xp.write(u"Author : {}".format(sfgram_dataset.author), log_level=0)
xp.write(u"Texts : {}".format(len(sfgram_dataset.texts)), log_level=0)
# W index
w_index = 0
# Last space
last_space = dict()
# Iterate
for space in param_space:
# Params
reservoir_size, w_sparsity, leak_rate, input_scaling, \
input_sparsity, spectral_radius, feature, aggregation, \
state_gram, feedbacks_sparsity, lang, embedding, \
ridge_param, washout = functions.get_params(space)
# Choose the right transformer
sfgram_dataset.transform = features.create_transformer(
feature, embedding, args.embedding_path, lang)
# Set experience state
xp.set_state(space)
# Average sample
average_sample = np.array([])
# For each sample
for n in range(args.n_samples):
# Set sample
xp.set_sample_state(n)
xp.write(u"Authors : {}".format(reutersc50_dataset.authors), log_level=0)
# Last space
last_space = dict()
# Create W matrices
base_w = create_matrices(int(args.get_space()['reservoir_size'][-1]),
float(args.get_space()['w_sparsity'][-1]),
int(args.get_space()['n_layers'][-1]))
# Iterate
for space in param_space:
# Params
reservoir_size, w_sparsity, leak_rate, input_scaling, \
input_sparsity, spectral_radius, feature, aggregation, \
state_gram, feedbacks_sparsity, lang, embedding = functions.get_params(space)
n_layers = int(space['n_layers'])
if n_layers == 1:
leaky_rates = leak_rate
else:
leaky_rates = np.linspace(1.0, leak_rate, n_layers)
# end if
w = base_w[:n_layers]
# Choose the right transformer
reutersc50_dataset.transform = features.create_transformer(
feature, embedding, args.embedding_path, lang)
# Set experience state
xp.set_state(space)
#region INIT
# Seed
torch.manual_seed(1)
# Parse args
args, use_cuda, param_space, xp = argument_parsing.parser_training()
# Last space
last_space = dict()
# Iterate
for space in param_space:
# Params
hidden_size, cell_size, feature, lang, dataset_start, window_size, learning_window, embedding_size, rnn_type, \
num_layers, dropout, output_dropout = functions.get_params(space)
# Feature transformer
feature_transformer = features.create_transformer(feature, args.pretrained,
args.embedding_path,
lang)
# Load PAN17 dataset
pan17_dataset, pan17_dataset_per_tweet, pan17_loader_train, pan17_loader_dev, pan17_loader_test = dataset.load_pan17_dataset_per_tweet(
output_length=settings.output_length[feature],
output_dim=settings.input_dims[feature],
batch_size=args.batch_size,
trained=not args.pretrained,
load_type=feature,
transform=feature_transformer)
# Disable CUDA
if not args.cuda:
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# end if
# Print authors
xp.write(u"Authors : {}".format(reutersc50_dataset.authors), log_level=0)
# Last space
last_space = dict()
# Iterate
for space in param_space:
# Params
hidden_size, cell_size, feature, lang, dataset_start, window_size, learning_window, embedding_size, rnn_type, num_layers, dropout, output_dropout = functions.get_params(
space)
# Dataset start
reutersc50_dataset.set_start(dataset_start)
# Set experience state
xp.set_state(space)
# Average sample
average_sample = np.array([])
# Load GloVe if needed
if args.pretrained and args.fine_tuning:
word2index, embedding_matrix, pretrained_vocsize = features.load_pretrained_weights(
feature=feature,
emb_path=args.embedding_path,
# First params
w = functions.manage_w(xp, args, args.keep_w)
# W index
w_index = 0
# Last space
last_space = dict()
# Iterate
for space in param_space:
# Params
reservoir_size, w_sparsity, leak_rate, input_scaling, \
input_sparsity, spectral_radius, feature, aggregation, \
state_gram, feedbacks_sparsity, lang, embedding, \
dataset_start, window_size, ridge_param, washout = functions.get_params(space)
# Choose the right transformer
reutersc50_dataset.transform = features.create_transformer(
feature, embedding, args.embedding_path, lang)
# Dataset start
reutersc50_dataset.set_start(dataset_start)
# Set experience state
xp.set_state(space)
# Average sample
average_sample = np.array([])
# New W?
# First params
w = functions.manage_w(xp, args, args.keep_w)
# W index
w_index = 0
# Last space
last_space = dict()
# Iterate
for space in param_space:
# Params
reservoir_size, w_sparsity, leak_rate, input_scaling, \
input_sparsity, spectral_radius, feature, aggregation, \
state_gram, feedbacks_sparsity, lang, embedding, dataset_start, window_size = functions.get_params(space)
# Choose the right transformer
reutersc50_dataset.transform = features.create_transformer(
feature, embedding, args.embedding_path, lang)
# Dataset start
reutersc50_dataset.set_start(dataset_start)
# Set experience state
xp.set_state(space)
# Average sample
average_sample = np.array([])
# New W?
