# Operations on the GPU if available
if torch.cuda.is_available():
DEVICE = 'cuda'
else:
DEVICE = 'cpu'
device = torch.device(DEVICE)
set_seed_everywhere(SEED)
# %% Data
params['t_span'] = (params['t_min'], params['t_max'])
params['p_span'] = (params['p_min'], params['p_max'])
data = create_dataset(params)
train, trainc, test = init_dataset(data, params, transformation=None)
# %% Train
model_nn = NN(nn_params)
results_NN = train_NN(model_nn,
train,
test,
nn_params,
params,
args,
noise=0.01)
# model_pinn = PINN(nn_params, params)
np.mean(mse_u_loss),
np.mean(mse_f_loss)])
# Save models and data
torch.save(model.state_dict(),
PATH_MODELS + NAME + f'_{n_epochs}_{n_data}_{n_coll}.pth')
np.savez(PATH_DATA + NAME + f'_{n_epochs}_{batch_size}_{n_data}_{n_coll}',
train_loss=train_loss)
return train_loss
#%% Testing
if __name__ == '__main__':
args, general, params, nn_params = cli()
params['t_span'] = (params['t_min'], params['t_max'])
params['p_span'] = (params['p_min'], params['p_max'])
data = create_dataset(params)
train, trainc, test = init_dataset(data, params)
#model_nn = NN(nn_params)
#results_nn = train_NN(model_nn, train, test, nn_params, data_params)
model_pinn = PINN(nn_params, params)
results_pinn = train_PINN(model_pinn, trainc, test, nn_params, params)
import argparse
import numpy as np
import tensorflow as tf
from model import ANN
import data
parser = argparse.ArgumentParser(description='Visualize ANN')
parser.add_argument('-d',
'--dataset',
type=str,
default='mnist',
choices=data.get_names())
parser.add_argument('--num_iter', type=int, default=5000)
args = parser.parse_args()
dataset = data.init_dataset(name=args.dataset)
model = ANN(dataset.shape)
model.train(dataset.tr_data, dataset.tr_labels, num_iter=args.num_iter)
if __name__ == '__main__':
DATA_DIR = 'data/'
from cli import cli
from data import create_dataset, init_dataset
args, general, params, nn_params = cli()
params['t_span'] = (params['t_min'], params['t_max'])
params['p_span'] = (params['p_min'], params['p_max'])
n_data = params["n_data"]
n_coll = params['n_collocation']
data = create_dataset(params)
X_u, X_f, y_delta, y_omega = data
train, trainc, test = init_dataset(data, params)
train_idx, trainc_idx, test_idx = init_dataset(data,
params,
sample=False,
transformation=None)
X_train, y_delta_train, y_omega_train, trf_params_train = train
X_test, y_delta_test, y_omega_test, trf_params_test = test
X_train_idx, y_delta_train_idx, y_omega_train_idx, trf_params_train_idx = train_idx
X_test_idx, y_delta_test_idx, y_omega_test_idx, trf_params_test_idx = test_idx
#%% Data for BNN
idx = 0
X_selected = torch.tensor(X_train_idx[idx * n_data:idx * n_data + n_data, :2],
dtype=torch.float32)
y_selected = torch.tensor(y_delta_train_idx[idx * n_data:idx * n_data +
dataset_path = '../dataset/train2017' style_image_path = '../style/kandinsky.jpg' content_image_path = '../content/tubingen.jpg' output_path = '../output/' batch_size = 1 batch_shape = (batch_size, 256, 256, 3) content_loss_weight = 1e0 style_loss_weight = 1e3 tv_loss_weight = 2e2 learning_rate = 1 epoches = 2 data.init_dataset(dataset_path, batch_shape) content_img = data.get_img(content_image_path) content_img = data.img_fit_to(content_img) content_input = np.expand_dims(content_img, axis=0) content_input = models.vgg_preprocess(content_input) content_input_vgg = tf.constant(content_input, dtype=tf.float32, name='content_input') style_input = np.expand_dims(data.get_img(style_image_path), axis=0) style_input = models.vgg_preprocess(style_input) style_input_vgg = tf.Variable(style_input, dtype=tf.float32, name='style_input') #mixer_net = models.load_mixer_net(batch_input) #image_input = mixer_net
},
resolver=lambda root, args, *_: save_obj_detect_image(
args.get('id'), args.get('project'), args.get('annotations'))
),
'allPost': GraphQLField(
ObjAllPostType,
args={
'name': GraphQLArgument(GraphQLString),
'description': GraphQLArgument(GraphQLString),
'tissue': GraphQLArgument(GraphQLString),
'dataset': GraphQLArgument(GraphQLString)
},
resolver=lambda root, args, *_: save_model_post(
args.get('name'),
args.get('description'),
args.get('tissue'),
args.get("dataset"),
),
)
}
)
Schema = GraphQLSchema(QueryRootType, MutationRootType)
# Init test project
fold_fpath = data.get_fpath(cfg.PROJECT_NAME, cfg.FOLD_FNAME)
if not os.path.exists(fold_fpath):
_ = data.init_dataset(cfg.PROJECT_NAME, cfg.MEDIA_PATH,
cfg.IMG_EXT, cfg.PROJECT_LABELS)
import os, sys, numpy as np
import config
os.chdir('src/') # fix for data.init_dataset()
np.random.seed(config.seed)
import data, tfidf, models, sentimentanalysis
from utils import utils, io
# info = pandas.read_csv(config.dataset_dir + 'final_data.csv')
dataset = data.init_dataset()
# load model
m = config.dataset_dir + 'models/default_model.json'
w = config.dataset_dir + 'models/default_model_w.h5'
model = models.load_model(m, w)
if __name__ == '__main__':
args = sys.argv
if len(args) > 1:
filename = '../' + args[1]
else:
filename = config.dataset_dir + '1118.txt'
print('\n filename:', filename)
tokens, lines = io.read_book3(filename)
# build feature vector
v1 = data.tokenlist_to_vector(tokens, dataset.sentiment_dataset)
v2 = np.array(sentimentanalysis.per_book(lines))