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?