# redistribute it and/or modify it under the terms of the GNU General Public
# License as published by the Free Software Foundation, version 2.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
# details.
#
# You should have received a copy of the GNU General Public License along with
# this program; if not, write to the Free Software Foundation, Inc., 51
# Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#
# Copyright Nils Schaetti <*****@*****.**>
# Imports
import torch.utils.data
import dataset
from echotorch.transforms import text
# Author identification dataset
pan18loader = torch.utils.data.DataLoader(dataset.AuthorIdentificationDataset(
root="./data/", download=True, transform=text.GloveVector(), problem=1),
batch_size=1,
shuffle=True)
# Get training data for this fold
for i, data in enumerate(pan18loader):
# Inputs and labels
inputs, labels = data
# end for
w = etnn.ESNCell.generate_w(reservoir_size, w_sparsity)
# Sample average
single_sample_average = np.array([])
# For each problem
for problem in np.arange(1, 3):
# Truth and prediction
y_true = np.array([])
y_pred = np.array([])
# Author identification training dataset
pan18loader_training = torch.utils.data.DataLoader(
dataset.AuthorIdentificationDataset(root="./data/",
download=True,
transform=transformer,
problem=problem,
lang=args.lang),
batch_size=1,
shuffle=True)
# Author identification test dataset
pan18loader_test = torch.utils.data.DataLoader(
dataset.AuthorIdentificationDataset(root="./data/",
download=True,
transform=transformer,
problem=problem,
train=False,
lang=args.lang),
batch_size=1,
shuffle=True)
import torch.utils.data
import dataset
from echotorch.transforms import text
import random
from torch.autograd import Variable
# Experience parameter
window_size = 500
batch_size = 64
sample_batch = 4
epoch_batches = 10
max_epoch = 1
# Author identification training dataset
pan18loader_training = torch.utils.data.DataLoader(
dataset.AuthorIdentificationDataset(root="./data/", download=True, transform=text.Character(), problem=1),
batch_size=1, shuffle=True)
# Author identification test dataset
pan18loader_test = torch.utils.data.DataLoader(
dataset.AuthorIdentificationDataset(root="./data/", download=True, transform=text.Character(), problem=1,
train=False),
batch_size=1, shuffle=True)
# Authors
author_to_idx = dict()
for idx, author in enumerate(pan18loader_training.dataset.authors):
author_to_idx[author] = idx
# end for
# Number of authors
text_length = 20
# Argument
args = tools.functions.argument_parser_training_model()
# Transforms
transform = transforms.Compose([
transforms.Character(),
transforms.ToIndex(start_ix=0),
transforms.MaxIndex(max_id=83),
transforms.ToNGram(n=text_length, overlapse=True),
transforms.Reshape((-1, 20))
])
# Author identification training dataset
dataset_train = dataset.AuthorIdentificationDataset(root="./data/", download=True, transform=transform, problem=1, lang='en')
# Author identification test dataset
dataset_valid = dataset.AuthorIdentificationDataset(root="./data/", download=True, transform=transform, problem=1, train=False, lang='en')
# Cross validation
dataloader_train = torch.utils.data.DataLoader(torchlanguage.utils.CrossValidation(dataset_train), batch_size=1, shuffle=True)
dataloader_valid = torch.utils.data.DataLoader(torchlanguage.utils.CrossValidation(dataset_valid, train=False), batch_size=1, shuffle=True)
# Author to idx
author_to_ix = dict()
for idx, author in enumerate(dataset_train.authors):
author_to_ix[author] = idx
# end for
# Model
