#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# Classify Module
# Module to classify the sentiment of movie reviews using a FastText model.
import nltk
import torch
import torchtext
import utility
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
input_arguments = utility.parse_input_arguments(module="classify")
# Initialize the field and dataset, build the vocabulary, and initialize the iterator.
nltk.download("punkt")
text_field = torchtext.data.Field(
tokenize=nltk.word_tokenize,
preprocessing=lambda x: list(nltk.bigrams(x)),
fix_length=500)
dataset = torchtext.data.TabularDataset(input_arguments.movie_review_path,
format="csv",
skip_header=True,
fields=[("review", text_field)])
text_field.build_vocab(dataset, max_size=25000)
iterator = torchtext.data.Iterator(dataset,
batch_size=1000,
shuffle=False,
# Copyright (C) 2020 Andreas Pentaliotis # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # at your option) any later version. # # 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, see <http://www.gnu.org/licenses/>. # Generate Module # Module to generate images using a trained generator. from keras.models import load_model import utility input_arguments = utility.parse_input_arguments(module="generate") generator = load_model(input_arguments.generator_path) images = utility.generate_images(generator, input_arguments.image_number) utility.save(images, input_arguments.output_path)
#
# 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, see <http://www.gnu.org/licenses/>.
# Simulate Module
# Module to run reinforcement learning simulations.
import simulations
import utility
input_arguments = utility.parse_input_arguments()
state_encoding = "array" if "network" in input_arguments.agent_type else "integer"
environment = utility.create_environment(input_arguments.environment_type,
input_arguments.grid_dimension_size,
input_arguments.reward_function,
state_encoding)
if input_arguments.simulation_function == "training_episodes":
# Control the randomness to get reproducible results.
utility.control_randomness(input_arguments.seed)
# Create the agent and run the training episodes.
agent = utility.create_agent(input_arguments.agent_type,
environment.compute_state_space_size(),
environment.compute_action_space_size())
# Train Module
# Module to train a FastText model on movie review data.
import nltk
import torch
import torch.nn as nn
import torchtext
import fasttext
import utility
torch.manual_seed(1)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
input_arguments = utility.parse_input_arguments(module="train")
# Initialize the fields and dataset, and build the vocabularies.
nltk.download("punkt")
text_field = torchtext.data.Field(
tokenize=nltk.word_tokenize,
preprocessing=lambda x: list(nltk.bigrams(x)),
fix_length=500)
label_field = torchtext.data.LabelField(dtype=torch.float)
dataset = torchtext.data.TabularDataset(input_arguments.movie_review_path,
format="csv",
skip_header=True,
fields=[("review", text_field),
("sentiment", label_field)])
text_field.build_vocab(dataset, max_size=25000)
label_field.build_vocab(dataset)