history_acc': val_history_acc
}
def latent_features(self, data_loader, return_labels=False):
"""Obtain latent features learnt by the model
Args:
data_loader: (DataLoader) loader containing the data
return_labels: (boolean) whether to return true labels or not
Returns:
features: (array) array containing the features from the data
"""
self.network.eval()
N = len(data_loader.dataset)
features = np.zeros((N, self.gaussian_size))
if return_labels:
true_labels = np.zeros(N, dtype=np.int64)
start_ind = 0
with torch.no_grad():
for (data, labels) in data_loader:
if self.cuda == 1:
data = data.cuda()
# flatten data
data = data.view(data.size(0), -1)
out = self.network.inference(data, self.gumbel_temp,
self.hard_gumbel)
latent_feat = out['mean']
end_ind = min(start_ind + data.size(0), N + 1)
# return true labels
if return_labels:
true_labels[start_ind:end_ind] = labels.cpu().numpy()
features[start_ind:end_ind] = latent_feat.cpu().detach().numpy(
)
start_ind += data.size(0)
if return_labels:
return features, true_labels
return features
def reconstruct_data(self, data_loader, sample_size=-1):
"""Reconstruct Data
Args:
data_loader: (DataLoader) loader containing the data
sample_size: (int) size of random data to consider from data_loader
Returns:
reconstructed: (array) array containing the reconstructed data
"""
self.network.eval()
# sample random data from loader
indices = np.random.randint(0,
len(data_loader.dataset),
size=sample_size)
test_random_loader = torch.utils.data.DataLoader(
data_loader.dataset,
batch_size=sample_size,
sampler=SubsetRandomSampler(indices))
# obtain values
it = iter(test_random_loader)
test_batch_data, _ = it.next()
original = test_batch_data.data.numpy()
if self.cuda:
test_batch_data = test_batch_data.cuda()
# obtain reconstructed data
out = self.network(test_batch_data, self.gumbel_temp, self.hard_gumbel)
reconstructed = out['x_rec']
return original, reconstructed.data.cpu().numpy()
def plot_latent_space(self, data_loader, save=False):
"""Plot the latent space learnt by the model
Args:
data: (array) corresponding array containing the data
labels: (array) corresponding array containing the labels
save: (bool) whether to save the latent space plot
Returns:
fig: (figure) plot of the latent space
"""
# obtain the latent features
features = self.latent_features(data_loader)
# plot only the first 2 dimensions
fig = plt.figure(figsize=(8, 6))
plt.scatter(features[:, 0],
features[:, 1],
c=labels,
marker='o',
edgecolor='none',
cmap=plt.cm.get_cmap('jet', 10),
s=10)
plt.colorbar()
if (save):
fig.savefig('latent_space.png')
return fig
def random_generation(self, num_elements=1):
"""Random generation for each category
Args:
num_elements: (int) number of elements to generate
Returns:
generated data according to num_elements
"""
# categories for each element
arr = np.array([])
for i in range(self.num_classes):
arr = np.hstack([arr, np.ones(num_elements) * i])
indices = arr.astype(int).tolist()
categorical = F.one_hot(torch.tensor(indices),
self.num_classes).float()
if self.cuda:
categorical = categorical.cuda()
# infer the gaussian distribution according to the category
mean, var = self.network.generative.pzy(categorical)
# gaussian random sample by using the mean and variance
noise = torch.randn_like(var)
std = torch.sqrt(var)
gaussian = mean + noise * std
# generate new samples with the given gaussian
generated = self.network.generative.pxz(gaussian)
return generated.cpu().detach().numpy()
class GMVAE:
def __init__(self, params):
self.batch_size = params.batch_size
self.batch_size_val = params.batch_size_val
self.initial_temperature = params.temperature
self.decay_temperature = params.decay_temperature
self.num_epochs = params.num_epochs
self.loss_type = params.loss_type
self.num_classes = params.num_classes
self.w_gauss = params.w_gaussian
self.w_categ = params.w_categorical
self.w_recon = params.w_reconstruction
self.decay_temp_rate = params.decay_temp_rate
self.gaussian_size = params.gaussian_size
self.min_temperature = params.min_temperature
self.temperature = params.temperature # current temperature
self.verbose = params.verbose
self.sess = tf.Session()
self.network = Networks(params)
self.losses = LossFunctions()
self.learning_rate = tf.placeholder(tf.float32, [])
self.lr = params.learning_rate
self.decay_epoch = params.decay_epoch
self.lr_decay = params.lr_decay
self.dataset = params.dataset
self.metrics = Metrics()
def create_dataset(self, is_training, data, labels, batch_size):
"""Create dataset given input data
Args:
is_training: (bool) whether to use the train or test pipeline.
At training, we shuffle the data and have multiple epochs
data: (array) corresponding array containing the input data
labels: (array) corresponding array containing the labels of the input data
batch_size: (int) size of each batch to consider from the data
Returns:
output: (dict) contains what will be the input of the tensorflow graph
"""
num_samples = data.shape[0]
# create dataset object
if labels is None:
dataset = tf.data.Dataset.from_tensor_slices(data)
else:
dataset = tf.data.Dataset.from_tensor_slices((data, labels))
# shuffle data in training phase
if is_training:
dataset = dataset.shuffle(num_samples).repeat()
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(1)
# create reinitializable iterator from dataset
iterator = dataset.make_initializable_iterator()
if labels is None:
data = iterator.get_next()
else:
data, labels = iterator.get_next()
iterator_init = iterator.initializer
output = {
'data': data,
'labels': labels,
'iterator_init': iterator_init
}
return output
def unlabeled_loss(self, data, latent_spec, output_size, is_training=True):
"""Model function defining the loss functions derived from the variational lower bound
Args:
data: (array) corresponding array containing the input data
latent_spec: (dict) contains the graph operations or nodes of the latent variables
output_size: (int) size of the output layer
is_training: (bool) whether we are in training phase or not
Returns:
loss_dic: (dict) contains the values of each loss function and predictions
"""
gaussian, mean, var = latent_spec['gaussian'], latent_spec[
'mean'], latent_spec['var']
categorical, prob, log_prob = latent_spec['categorical'], latent_spec[
'prob_cat'], latent_spec['log_prob_cat']
_logits, features = latent_spec['logits'], latent_spec['features']
output, y_mean, y_var = latent_spec['output'], latent_spec[
'y_mean'], latent_spec['y_var']
# reconstruction loss
if self.loss_type == 'bce':
loss_rec = self.w_recon * self.losses.binary_cross_entropy(
data, output)
elif self.loss_type == 'mse':
loss_rec = self.w_recon * tf.losses.mean_squared_error(
data, output)
else:
raise "invalid loss function... try bce or mse..."
# gaussian loss
loss_gaussian = self.w_gauss * self.losses.labeled_loss(
gaussian, mean, var, y_mean, y_var)
# categorical loss
loss_categorical = self.w_categ * -self.losses.entropy(_logits, prob)
# obtain predictions
predicted_labels = tf.argmax(_logits, axis=1)
# total_loss
loss_total = loss_rec + loss_gaussian + loss_categorical
loss_dic = {
'total': loss_total,
'predicted_labels': predicted_labels,
'reconstruction': loss_rec,
'gaussian': loss_gaussian,
'categorical': loss_categorical
}
return loss_dic
def create_model(self, is_training, inputs, output_size):
"""Model function defining the graph operations.
Args:
is_training: (bool) whether we are in training phase or not
inputs: (dict) contains the inputs of the graph (features, labels...)
this can be `tf.placeholder` or outputs of `tf.data`
output_size: (int) size of the output layer
Returns:
model_spec: (dict) contains the graph operations or nodes needed for training / evaluation
"""
data, _labels = inputs['data'], inputs['labels']
latent_spec = self.network.encoder(data, self.num_classes, is_training)
out_logits, y_mean, y_var, output = self.network.decoder(
latent_spec['gaussian'], latent_spec['categorical'], output_size,
is_training)
latent_spec['output'] = out_logits
latent_spec['y_mean'] = y_mean
latent_spec['y_var'] = y_var
# unlabeled losses
unlabeled_loss_dic = self.unlabeled_loss(data, latent_spec,
output_size, is_training)
loss_total = unlabeled_loss_dic['total']
if is_training:
# use adam for optimization
optimizer = tf.train.AdamOptimizer(self.learning_rate)
# needed for batch normalization layer
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss_total)
# create model specification
model_spec = inputs
model_spec['variable_init_op'] = tf.global_variables_initializer()
# optimizers are only available in training phase
if is_training:
model_spec['train_op'] = train_op
else:
model_spec['output'] = output
model_spec['loss_total'] = loss_total
model_spec['loss_rec_ul'] = unlabeled_loss_dic['reconstruction']
model_spec['loss_gauss_ul'] = unlabeled_loss_dic['gaussian']
model_spec['loss_categ_ul'] = unlabeled_loss_dic['categorical']
model_spec['true_labels'] = _labels
model_spec['predicted'] = unlabeled_loss_dic['predicted_labels']
return model_spec
def evaluate_dataset(self, is_training, num_batches, model_spec):
"""Evaluate the model
Args:
is_training: (bool) whether we are training or not
num_batches: (int) number of batches to train/test
model_spec: (dict) contains the graph operations or nodes needed for evaluation
Returns:
(dic) average of loss functions and metrics for the given number of batches
"""
avg_accuracy = 0.0
avg_nmi = 0.0
avg_loss_cat = 0.0
avg_loss_total = 0.0
avg_loss_rec = 0.0
avg_loss_gauss = 0.0
list_predicted_labels = []
list_true_labels = []
# initialize dataset iteratior
self.sess.run(model_spec['iterator_init'])
if is_training:
train_optimizer = model_spec['train_op']
# training phase
for j in range(num_batches):
_, loss_total, loss_cat_ul, loss_rec_ul, loss_gauss_ul, true_labels, predicted_labels = self.sess.run(
[
train_optimizer, model_spec['loss_total'],
model_spec['loss_categ_ul'], model_spec['loss_rec_ul'],
model_spec['loss_gauss_ul'], model_spec['true_labels'],
model_spec['predicted']
],
feed_dict={
self.network.temperature: self.temperature,
self.learning_rate: self.lr
})
# save values
list_predicted_labels.append(predicted_labels)
list_true_labels.append(true_labels)
avg_loss_rec += loss_rec_ul
avg_loss_gauss += loss_gauss_ul
avg_loss_cat += loss_cat_ul
avg_loss_total += loss_total
else:
# validation phase
for j in range(num_batches):
# run the tensorflow flow graph
loss_rec_ul, loss_gauss_ul, loss_cat_ul, loss_total, true_labels, predicted_labels = self.sess.run(
[
model_spec['loss_rec_ul'], model_spec['loss_gauss_ul'],
model_spec['loss_categ_ul'], model_spec['loss_total'],
model_spec['true_labels'], model_spec['predicted']
],
feed_dict={
self.network.temperature: self.temperature,
self.learning_rate: self.lr
})
# save values
list_predicted_labels.append(predicted_labels)
list_true_labels.append(true_labels)
avg_loss_rec += loss_rec_ul
avg_loss_gauss += loss_gauss_ul
avg_loss_cat += loss_cat_ul
avg_loss_total += loss_total
# average values by the given number of batches
avg_loss_rec /= num_batches
avg_loss_gauss /= num_batches
avg_loss_cat /= num_batches
avg_loss_total /= num_batches
# average accuracy and nmi of all the data
predicted_labels = np.hstack(list_predicted_labels)
true_labels = np.hstack(list_true_labels)
avg_nmi = self.metrics.nmi(predicted_labels, true_labels)
avg_accuracy = self.metrics.cluster_acc(predicted_labels, true_labels)
return {
'loss_rec': avg_loss_rec,
'loss_gauss': avg_loss_gauss,
'loss_cat': avg_loss_cat,
'loss_total': avg_loss_total,
'accuracy': avg_accuracy,
'nmi': avg_nmi
}
def train(self, train_data, train_labels, val_data, val_labels):
"""Train the model
Args:
train_data: (array) corresponding array containing the training data
train_labels: (array) corresponding array containing the labels of the training data
val_data: (array) corresponding array containing the validation data
val_labels: (array) corresponding array containing the labels of the validation data
Returns:
output: (dict) contains the history of train/val loss
"""
train_history_loss, val_history_loss = [], []
train_history_acc, val_history_acc = [], []
train_history_nmi, val_history_nmi = [], []
# create training and validation dataset
train_dataset = self.create_dataset(True, train_data, train_labels,
self.batch_size)
val_dataset = self.create_dataset(False, val_data, val_labels,
self.batch_size_val)
self.output_size = train_data.shape[1]
# create train and validation models
train_model = self.create_model(True, train_dataset, self.output_size)
val_model = self.create_model(False, val_dataset, self.output_size)
# set number of batches
num_train_batches = int(
np.ceil(train_data.shape[0] / (1.0 * self.batch_size)))
num_val_batches = int(
np.ceil(val_data.shape[0] / (1.0 * self.batch_size_val)))
# initialize global variables
self.sess.run(train_model['variable_init_op'])
# training and validation phases
print('Training phase...')
for i in range(self.num_epochs):
# decay learning rate according to decay_epoch parameter
if self.decay_epoch > 0 and (i + 1) % self.decay_epoch == 0:
self.lr = self.lr * self.lr_decay
print('Decaying learning rate: %lf' % self.lr)
# evaluate train and validation datasets
train_loss = self.evaluate_dataset(True, num_train_batches,
train_model)
val_loss = self.evaluate_dataset(False, num_val_batches, val_model)
# get training results for printing
train_loss_rec = train_loss['loss_rec']
train_loss_gauss = train_loss['loss_gauss']
train_loss_cat = train_loss['loss_cat']
train_accuracy = train_loss['accuracy']
train_nmi = train_loss['nmi']
train_total_loss = train_loss['loss_total']
# get validation results for printing
val_loss_rec = val_loss['loss_rec']
val_loss_gauss = val_loss['loss_gauss']
val_loss_cat = val_loss['loss_cat']
val_accuracy = val_loss['accuracy']
val_nmi = val_loss['nmi']
val_total_loss = val_loss['loss_total']
# if verbose then print specific information about training
if self.verbose == 1:
print("(Epoch %d / %d)" % (i + 1, self.num_epochs))
print("Train - REC: %.5lf; Gauss: %.5lf; Cat: %.5lf;" % \
(train_loss_rec, train_loss_gauss, train_loss_cat))
print("Valid - REC: %.5lf; Gauss: %.5lf; Cat: %.5lf;" % \
(val_loss_rec, val_loss_gauss, val_loss_cat))
print("Accuracy=Train: %.5lf; Val: %.5lf NMI=Train: %.5lf; Val: %.5lf Total Loss=Train: %.5lf; Val: %.5lf" % \
(train_accuracy, val_accuracy, train_nmi, val_nmi, train_total_loss, val_total_loss))
else:
print("(Epoch %d / %d) Train Loss: %.5lf; Val Loss: %.5lf Train ACC: %.5lf; Val ACC: %.5lf Train NMI: %.5lf; Val NMI: %.5lf" % \
(i + 1, self.num_epochs, train_total_loss, val_total_loss, train_accuracy, val_accuracy, train_nmi, val_nmi))
# save loss and accuracy of each epoch
train_history_loss.append(train_total_loss)
val_history_loss.append(val_total_loss)
train_history_acc.append(train_accuracy)
val_history_acc.append(val_accuracy)
if self.decay_temperature == 1:
# decay temperature of gumbel-softmax
self.temperature = np.maximum(
self.initial_temperature * np.exp(-self.decay_temp_rate *
(i + 1)),
self.min_temperature)
if self.verbose == 1:
print("Gumbel Temperature: %.5lf" % self.temperature)
return {
'train_history_loss': train_history_loss,
'val_history_loss': val_history_loss,
'train_history_acc': train_history_acc,
'val_history_acc': val_history_acc
}
def test(self, test_data, test_labels, batch_size=-1):
"""Test the model with new data
Args:
test_data: (array) corresponding array containing the testing data
test_labels: (array) corresponding array containing the labels of the testing data
batch_size: (int) batch size used to run the model
Return:
accuracy for the given test data
"""
# if batch_size is not specified then use all data
if batch_size == -1:
batch_size = test_data.shape[0]
# create dataset
test_dataset = self.create_dataset(False, test_data, test_labels,
batch_size)
true_labels = test_dataset['labels']
# perform a forward call on the encoder to obtain predicted labels
latent = self.network.encoder(test_dataset['data'], self.num_classes)
logits = latent['logits']
predicted_labels = tf.argmax(logits, axis=1)
# initialize dataset iterator
self.sess.run(test_dataset['iterator_init'])
# calculate number of batches given batch size
num_batches = int(np.ceil(test_data.shape[0] / (1.0 * batch_size)))
# evaluate the model
list_predicted_labels = []
list_true_labels = []
for j in range(num_batches):
_predicted_labels, _true_labels = self.sess.run(
[predicted_labels, true_labels],
feed_dict={
self.network.temperature: self.temperature,
self.learning_rate: self.lr
})
# save values
list_predicted_labels.append(_predicted_labels)
list_true_labels.append(_true_labels)
# average accuracy and nmi of all the data
predicted_labels = np.hstack(list_predicted_labels)
true_labels = np.hstack(list_true_labels)
avg_nmi = self.metrics.nmi(predicted_labels, true_labels)
avg_accuracy = self.metrics.cluster_acc(predicted_labels, true_labels)
return avg_accuracy, avg_nmi
def latent_features(self, data, batch_size=-1):
"""Obtain latent features learnt by the model
Args:
data: (array) corresponding array containing the data
batch_size: (int) size of each batch to consider from the data
Returns:
features: (array) array containing the features from the data
"""
# if batch_size is not specified then use all data
if batch_size == -1:
batch_size = data.shape[0]
# create dataset
dataset = self.create_dataset(False, data, None, batch_size)
# we will use only the encoder network
latent = self.network.encoder(dataset['data'], self.num_classes)
encoder = latent['features']
# obtain the features from the input data
self.sess.run(dataset['iterator_init'])
num_batches = data.shape[0] // batch_size
features = np.zeros((data.shape[0], self.gaussian_size))
for j in range(num_batches):
features[j * batch_size:j * batch_size +
batch_size] = self.sess.run(encoder,
feed_dict={
self.network.temperature:
self.temperature,
self.learning_rate:
self.lr
})
return features
def reconstruct_data(self, data, batch_size=-1):
"""Reconstruct Data
Args:
data: (array) corresponding array containing the data
batch_size: (int) size of each batch to consider from the data
Returns:
reconstructed: (array) array containing the reconstructed data
"""
# if batch_size is not specified then use all data
if batch_size == -1:
batch_size = data.shape[0]
# create dataset
dataset = self.create_dataset(False, data, None, batch_size)
# reuse model used in training
model_spec = self.create_model(False, dataset, data.shape[1])
# obtain the reconstructed data
self.sess.run(model_spec['iterator_init'])
num_batches = data.shape[0] // batch_size
reconstructed = np.zeros(data.shape)
pos = 0
for j in range(num_batches):
reconstructed[pos:pos + batch_size] = self.sess.run(
model_spec['output'],
feed_dict={
self.network.temperature: self.temperature,
self.learning_rate: self.lr
})
pos += batch_size
return reconstructed
def plot_latent_space(self, data, labels, save=False):
"""Plot the latent space learnt by the model
Args:
data: (array) corresponding array containing the data
labels: (array) corresponding array containing the labels
save: (bool) whether to save the latent space plot
Returns:
fig: (figure) plot of the latent space
"""
# obtain the latent features
features = self.latent_features(data)
# plot only the first 2 dimensions
fig = plt.figure(figsize=(8, 6))
plt.scatter(features[:, 0],
features[:, 1],
c=labels,
marker='o',
edgecolor='none',
cmap=plt.cm.get_cmap('jet', 10),
s=10)
plt.colorbar()
if (save):
fig.savefig('latent_space.png')
return fig
def generate_data(self, num_elements=1, category=0):
"""Generate data for a specified category
Args:
num_elements: (int) number of elements to generate
category: (int) category from which we will generate data
Returns:
generated data according to num_elements
"""
indices = (np.ones(num_elements) * category).astype(int).tolist()
# category is specified with a one-hot array
categorical = tf.one_hot(indices, self.num_classes)
# infer the gaussian distribution according to the category
mean, var = self.network.gaussian_from_categorical(categorical)
# gaussian random sample by using the mean and variance
gaussian = tf.random_normal(tf.shape(mean), mean, tf.sqrt(var))
# generate new samples with the given gaussian
_, out = self.network.output_from_gaussian(gaussian, self.output_size)
return self.sess.run(out,
feed_dict={
self.network.temperature: self.temperature,
self.learning_rate: self.lr
})
def random_generation(self, num_elements=1):
"""Random generation for each category
Args:
num_elements: (int) number of elements to generate
Returns:
generated data according to num_elements
"""
# categories for each element
arr = np.array([])
for i in range(self.num_classes):
arr = np.hstack([arr, np.ones(num_elements) * i])
indices = arr.astype(int).tolist()
categorical = tf.one_hot(indices, self.num_classes)
# infer the gaussian distribution according to the category
mean, var = self.network.gaussian_from_categorical(categorical)
# gaussian random sample by using the mean and variance
gaussian = tf.random_normal(tf.shape(mean), mean, tf.sqrt(var))
# generate new samples with the given gaussian
_, out = self.network.output_from_gaussian(gaussian, self.output_size)
return self.sess.run(out,
feed_dict={
self.network.temperature: self.temperature,
self.learning_rate: self.lr
})