def __main__():
from models.semi_supervised.deep_generative_models.models.auxiliary_dgm import AuxiliaryDeepGenerativeModel
from utils.files_destinations import home_path, destination_folder, data_folder, results_folder
dataset_name = "mnist_ssl_conv"
n_epochs = 1000
lr = 1e-4
h_dims = [64, 32]
h_dims_classifier = [128]
betas = (0.9, 0.999)
z_dims = [20]
a_dims = [20]
num_elements = 2
input_shape = [1, 28, 28]
vae_flavour = "o-sylvester"
number_of_flows = 4
batch_size = 64
warmup = 3
load_vae = False
resume = False
labels_per_class = -1
early_stopping = 100
use_conv = True
auxiliary = True
mc = 1
iw = 1
l1 = 0.
l2 = 0.
dgm = AuxiliaryDeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
a_dim=a_dims[0],
num_elements=num_elements,
use_conv=use_conv)
dgm.set_configs(home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
dgm.load_example_dataset(dataset="mnist",
batch_size=batch_size,
labels_per_class=labels_per_class)
dgm.define_configurations(early_stopping=early_stopping,
warmup=warmup,
flavour=vae_flavour)
dgm.cuda()
dgm.set_conv_adgm_layers(is_hebb_layers=False, input_shape=input_shape)
# import the M1 in the M1+M2 model (Kingma et al, 2014)
if load_vae:
print("Importing the model: ", dgm.model_file_name)
if use_conv:
dgm.import_cvae()
else:
dgm.load_ae(load_history=False)
#dgm.set_dgm_layers_pretrained()
if resume:
print("Resuming training")
dgm.load_model()
dgm.cuda()
# dgm.vae.generate_random(False, batch_size, z1_size, [1, 28, 28])
dgm.run(n_epochs, auxiliary, mc, iw, lambda1=l1, lambda2=l2)
def __main_gm__():
meta_df = None
unlabelled_meta_df = None
from models.semi_supervised.deep_generative_models.models.auxiliary_dgm import AuxiliaryDeepGenerativeModel
from utils.files_destinations import home_path, destination_folder, data_folder, results_folder, plots_folder_path
examples_list = ["mnist", "cifar10", "cifar100"]
if example not in examples_list and not import_local_file and import_geo:
g = GeoParser(home_path=home_path,
geo_ids=geo_ids,
unlabelled_geo_ids=unlabelled_geo_ids,
bad_geo_ids=bad_geo_ids)
g.get_geo(load_from_disk=load_from_disk)
meta_df = g.merge_datasets(load_from_disk=load_merge, labelled=True)
unlabelled_meta_df = g.merge_datasets(load_from_disk=load_merge,
labelled=False)
if translate is "y":
for geo_id in geo_ids:
g.translate_indices_df(geo_id, labelled=True)
for geo_id in unlabelled_geo_ids:
g.translate_indices_df(geo_id, labelled=False)
meta_df, unlabelled_meta_df = adapt_datasets(meta_df,
unlabelled_meta_df)
is_example = False
from utils.list_parameters import z_dims, h_dims, num_elements
dgm = AuxiliaryDeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
a_dim=a_dim,
num_elements=num_elements,
use_conv=use_conv)
dgm.set_configs(home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=initial_lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
if meta_df is not None:
dgm.import_dataframe(meta_df, batch_size, labelled=True)
if has_unlabelled:
dgm.import_dataframe(unlabelled_meta_df,
batch_size,
labelled=False)
else:
dgm.import_dataframe(meta_df, batch_size, labelled=False)
elif example in examples_list:
dgm.load_example_dataset(dataset=example,
batch_size=batch_size,
labels_per_class=400)
is_example = True
print("PCA saved at: ", plots_folder_path)
#meta_df = pd.DataFrame(dgm.train_ds)
#ordination2d(meta_df, epoch="pre", dataset_name=dataset_name, ord_type="pca",
# images_folder_path=plots_folder_path, info=str(geo_ids)+str(unlabelled_geo_ids)+str(bad_geo_ids))
dgm.define_configurations(early_stopping=early_stopping,
warmup=warmup,
flavour=vae_flavour)
dgm.set_data(labels_per_class=labels_per_class, is_example=is_example)
dgm.cuda()
if use_conv:
dgm.set_conv_adgm_layers(is_hebb_layers=False, input_shape=img_shape)
else:
dgm.set_adgm_layers(h_dims=h_dims_classifier)
# import the M1 in the M1+M2 model (Kingma et al, 2014)
if load_vae:
print("Importing the model: ", dgm.model_file_name)
if use_conv:
dgm.import_cvae()
else:
dgm.load_ae(load_history=False)
#dgm.set_dgm_layers_pretrained()
if resume:
print("Resuming training")
dgm.load_model()
dgm.cuda()
# dgm.vae.generate_random(False, batch_size, z1_size, [1, 28, 28])
dgm.run(n_epochs, auxiliary, mc, iw, lambda1=l1, lambda2=l2)
def __main__():
from data_preparation.GeoParser import GeoParser
from dimension_reduction.ordination import ordination2d
from sklearn.decomposition import PCA
from IPython.display import Image
import pandas as pd
import numpy as np
from models.semi_supervised.deep_generative_models.models.auxiliary_dgm import AuxiliaryDeepGenerativeModel
from utils.utils import dict_of_int_highest_elements, plot_evaluation
# files_destinations
home_path = "/home/simon/"
destination_folder = "annleukemia"
data_folder = "data"
results_folder = "results"
meta_destination_folder = "pandas_meta_df"
plots_folder_path = "/".join(
[home_path, destination_folder, results_folder, "plots/"])
#dataset_name = "gse33000_and_GSE24335_GSE44768_GSE44771_GSE44770"
dataset_name = "dessins"
activation = "relu"
#nrep = 3
betas = (0.9, 0.999)
vae_flavour = "o-sylvester"
early_stopping = 200
labels_per_class = 10000
n_epochs = 1000
warmup = 100
gt_input = 10000
# if ladder is yes builds a ladder vae. Do not combine with auxiliary (yet; might be possible and relatively
# not too hard to implement, but might be overkill. Might be interesting too)
translate = "n"
# Types of deep generative model
# Convolution neural network (convolutional VAE and convolutional classifier)
use_conv_ae = False #Not applicable if not sequence (images, videos, sentences, DNA...)
use_convnet = True
# Ladder VAE (L-VAE)
ladder = False
# Auxiliary Variational Auto-Encoder (A-VAE)
auxiliary = True
# Load pre-computed vae (unsupervised learning)
load_vae = False
lr = 1e-4
l1 = 0.
l2 = 0.
batch_size = 128
mc = 1 # seems to be a problem when mc > 1 for display only, results seem good
iw = 1 # seems to be a problem when iw > 1 for display only, results seem good
# Neurons layers
a_dim = 50
h_dims_classifier = [256]
h_dims = [256, 128]
z_dims = [50]
# number of flows
number_of_flows = 5
num_elements = 2
# Files destinations
load_from_disk = True
load_merge = False
home_path = "/home/simon/"
destination_folder = "annleukemia"
data_folder = "data"
results_folder = "results"
meta_destination_folder = "pandas_meta_df"
plots_folder_path = "/".join(
[home_path, destination_folder, results_folder, "plots/"])
dgm = AuxiliaryDeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
a_dim=a_dim,
num_elements=num_elements,
is_hebb_layers=True,
gt_input=gt_input)
dgm.set_configs(home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers",
is_unlabelled=True)
dgm.load_local_dataset(
root_train="/home/simon/annleukemia/data/kaggle_dessins/train",
root_valid="/home/simon/annleukemia/data/kaggle_dessins/valid",
root_test="/home/simon/annleukemia/data/kaggle_dessins/test",
n_classes=31,
batch_size=batch_size,
labels_per_class=labels_per_class,
extra_class=True,
unlabelled_train_ds=True,
normalize=False,
mu=0.5,
var=0.5)
is_example = False
# GET ordination from this!
train = np.vstack([x[0].data.numpy() for x in dgm.x_train])
# unlabelled_train = np.vstack([x[0].data.numpy() for x in dgm.unlabelled_x_train])
targets = np.vstack([x[1].data.numpy() for x in dgm.x_train])
labels = [x.tolist().index(1) for x in targets]
dgm.define_configurations(early_stopping=early_stopping,
warmup=warmup,
flavour=vae_flavour)
dgm.set_data(labels_per_class=labels_per_class,
is_example=True,
extra_class=True)
planes_classifier = [1, 16, 32, 64, 128, 256, 512]
classifier_kernels = [3, 3, 3, 3, 3, 3, 3]
classifier_pooling_layers = [
True, True, True, True, True, True, False, False
]
dgm.set_adgm_layers(h_dims=h_dims_classifier,
input_shape=[1, 100, 100],
use_conv_classifier=use_convnet,
planes_classifier=planes_classifier,
classifier_kernels=classifier_kernels,
classifier_pooling_layers=classifier_pooling_layers)
#dgm.set_dgm_layers()
# import the M1 in the M1+M2 model (Kingma et al, 2014). Not sure if it still works...
if load_vae:
print("Importing the model: ", dgm.model_file_name)
if use_conv_ae:
dgm.import_cvae()
else:
dgm.load_model()
# dgm.set_dgm_layers_pretrained()
dgm.cuda()
# dgm.vae.generate_random(False, batch_size, z1_size, [1, 28, 28])
dgm.run(n_epochs,
auxiliary,
mc,
iw,
lambda1=l1,
lambda2=l2,
verbose=1,
show_progress=10,
show_pca_train=10,
show_lda_train=10,
show_pca_generated=10,
clip_grad=1e-5,
is_input_pruning=False,
start_pruning=10000,
show_lda_generated=10,
warmup_n=-1,
alpha_rate=1000)
def __main__():
train_ds = None
valid_ds = None
meta_df = None
unlabelled_meta_df = None
from torchvision import transforms, datasets
from models.discriminative.artificial_neural_networks.mlp_keras import mlp_keras
from graveyard.HebbNet import HebbNet
from models.semi_supervised.deep_generative_models.models.auxiliary_dgm import AuxiliaryDeepGenerativeModel
from models.semi_supervised.deep_generative_models.models.ladder_dgm import LadderDeepGenerativeModel
from models.semi_supervised.deep_generative_models.models.dgm import DeepGenerativeModel
from models.generative.autoencoders.vae.vae import VariationalAutoencoder
from models.generative.autoencoders.vae.ladder_vae import LadderVariationalAutoencoder
from models.generative.autoencoders.vae.sylvester_vae import SylvesterVAE
from utils.utils import dict_of_int_highest_elements, plot_evaluation
from utils.files_destinations import home_path, destination_folder, data_folder, meta_destination_folder, \
results_folder, plots_folder_path
examples_list = ["mnist", "cifar10", "cifar100"]
if example not in examples_list and not import_local_file and import_geo:
g = GeoParser(home_path=home_path,
geo_ids=geo_ids,
unlabelled_geo_ids=unlabelled_geo_ids,
bad_geo_ids=bad_geo_ids)
g.get_geo(load_from_disk=load_from_disk)
meta_df = g.merge_datasets(load_from_disk=load_merge, labelled=True)
unlabelled_meta_df = g.merge_datasets(load_from_disk=load_merge,
labelled=False)
if translate is "y":
for geo_id in geo_ids:
g.translate_indices_df(geo_id, labelled=True)
for geo_id in unlabelled_geo_ids:
g.translate_indices_df(geo_id, labelled=False)
meta_df, unlabelled_meta_df = adapt_datasets(meta_df,
unlabelled_meta_df)
elif import_local_file:
train_arrays = np.load(local_folder + train_images_fname,
encoding="latin1")
train_dataset = np.vstack(train_arrays[:, 1])
train_labels = genfromtxt(local_folder + train_labels_fname,
delimiter=",",
dtype=str,
skip_header=True)[:, 1]
test_dataset = np.vstack(
np.load(local_folder + "test_images.npy", encoding="latin1")[:, 1])
meta_df = pd.DataFrame(train_dataset, columns=train_labels)
img_shape = [1, 100, 100]
elif import_dessins:
train_labels = genfromtxt("data/kaggle_dessins/" + train_labels_fname,
delimiter=",",
dtype=str,
skip_header=True)[:, 1]
data_transform = transforms.Compose([
transforms.RandomSizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
dessins_train_data = datasets.ImageFolder(
root='data/kaggle_dessins/train/', transform=data_transform)
dessins_valid_data = datasets.ImageFolder(
root='data/kaggle_dessins/valid/', transform=data_transform)
train_ds = torch.utils.data.DataLoader(dessins_train_data,
batch_size=batch_size,
shuffle=True,
num_workers=0)
valid_ds = torch.utils.data.DataLoader(dessins_valid_data,
batch_size=batch_size,
shuffle=True,
num_workers=0)
if ords == "pca" or ords == "both" or ords == "b" or ords == "p":
print("PCA saved at: ", plots_folder_path)
ordination2d(meta_df,
epoch="pre",
dataset_name=dataset_name,
ord_type="pca",
images_folder_path=plots_folder_path,
info=str(geo_ids) + str(unlabelled_geo_ids) +
str(bad_geo_ids))
if ords is "tsne" or ords is "both" or ords is "b" or ords is "t":
ordination2d(g.meta_df,
dataset_name,
"tsne",
dataset_name,
plots_folder_path + "tsne/",
info=str(geo_ids) + str(unlabelled_geo_ids) +
str(bad_geo_ids))
if "mlp" in nets or "MLP" in nets or all_automatic:
mlp = mlp_keras()
mlp.set_configs(home_path,
results_folder,
data_folder,
destination_folder,
meta_destination_folder=meta_destination_folder)
mlp.import_dataframe(meta_df, batch_size)
mlp.init_keras_paramters()
mlp.set_data()
if nvidia:
mlp.cuda()
for r in range(nrep):
if nrep is 1:
mlp.load_model()
print(str(r) + "/" + str(nrep))
mlp.train(batch_size=batch_size, n_epochs=n_epochs, nrep=nrep)
if evaluate_individually:
mlp.evaluate_individual()
top_loss_diff = dict_of_int_highest_elements(
mlp.loss_diff, 20) # highest are the most affected
top_index = top_loss_diff.keys()
mlp.evaluate_individual_arrays(top_index)
file_name = plots_folder_path + "_".join(
[mlp.dataset_name, mlp.init, mlp.batch_size, mlp.nrep])
plot_evaluation(mlp.log_loss_diff_indiv,
file_name + "_accuracy_diff.png", 20)
if "vae" in nets or "VAE" in nets or all_automatic:
from utils.parameters import h_dims, betas, num_elements
is_example = True
if ladder:
from utils.parameters import z_dims
vae = LadderVariationalAutoencoder(vae_flavour,
z_dims=z_dims,
h_dims=h_dims,
n_flows=number_of_flows,
num_elements=num_elements,
auxiliary=False)
z_dim = z_dims[-1]
elif vae_flavour in ["o-sylvester", "h-sylvester", "t-sylvester"]:
print("vae_flavour", vae_flavour)
from utils.parameters import z_dim_last
vae = SylvesterVAE(vae_flavour,
z_dims=[z_dim_last],
h_dims=h_dims,
n_flows=number_of_flows,
num_elements=num_elements,
auxiliary=False)
else:
print("vae_flavour", vae_flavour)
from utils.parameters import z_dim_last
vae = VariationalAutoencoder(vae_flavour,
h_dims=h_dims,
n_flows=number_of_flows,
auxiliary=False)
vae.set_configs(home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=initial_lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
if meta_df is not None:
vae.import_dataframe(g.meta_df, batch_size)
elif example in examples_list:
print("LOADING EXAMPLE")
vae.load_example_dataset(dataset=example, batch_size=batch_size)
is_example = True
print("PCA saved at: ", plots_folder_path)
meta_df = vae.train_ds
ordination2d(meta_df,
epoch="pre",
dataset_name=dataset_name,
ord_type="pca",
images_folder_path=plots_folder_path,
info=str(geo_ids) + str(unlabelled_geo_ids) +
str(bad_geo_ids))
vae.define_configurations(vae_flavour,
early_stopping=1000,
warmup=warmup,
ladder=ladder,
z_dim=z_dim_last)
vae.set_data(is_example=is_example)
if ladder:
print("Setting ladder layers")
vae.set_lvae_layers()
else:
vae.set_vae_layers()
if load_vae:
vae.load_model()
vae.run(epochs=n_epochs)
if "dgm" in nets or "DGM" in nets or all_automatic:
is_example = False
from utils.list_parameters import z_dims, h_dims, num_elements, a_dims
if auxiliary:
dgm = AuxiliaryDeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
a_dim=a_dim,
num_elements=num_elements,
use_conv=use_conv)
dgm.set_configs(home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=initial_lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
elif ladder:
dgm = LadderDeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
auxiliary=False)
dgm.set_configs(home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=initial_lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
else:
print(vae_flavour)
dgm = DeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
a_dim=None,
auxiliary=False,
num_elements=num_elements)
dgm.set_configs(home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=initial_lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
if meta_df is not None:
dgm.import_dataframe(meta_df, batch_size, labelled=True)
if has_unlabelled:
dgm.import_dataframe(unlabelled_meta_df,
batch_size,
labelled=False)
else:
dgm.import_dataframe(meta_df, batch_size, labelled=False)
elif example in examples_list:
dgm.load_example_dataset(dataset=example,
batch_size=batch_size,
labels_per_class=400)
is_example = True
print("PCA saved at: ", plots_folder_path)
#meta_df = pd.DataFrame(dgm.train_ds)
#ordination2d(meta_df, epoch="pre", dataset_name=dataset_name, ord_type="pca",
# images_folder_path=plots_folder_path, info=str(geo_ids)+str(unlabelled_geo_ids)+str(bad_geo_ids))
elif import_dessins:
import os
dgm.batch_size = batch_size
dgm.input_shape = [1, 100, 100]
dgm.input_size = 10000
dgm.labels_set = os.listdir('data/kaggle_dessins/train/')
dgm.num_classes = len(dgm.labels_set)
dgm.make_loaders(train_ds=train_ds,
valid_ds=valid_ds,
test_ds=None,
labels_per_class=labels_per_class,
unlabelled_train_ds=None,
unlabelled_samples=True)
dgm.define_configurations(early_stopping=early_stopping,
warmup=warmup,
flavour=vae_flavour)
if import_local_file:
dgm.labels = train_labels
dgm.labels_set = list(set(train_labels))
if not import_dessins:
dgm.set_data(labels_per_class=labels_per_class,
is_example=is_example)
dgm.cuda()
if auxiliary:
if use_conv:
dgm.set_conv_adgm_layers(is_hebb_layers=False,
input_shape=img_shape)
else:
dgm.set_adgm_layers(h_dims=h_dims_classifier)
elif ladder:
dgm.set_ldgm_layers()
else:
if use_conv:
dgm.set_conv_dgm_layers(input_shape=img_shape)
else:
dgm.set_dgm_layers()
# import the M1 in the M1+M2 model (Kingma et al, 2014)
if load_vae:
print("Importing the model: ", dgm.model_file_name)
if use_conv:
dgm.import_cvae()
else:
dgm.load_ae(load_history=False)
#dgm.set_dgm_layers_pretrained()
if resume:
print("Resuming training")
dgm.load_model()
dgm.cuda()
# dgm.vae.generate_random(False, batch_size, z1_size, [1, 28, 28])
dgm.run(n_epochs, auxiliary, mc, iw, lambda1=l1, lambda2=l2)
if "hnet" in nets or all_automatic:
net = HebbNet()
if meta_df is not None:
net.import_dataframe(g.meta_df, batch_size)
elif example in examples_list:
net.load_example_dataset(dataset=example,
batch_size=batch_size,
labels_per_class=400)
is_example = True
images_folder_path = plots_folder_path + "pca/"
ordination2d(net.meta_df,
"pca",
images_folder_path,
dataset_name=dataset_name)
else:
print("Example not available")
exit()
net.set_data()
net.init_parameters(batch_size=batch_size,
input_size=net.input_size,
num_classes=net.num_classes,
dropout=0.5,
gt=[0, 0, 0, 0, 0, 0],
gt_input=-10000,
hyper_count=5,
clampmax=100,
n_channels=n_channels)
net.set_layers()
if torch.cuda.is_available():
net.cuda()
net.run(n_epochs, hebb_round=1, display_rate=1)
def __main_gm__():
train_ds = None
valid_ds = None
meta_df = None
unlabelled_meta_df = None
from torchvision import transforms, datasets
from models.discriminative.artificial_neural_networks.hebbian_network.HebbNet import HebbNet
from models.semi_supervised.deep_generative_models.models.auxiliary_dgm import AuxiliaryDeepGenerativeModel
from models.semi_supervised.deep_generative_models.models.ladder_dgm import LadderDeepGenerativeModel
from models.semi_supervised.deep_generative_models.models.dgm import DeepGenerativeModel
from files_destinations import home_path, destination_folder, data_folder, meta_destination_folder, \
results_folder, plots_folder_path
examples_list = ["mnist", "cifar10", "cifar100"]
if example not in examples_list and not import_local_file and import_geo:
g = GeoParser(home_path=home_path,
geo_ids=geo_ids,
unlabelled_geo_ids=unlabelled_geo_ids,
bad_geo_ids=bad_geo_ids)
g.get_geo(load_from_disk=load_from_disk)
meta_df = g.merge_datasets(load_from_disk=load_merge, labelled=True)
unlabelled_meta_df = g.merge_datasets(load_from_disk=load_merge,
labelled=False)
if translate is "y":
for geo_id in geo_ids:
g.translate_indices_df(geo_id, labelled=True)
for geo_id in unlabelled_geo_ids:
g.translate_indices_df(geo_id, labelled=False)
meta_df, unlabelled_meta_df = adapt_datasets(meta_df,
unlabelled_meta_df)
if ords == "pca" or ords == "both" or ords == "b" or ords == "p":
print("PCA saved at: ", plots_folder_path)
ordination2d(meta_df,
epoch="pre",
dataset_name=dataset_name,
ord_type="pca",
images_folder_path=plots_folder_path,
info=str(geo_ids) + str(unlabelled_geo_ids) +
str(bad_geo_ids))
if ords is "tsne" or ords is "both" or ords is "b" or ords is "t":
ordination2d(g.meta_df,
dataset_name,
"tsne",
dataset_name,
plots_folder_path + "tsne/",
info=str(geo_ids) + str(unlabelled_geo_ids) +
str(bad_geo_ids))
if "dgm" in nets or "DGM" in nets or all_automatic:
is_example = False
from list_parameters import z_dims, h_dims, num_elements, a_dims
if auxiliary:
dgm = AuxiliaryDeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
a_dim=a_dim,
num_elements=num_elements,
use_conv=use_conv)
dgm.set_configs(home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=initial_lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
elif ladder:
dgm = LadderDeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
auxiliary=False)
dgm.set_configs(home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=initial_lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
else:
print(vae_flavour)
dgm = DeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
a_dim=None,
auxiliary=False,
num_elements=num_elements)
dgm.set_configs(home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=initial_lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
if meta_df is not None:
dgm.import_dataframe(meta_df, batch_size, labelled=True)
if has_unlabelled:
dgm.import_dataframe(unlabelled_meta_df,
batch_size,
labelled=False)
else:
dgm.import_dataframe(meta_df, batch_size, labelled=False)
elif example in examples_list:
dgm.load_example_dataset(dataset=example,
batch_size=batch_size,
labels_per_class=400)
is_example = True
print("PCA saved at: ", plots_folder_path)
#meta_df = pd.DataFrame(dgm.train_ds)
#ordination2d(meta_df, epoch="pre", dataset_name=dataset_name, ord_type="pca",
# images_folder_path=plots_folder_path, info=str(geo_ids)+str(unlabelled_geo_ids)+str(bad_geo_ids))
elif import_dessins:
import os
dgm.batch_size = batch_size
dgm.input_shape = [1, 100, 100]
dgm.input_size = 10000
dgm.labels_set = os.listdir('data/kaggle_dessins/train/')
dgm.num_classes = len(dgm.labels_set)
dgm.make_loaders(train_ds=train_ds,
valid_ds=valid_ds,
test_ds=None,
labels_per_class=labels_per_class,
unlabelled_train_ds=None,
unlabelled_samples=True)
dgm.define_configurations(early_stopping=early_stopping,
warmup=warmup,
flavour=vae_flavour)
if import_local_file:
dgm.labels = train_labels
dgm.labels_set = list(set(train_labels))
if not import_dessins:
dgm.set_data(labels_per_class=labels_per_class,
is_example=is_example)
dgm.cuda()
if auxiliary:
if use_conv:
dgm.set_conv_adgm_layers(is_hebb_layers=False,
input_shape=img_shape)
else:
dgm.set_adgm_layers(h_dims=h_dims_classifier)
elif ladder:
dgm.set_ldgm_layers()
else:
if use_conv:
dgm.set_conv_dgm_layers(input_shape=img_shape)
else:
dgm.set_dgm_layers()
# import the M1 in the M1+M2 model (Kingma et al, 2014)
if load_vae:
print("Importing the model: ", dgm.model_file_name)
if use_conv:
dgm.import_cvae()
else:
dgm.load_ae(load_history=False)
#dgm.set_dgm_layers_pretrained()
if resume:
print("Resuming training")
dgm.load_model()
dgm.cuda()
# dgm.vae.generate_random(False, batch_size, z1_size, [1, 28, 28])
dgm.run(n_epochs, auxiliary, mc, iw, lambda1=l1, lambda2=l2)
if "hnet" in nets or all_automatic:
net = HebbNet()
if meta_df is not None:
net.import_dataframe(g.meta_df, batch_size)
elif example in examples_list:
net.load_example_dataset(dataset=example,
batch_size=batch_size,
labels_per_class=400)
is_example = True
images_folder_path = plots_folder_path + "pca/"
ordination2d(net.meta_df,
"pca",
images_folder_path,
dataset_name=dataset_name)
else:
print("Example not available")
exit()
net.set_data()
net.init_parameters(batch_size=batch_size,
input_size=net.input_size,
num_classes=net.num_classes,
dropout=0.5,
gt=[0, 0, 0, 0, 0, 0],
gt_input=-10000,
hyper_count=5,
clampmax=100,
n_channels=n_channels)
net.set_layers()
if torch.cuda.is_available():
net.cuda()
net.run(n_epochs, hebb_round=1, display_rate=1)
def __main__():
geo_ids = ["GSE33000"]
unlabelled_geo_ids = ["GSE33000"]
bad_geo_ids = []
home_path = "/home/simon/"
destination_folder = "annleukemia"
data_folder = "data"
results_folder = "results"
dataset_name = "mnist_ssl"
h_dims = [512, 256]
h_dims_classifier = [128]
betas = (0.9, 0.999)
z_dims = [100]
a_dim = [100]
num_elements = 3
batch_size = 64 # if smaller not working... should work though...
number_of_flows = 10
input_shape = [1, 35371]
# Total number of inputs in microarray. Each microarray might have a different number,
# corresponding in part often, but different microarrays might be difficult to put
# together if they don't sufficiently overlap (though not impossible... )
warmup = 0
use_conv = False
vae_flavour = "o-sylvester"
labels_per_class = -1
early_stopping = 100
has_unlabelled = False
load_vae = False
resume = False # not working... TODO to be corrected, would be useful
n_epochs = 10000
auxiliary = True
ladder = False
mc = 1
iw = 1
l1 = 1e-6
l2 = 1e-6
lr = 1e-5
load_from_disk = True
translate = False
load_merge = False
num_classes = 3
from models.semi_supervised.deep_generative_models.models.auxiliary_dgm import AuxiliaryDeepGenerativeModel
from models.semi_supervised.deep_generative_models.models.ladder_dgm import LadderDeepGenerativeModel
from models.semi_supervised.deep_generative_models.models.dgm import DeepGenerativeModel
g = GeoParser(home_path=home_path,
geo_ids=geo_ids,
unlabelled_geo_ids=unlabelled_geo_ids,
bad_geo_ids=bad_geo_ids)
g.get_geo(load_from_disk=load_from_disk, automatic_attribute_list=True)
meta_df = g.merge_datasets(load_from_disk=load_merge, labelled=True)
unlabelled_meta_df = g.merge_datasets(load_from_disk=load_merge,
labelled=False)
if translate is "y":
for geo_id in geo_ids:
g.translate_indices_df(geo_id, labelled=True)
for geo_id in unlabelled_geo_ids:
g.translate_indices_df(geo_id, labelled=False)
meta_df, unlabelled_meta_df = adapt_datasets(meta_df, unlabelled_meta_df)
is_example = False
extra_class = True
if auxiliary:
dgm = AuxiliaryDeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
a_dim=a_dim,
num_elements=num_elements,
use_conv=use_conv,
labels_per_class=labels_per_class)
dgm.set_configs(num_classes=num_classes,
extra_class=extra_class,
home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
elif ladder:
dgm = LadderDeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
auxiliary=False,
labels_per_class=labels_per_class)
dgm.set_configs(extra_class=extra_class,
home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
else:
print(vae_flavour)
dgm = DeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
a_dim=None,
auxiliary=False,
num_elements=num_elements,
labels_per_class=labels_per_class)
dgm.set_configs(extra_class=extra_class,
home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
if auxiliary:
if use_conv:
dgm.set_conv_adgm_layers(input_shape=input_shape)
else:
dgm.set_adgm_layers(h_dims=h_dims_classifier,
input_shape=input_shape)
elif ladder:
dgm.set_ldgm_layers()
else:
if use_conv:
dgm.set_conv_dgm_layers(input_shape=input_shape)
else:
dgm.set_dgm_layers(input_shape=input_shape)
# import the M1 in the M1+M2 model (Kingma et al, 2014)
if load_vae:
print("Importing the model: ", dgm.model_file_name)
if use_conv:
dgm.import_cvae()
else:
dgm.load_ae(load_history=False)
#dgm.set_dgm_layers_pretrained()
if resume:
print("Resuming training")
dgm.load_model()
dgm.define_configurations(early_stopping=early_stopping,
warmup=warmup,
flavour=vae_flavour)
if meta_df is not None:
dgm.import_dataframe(meta_df, batch_size, labelled=True)
if has_unlabelled:
dgm.import_dataframe(unlabelled_meta_df,
batch_size,
labelled=False)
else:
dgm.import_dataframe(meta_df, batch_size, labelled=False)
dgm.set_data(labels_per_class,
ratio_training=0.8,
ratio_valid=0.1,
is_example=is_example,
is_split=True,
ignore_training_inputs=0,
is_custom_data=False)
dgm.cuda()
import os
os.chdir(home_path + "/" + destination_folder)
dgm.run(n_epochs,
auxiliary,
mc,
iw,
lambda1=l1,
lambda2=l2,
generate_extra_class=1000)
def __main__():
import os
# os.chdir("..") # To return at the root of the project
from models.semi_supervised.deep_generative_models.models.auxiliary_dgm import AuxiliaryDeepGenerativeModel
home_path = "/home/simon/"
destination_folder = "annleukemia"
data_folder = "data"
results_folder = "results"
dataset_name = "mnist_ssl"
h_dims = [300, 300]
h_dims_classifier = [128]
betas = (0.9, 0.999)
z_dims = [10]
a_dims = [10]
num_elements = 2
batch_size = 128
number_of_flows = 10
input_shape = [1, 28, 28]
lr = 1e-4
warmup = 10
meta_df = None
unlabelled_meta_df = None
use_conv = False
# vae_flavour = "o-sylvester" set in set_vae_flavour.py
from set_vae_flavour import vae_flavour
labels_per_class = -1
early_stopping = 200
has_unlabelled = False
load_vae = False
resume = False # not working... TODO to be corrected, would be useful
n_epochs = 1000
auxiliary = True
classif_weight = 1.0
repetitions = 10
clip_grad = 1e-4
mc = 1
iw = 1
l1 = 0
l2 = 0
num_classes = 10
dgm = AuxiliaryDeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
a_dim=a_dims[0],
num_elements=num_elements,
use_conv=use_conv)
dgm.set_configs(home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
if meta_df is not None:
dgm.import_dataframe(meta_df, batch_size, labelled=True)
if has_unlabelled:
dgm.import_dataframe(unlabelled_meta_df,
batch_size,
labelled=False)
else:
dgm.import_dataframe(meta_df, batch_size, labelled=False)
dgm.load_example_dataset(dataset="mnist",
batch_size=batch_size,
labels_per_class=labels_per_class,
extra_class=True)
dgm.define_configurations(early_stopping=early_stopping,
warmup=warmup,
flavour=vae_flavour,
model_name="mnist_vae",
z_dim=z_dims[-1])
dgm.cuda()
if use_conv:
dgm.set_conv_adgm_layers(is_hebb_layers=False, input_shape=input_shape)
else:
dgm.set_adgm_layers(h_dims=h_dims_classifier,
input_shape=input_shape,
num_classes=num_classes)
# import the M1 in the M1+M2 model (Kingma et al, 2014)
if load_vae:
print("Importing the model: ", dgm.model_file_name)
if use_conv:
dgm.import_cvae()
else:
dgm.load_ae(load_history=False)
#dgm.set_dgm_layers_pretrained()
if resume:
print("Resuming training")
dgm.load_model()
dgm.cuda()
log_likehihoods = dgm.run(n_epochs,
auxiliary,
mc,
iw,
lambda1=l1,
lambda2=l2,
t_max=classif_weight,
verbose=1,
generate_extra_class=1000,
clip_grad=clip_grad,
times=repetitions)
mean_ll = np.mean(log_likehihoods)
print("Mean:", mean_ll)
def __main__():
from models.semi_supervised.deep_generative_models.models.auxiliary_dgm import AuxiliaryDeepGenerativeModel
from models.semi_supervised.deep_generative_models.models.ladder_dgm import LadderDeepGenerativeModel
from models.semi_supervised.deep_generative_models.models.dgm import DeepGenerativeModel
from utils.files_destinations import home_path, destination_folder, data_folder, results_folder
h_dims = [64, 32]
h_dims_classifier = [128]
betas = (0.9, 0.999)
z_dims = [20]
a_dims = [20]
num_elements = 2
img_shape = [1, 28, 28]
meta_df = None
unlabelled_meta_df = None
labels_per_class = -1
early_stopping = 100
if auxiliary:
dgm = AuxiliaryDeepGenerativeModel(vae_flavour, z_dims, h_dims, n_flows=number_of_flows,a_dim=a_dims[0],
num_elements=num_elements, use_conv=use_conv)
dgm.set_configs(home_path=home_path, results_folder=results_folder, data_folder=data_folder,
destination_folder=destination_folder, dataset_name=dataset_name, lr=initial_lr,
meta_destination_folder="meta_pandas_dataframes", csv_filename="csv_loggers")
elif ladder:
dgm = LadderDeepGenerativeModel(vae_flavour, z_dims, h_dims, n_flows=number_of_flows, auxiliary=False)
dgm.set_configs(home_path=home_path, results_folder=results_folder, data_folder=data_folder,
destination_folder=destination_folder, dataset_name=dataset_name, lr=initial_lr,
meta_destination_folder="meta_pandas_dataframes", csv_filename="csv_loggers")
else:
print(vae_flavour)
dgm = DeepGenerativeModel(vae_flavour, z_dims, h_dims, n_flows=number_of_flows, a_dim=None, auxiliary=False,
num_elements=num_elements)
dgm.set_configs(home_path=home_path, results_folder=results_folder, data_folder=data_folder,
destination_folder=destination_folder, dataset_name=dataset_name, lr=initial_lr,
meta_destination_folder="meta_pandas_dataframes", csv_filename="csv_loggers")
if meta_df is not None:
dgm.import_dataframe(meta_df, batch_size, labelled=True)
if has_unlabelled:
dgm.import_dataframe(unlabelled_meta_df, batch_size, labelled=False)
else:
dgm.import_dataframe(meta_df, batch_size, labelled=False)
dgm.load_example_dataset(dataset="mnist", batch_size=batch_size, labels_per_class=labels_per_class)
dgm.define_configurations(early_stopping=early_stopping, warmup=warmup, flavour=vae_flavour)
dgm.cuda()
if auxiliary:
if use_conv:
dgm.set_conv_adgm_layers(is_hebb_layers=False, input_shape=img_shape)
else:
dgm.set_adgm_layers(h_dims=h_dims_classifier)
elif ladder:
dgm.set_ldgm_layers()
else:
if use_conv:
dgm.set_conv_dgm_layers(input_shape=img_shape)
else:
dgm.set_dgm_layers()
# import the M1 in the M1+M2 model (Kingma et al, 2014)
if load_vae:
print("Importing the model: ", dgm.model_file_name)
if use_conv:
dgm.import_cvae()
else:
dgm.load_ae(load_history=False)
#dgm.set_dgm_layers_pretrained()
if resume:
print("Resuming training")
dgm.load_model()
dgm.cuda()
# dgm.vae.generate_random(False, batch_size, z1_size, [1, 28, 28])
dgm.run(n_epochs, auxiliary, mc, iw, lambda1=l1, lambda2=l2 )
def __main__():
from models.semi_supervised.utils.utils import onehot
from models.semi_supervised.deep_generative_models.models.auxiliary_dgm import AuxiliaryDeepGenerativeModel
local_folder = "./data/kaggle_dessins/"
train_images_fname = "train_images.npy"
train_labels_fname = "train_labels.csv"
home_path = "/home/simon/"
destination_folder = "annleukemia"
data_folder = "data"
results_folder = "results"
dataset_name = "dessins_ssl"
betas = (0.9, 0.999)
z_dims = [32]
a_dims = [50]
h_dims = [128, 64]
num_elements = 5
batch_size = 32
number_of_flows = 10
input_shape = [1, 100, 100]
lr = 3e-5
warmup = 100
meta_df = None
unlabelled_meta_df = None
use_conv = False
vae_flavour = "o-sylvester"
labels_per_class = 100
early_stopping = 100
has_unlabelled = False
load_vae = False
resume = False # not working... TODO to be corrected, would be useful
n_epochs = 10000
auxiliary = True
mc = 1
iw = 1
l1 = 0.
l2 = 0.
dgm = AuxiliaryDeepGenerativeModel(vae_flavour,
z_dims,
h_dims,
n_flows=number_of_flows,
a_dim=a_dims[0],
num_elements=num_elements,
use_conv=use_conv)
dgm.set_configs(home_path=home_path,
results_folder=results_folder,
data_folder=data_folder,
destination_folder=destination_folder,
dataset_name=dataset_name,
lr=lr,
meta_destination_folder="meta_pandas_dataframes",
csv_filename="csv_loggers")
train_labels = genfromtxt("data/kaggle_dessins/train_labels.csv",
delimiter=",",
dtype=str,
skip_header=True)[:, 1]
train_labels_set = set(train_labels)
num_classes = len(train_labels_set)
data_transform = transforms.Compose([
#transforms.RandomRotation(180),
#transforms.RandomHorizontalFlip(),
#transforms.RandomVerticalFlip(),
transforms.Grayscale(),
transforms.ToTensor(),
#transforms.ColorJitter()
])
dessins_train_data = datasets.ImageFolder(
root='data/kaggle_dessins/train/', transform=data_transform)
dessins_valid_data = datasets.ImageFolder(
root='data/kaggle_dessins/valid/', transform=data_transform)
train_ds = torch.utils.data.DataLoader(dessins_train_data,
batch_size=batch_size,
shuffle=True,
num_workers=0)
valid_ds = torch.utils.data.DataLoader(dessins_valid_data,
batch_size=batch_size,
shuffle=True,
num_workers=0)
dgm.batch_size = batch_size
dgm.input_shape = input_shape
dgm.input_size = np.prod(input_shape)
dgm.make_loaders(train_ds=train_ds,
valid_ds=valid_ds,
test_ds=None,
labels_per_class=-1,
unlabelled_train_ds=None,
unlabelled_samples=True)
dgm.define_configurations(early_stopping=early_stopping,
warmup=warmup,
flavour=vae_flavour)
dgm.labels_set = os.listdir('data/kaggle_dessins/train/')
dgm.num_classes = len(dgm.labels_set)
dgm.labels = train_labels
dgm.cuda()
dgm.train_loader = dgm.train_loader.dataset
dgm.valid_loader = dgm.valid_loader.dataset
dgm.set_data(labels_per_class=-1,
is_example=False,
extra_class=False,
has_unlabelled_samples=False,
is_split=False,
is_custom_data=True)
dgm.set_adgm_layers(is_hebb_layers=False,
input_shape=input_shape,
h_dims=h_dims)
dgm.run(n_epochs, auxiliary, mc, iw, lambda1=l1, lambda2=l2, clip_grad=0.)