def train_model(model, X_train, y_train):
history = model.fit(X_train,
y_train,
epochs=200,
batch_size=200,
verbose=1,
validation_split=0.2)
plot_history(history)
return history
def main():
train_ds, val_ds = build_image_generator()
model = build_model()
model.summary()
history = model.fit(train_ds, validation_data=val_ds, epochs=10)
model.save('saved_model')
plot_history(history)
def fit_directory(self,
path,
batch_size,
epochs,
val_path=None,
save_weights=False):
train_generator = self._train_generator(path, batch_size)
if val_path is None:
validation_generator = None
validation_steps = None
else:
validation_generator = self._test_val_generator(
val_path, batch_size)
validation_steps = validation_generator.samples / batch_size
callbacks = [
TensorBoard(log_dir='logs/' + self.born_time),
ReduceLROnPlateau(monitor='val_loss',
factor=0.45802,
patience=10,
verbose=1)
]
if save_weights:
callbacks.append(
ModelCheckpoint(os.path.join(
self.dump_path,
'weights_' + time.strftime('%Y%m%d%H%M%S', time.gmtime())),
verbose=1,
period=20))
history = self.model.fit_generator(
train_generator,
steps_per_epoch=train_generator.samples / batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=validation_steps,
callbacks=callbacks)
utils.plot_history(history,
self.dump_path,
identifier='e' + str(epochs) + '_b' +
str(batch_size))
with open(
os.path.join(
self.dump_path, 'e' + str(epochs) + '_b' +
str(batch_size) + '_history.pklz'), 'wb') as f:
cPickle.dump((history.epoch, history.history, history.params,
history.validation_data, self.model.get_config()), f,
cPickle.HIGHEST_PROTOCOL)
if save_weights:
self.model.save_weights(
os.path.join(
self.dump_path, 'e' + str(epochs) + '_b' +
str(batch_size) + '_weights.h5'))
return history
def main(debug: bool = False, eager: bool = False):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
X_train, _1, X_test, _2 = dataget.image.mnist(global_cache=True).get()
# Now binarize data
X_train = (X_train > 0).astype(jnp.float32)
X_test = (X_test > 0).astype(jnp.float32)
print("X_train:", X_train.shape, X_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
model = elegy.Model(
module=VariationalAutoEncoder.defer(),
loss=[KLDivergence(), BinaryCrossEntropy(on="logits")],
optimizer=optix.adam(1e-3),
run_eagerly=eager,
)
epochs = 10
# Fit with datasets in memory
history = model.fit(
x=X_train,
epochs=epochs,
batch_size=64,
steps_per_epoch=100,
validation_data=(X_test, ),
shuffle=True,
)
plot_history(history)
# get random samples
idxs = np.random.randint(0, len(X_test), size=(5, ))
x_sample = X_test[idxs]
# get predictions
y_pred = model.predict(x=x_sample)
# plot results
plt.figure(figsize=(12, 12))
for i in range(5):
plt.subplot(2, 5, i + 1)
plt.imshow(x_sample[i], cmap="gray")
plt.subplot(2, 5, 5 + i + 1)
plt.imshow(y_pred["image"][i], cmap="gray")
plt.show()
def main():
"""Main function of the program.
The function loads the dataset and calls training and validation functions.
"""
model, optimizer, args = initializer()
train_loader, test_loader, exit_tags = utils.load_dataset(args)
# disable training
if args.testing:
result = validate(args, model, test_loader)
#print('\nThe avg val_loss: {:.4f}, avg val_cost: {:.2f}%, avg val_acc: {:.2f}%\n'
# .format(result['val_loss'], result['cost'], result['acc']))
#examine(args, model, test_loader)
return
if args.two_stage:
args.loss_func = "v0"
for epoch in range(args.start_epoch, args.epochs + 1):
print('{:3d}:'.format(epoch), end='')
# two-stage training uses the loss version-1 after training for 25 epochs
if args.two_stage and epoch > 25:
args.loss_func = "v1"
# use adaptive learning rate
if args.adaptive_lr:
utils.adaptive_learning_rate(args, optimizer, epoch)
result = {'epoch': epoch}
result.update(train(args, model, train_loader, optimizer, exit_tags))
# validate and keep history at each log interval
if epoch % args.log_interval == 0:
result.update(validate(args, model, test_loader))
utils.save_history(args, result)
# save model parameters
if not args.no_save_model:
utils.save_model(args, model, epoch)
# print the best validation result
best_epoch = utils.close_history(args)
# save the model giving the best validation results as a final model
if not args.no_save_model:
utils.save_model(args, model, best_epoch, True)
utils.plot_history(args)
def main():
fname = str(datetime.now()).replace(':', '_').replace(' ', '_')[5:19]
create_dirs_write_config(fname, config, 'cnn')
spectrogram, melfilters = load_mel_spectrogram_db(original_audio_list[config['audio']], config)
print('Finished loading audio and creating spectrogram, shape: {}\n'.format(spectrogram.shape))
print('Min/max spectrogram: {}, {}'.format(np.min(spectrogram), np.max(spectrogram)))
config['start_offset'] += config['use_prev_frames']
spectrogram = spectrogram.T # rows should be different training examples, so different points in time
mm_scaler = MinMaxScaler()
spectrogram = mm_scaler.fit_transform(spectrogram)
print('Min/max spectrogram post-scaling: {}, {}'.format(np.min(spectrogram), np.max(spectrogram)))
X_train = np.zeros((config['n_train'], config['use_prev_frames'], spectrogram.shape[1]))
for i in range(config['use_prev_frames']):
X_train[:, i, :] = spectrogram[i:i + config['n_train'], :]
y_train = spectrogram[config['use_prev_frames']:config['n_train']+config['use_prev_frames'], :]
cnn = get_model((X_train.shape[1], X_train.shape[2]))
callbacks = get_callbacks(fname, config)
history = cnn.fit(X_train, y_train, epochs=config['n_epochs'], batch_size=config['batch_size'],
validation_split=0.1,
verbose=1, callbacks=callbacks)
write_keras_model(fname, config, 'cnn', cnn)
plot_history(fname, config, 'cnn', history)
output_spectrogram = np.zeros((config['n_test'] + config['use_prev_frames'], spectrogram.shape[1]))
output_spectrogram[:config['use_prev_frames'], :] = spectrogram[config['test_offset']:config['test_offset']
+ config['use_prev_frames'], :]
print('Running prediction')
for i in range(config['n_test']):
cnn_input = output_spectrogram[i:i + config['use_prev_frames'], :].reshape([1, config['use_prev_frames'],
config['n_mel']])
cnn_output = cnn.predict(cnn_input)
cnn_output = cnn_output.clip(0., 1.)
output_spectrogram[config['use_prev_frames'] + i, :] = cnn_output
convert_output_to_audio(output_spectrogram, config, mm_scaler, melfilters, fname, 'cnn')
def main():
transform_train = torchvision.transforms.Compose([
torchvision.transforms.RandomCrop(32, padding=4, padding_mode='edge'),
torchvision.transforms.RandomHorizontalFlip(),
# torchvision.transforms.RandomRotation(15),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transform_test = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
training_data = torchvision.datasets.CIFAR10('./data/cifar10', train=True, transform=transform_train, download=True)
testing_data = torchvision.datasets.CIFAR10('./data/cifar10', train=False, transform=transform_test, download=True)
model = resnet_v2(input_shape, depth, n_classes)
model.summary()
model.compile(Adam(lr=0.001), 'categorical_crossentropy', 'accuracy')
def lr_schduler(model):
lr = 1e-3
if model.n_epoch > 180:
lr *= 0.5e-3
elif model.n_epoch > 160:
lr *= 1e-3
elif model.n_epoch > 120:
lr *= 1e-2
elif model.n_epoch > 80:
lr *= 1e-1
model.optimizer.lr = lr
print('Learning rate: ', lr)
return lr
lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
cooldown=0,
patience=6,
min_lr=0.5e-6)
history = model.fit_torchvision(training_data, batch_size=batch_size, epochs=epochs,
validation_data=testing_data, callbacks=[lr_schduler])
plot_history(history, 'cifar10_resnet56_v2.jpg')
model.save('resnet56_v2.h8')
def task(param):
output_path = 'output/'
if len(sys.argv) > 1:
output_path = sys.argv[1]
if len(sys.argv) > 2 and sys.argv[2] == 'plot':
plot_history(output_path)
exit(0)
else:
if os.path.exists(output_path):
i = 1
while os.path.exists(output_path):
output_path = 'output%d/' % i
i += 1
if not os.path.exists(output_path):
os.makedirs(output_path)
logging.info('output path: {}'.format(output_path))
train_model(output_path, param)
def plot(update, context):
args = context.args
if not args:
return update.message.reply_text('Please provide ticker as argument')
ticker = args[0].upper()
interval = "1h"
period = "1y"
# Validate interval arg
if len(args) == 2:
interval = args[1]
if interval not in INTERVALS:
return update.message.reply_text(
f'{interval} not a valid interval')
if interval.endswith('m'):
period = "1mo"
# Get historical market data
stock = yf.Ticker(ticker)
hist = stock.history(period=period, interval=interval, prepost=True)
# If ticker is not valid, will appear in _ERRORS dict
if ticker in yf.shared._ERRORS:
return update.message.reply_text(f'{yf.shared._ERRORS[ticker]}')
# Keep last data points
hist = hist.tail(100)
# Plot
plot_history(hist, ticker)
plt.savefig('image.png', bbox_inches='tight')
plt.clf()
# Upload chart to AWS so its available to anyone
aws_file = f'{uuid.uuid4()}.png'
aws_url = f'https://stocks-bot.s3-sa-east-1.amazonaws.com/{aws_file}'
upload_to_aws('image.png', 'stocks-bot', aws_file)
# Response
update.message.reply_photo(aws_url)
def train(g_model,
d_model,
gan_model,
dataset,
latent_dim,
epochs=20,
batch=64):
batch_per_epoch = int(dataset.shape[0] / batch)
steps = batch_per_epoch * epochs
half_batch = int(batch / 2)
d1_hist, d2_hist, g_hist = list(), list(), list()
for i in range(steps):
X_real, y_real = generate_real_samples(dataset, half_batch)
X_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)
# update discriminator model
d_loss1 = d_model.train_on_batch(X_real, y_real)
d_loss2 = d_model.train_on_batch(X_fake, y_fake)
# update generator via discriminator's loss
z_input = generate_latent_points(latent_dim, batch)
y_real2 = np.ones((batch, 1))
g_loss = gan_model.train_on_batch(z_input, y_real2)
print("{:d}, d1={:.3f}, d2={:.3f}, g={:.3f}".format(
i + 1, d_loss1, d_loss2, g_loss))
d1_hist.append(d_loss1)
d2_hist.append(d_loss2)
g_hist.append(g_loss)
if (i + 1) % (batch_per_epoch * 1) == 0:
summarize_performance(i, g_model, latent_dim)
plot_history(d1_hist, d2_hist, g_hist)
def simulate(id, crypto_amount, assets_jpy, coef, crypto_name, start, end, result_root="simulations/", save=False,
verbose=False):
df_historical_price = get_historical_price(crypto_name=crypto_name, start=start, end=end)
history = run_simulation(crypto_amount, assets_jpy, coef, df_historical_price)
final_state = history[-1]
# Count of (buy/sell/nothing)
count = collections.defaultdict(int)
for term in history:
count[term['state']] += 1
summary = {
"earn_rate": (final_state['total'] - assets_jpy) / assets_jpy,
"final_total": final_state['total'],
"final_jpy": final_state["JPY"],
"final_crypto_amount": final_state['crypto_amount'],
**count
}
if save:
sub_path = f"#{id}_{crypto_name}_{assets_jpy:.1f}_coef[{'_'.join(map(lambda x: str(x), coef))}]_{start.date()}_{end.date()}"
result_path = os.path.join(result_root, sub_path)
if not os.path.exists(result_path):
os.makedirs(result_path)
history_file = os.path.join(result_path, "history.xlsx")
plot_file = os.path.join(result_path, "plot.png")
df_history = pd.DataFrame(history)
df_history.to_excel(history_file)
plot_history(df_history, plot_file)
if verbose:
print(f"Result of running: {sub_path}:")
print(summary)
return summary
def fit_directory(self,
path,
batch_size,
epochs,
val_path=None,
save_weights=False):
train_generator = self._train_generator(path, batch_size)
if val_path is None:
validation_generator = None
validation_steps = None
else:
validation_generator = self._test_val_generator(
val_path, batch_size)
validation_steps = validation_generator.samples / batch_size
history = self.model.fit_generator(
train_generator,
steps_per_epoch=train_generator.samples / batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=validation_steps)
utils.plot_history(history,
self.dump_path,
identifier='e' + str(epochs) + '_b' +
str(batch_size))
with open(
os.path.join(
self.dump_path, 'e' + str(epochs) + '_b' +
str(batch_size) + '_history.pklz'), 'wb') as f:
cPickle.dump((history.epoch, history.history, history.params,
history.validation_data, self.model.get_config()), f,
cPickle.HIGHEST_PROTOCOL)
if save_weights:
self.model.save_weights(
os.path.join(
self.dump_path, 'e' + str(epochs) + '_b' +
str(batch_size) + '_weights.h5'))
return history
def main():
(X_train, y_train), (X_valid, y_valid), (X_test,
y_test) = make_mnist_data()
print(f'X_train: {X_train.shape}')
print(f'X_valid: {X_valid.shape}')
print(f'X_test : {X_test.shape}')
print(f'y_train: {y_train.shape}')
print(f'y_valid: {y_valid.shape}')
print(f'y_test: {y_test.shape}')
model = make_model()
history = model.fit(X_train,
y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(X_valid, y_valid))
test_loss, test_acc = model.evaluate(X_test, y_test)
print()
print(f'Test loss: {test_loss}, test acc: {test_acc}')
plot_history(history, 'his.jpg')
def main():
argv = sys.argv
if len(argv) != 4:
print('Usage: ' + argv[0] + ' model_name dataset epochs')
sys.exit(0)
model_name = argv[1]
data = datautils.load(argv[2])
normalized, mean, std = datautils.normalize(data)
differentiated = datautils.differentiate(normalized)
(train, test) = datautils.split(differentiated, 0.7)
# utils.plot_data(data)
print("training set length: {}".format(len(train)))
print("test set length: {}".format(len(test)))
"""train"""
model = LSTM()
time_steps = 100 # window size
batch_size = 8 # data augmentation
history = model.train(model_name, train, int(argv[3]), batch_size,
time_steps)
utils.plot_history(history)
"""test"""
head = int(len(test) * 0.6)
tail = len(test) - head
projection = model.evaluate(model_name, test[:head], tail)
"""plot"""
train = datautils.undifferentiate(train, normalized[0])
test = datautils.undifferentiate(test, train[-1])
projection = datautils.undifferentiate(projection, test[-1])
testset_denorm = datautils.denormalize(test, mean, std)
results_denorm = datautils.denormalize(projection, mean, std)
utils.plot_multiple([testset_denorm, results_denorm], [0, head])
batch_size=32,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
config.validation_dir,
target_size=(150, 150),
batch_size=32,
class_mode='binary')
print(train_generator.class_indices)
# 訓練
history = model.fit_generator(generator=train_generator,
steps_per_epoch=int(np.floor(2000 / 32)),
epochs=50,
validation_data=validation_generator,
validation_steps=int(np.floor(800 / 32)))
utils.plot_history(history)
# 結果を保存
model.save(os.path.join(config.result_dir, 'scratch_model.h5'))
model.save_weights(
os.path.join(config.result_dir, 'scratch_weights.h5'))
utils.save_history(
history, os.path.join(config.result_dir, 'scratch_history.txt'))
except (KeyboardInterrupt, SystemExit):
utils.unlock()
utils.error(config.syserr)
except LunaExcepion as e:
utils.error(e.value)
if (e.value == config.locked):
exit()
def train(train_dataset, valid_dataset, validation_bool, test_dataset,
fam_dict_path, num_column, num_trains, num_tests, test_file_path,
args):
# load model
model = rna_model.DeepRfam(seq_length=args.seq_length,
num_c=num_column,
num_filters=args.num_filters,
filter_sizes=args.filter_sizes,
dropout_rate=args.keep_prob,
num_classes=args.num_classes,
num_hidden=args.num_hidden)
print(model.summary())
# model compile
model.compile(
loss=args.loss_function,
optimizer=eval(f"optimizers.{args.optimizer}")(lr=args.learning_rate),
metrics=['accuracy'])
# start and record training history
if validation_bool:
train_history = model.fit_generator(train_dataset,
epochs=args.num_epochs,
verbose=1,
validation_data=valid_dataset,
use_multiprocessing=True,
workers=6)
else:
train_history = model.fit_generator(train_dataset,
epochs=args.num_epochs,
verbose=1,
use_multiprocessing=True,
workers=6)
# # test accuracy
# t1 = time.time()
# scores = model.evaluate_generator(test_dataset, steps=num_tests // args.batch_size + 1)
# delta_t = time.time() - t1
# print(f"Running time (Prediction):{delta_t} (s)\nAccuracy:{scores[1]}")
# print(f"Running time (Prediction):{delta_t} (s)\nAccuracy:{scores[1]}")
# =================================logging=============================================
local_time = time.strftime("%m-%d_%H-%M", time.localtime())
# determine log file name and `mkdir`
if args.log_name is None:
log_file_name = local_time
else:
log_file_name = local_time + '_' + args.log_name
# os.system(f"mkdir -p {args.log_dir}/{log_file_name}")
os.makedirs(f"{args.log_dir}/{log_file_name}")
# save model to .h5 file
model.save(f"{args.log_dir}/{log_file_name}/{log_file_name}.h5")
# save the image of model structure
plot_model(model,
to_file=f"{args.log_dir}/{log_file_name}/model_structure.png",
show_shapes=True)
# save confusion matrix into .csv file
# prediction = model.predict_generator(test_generator, workers=6, use_multiprocessing=True)
prediction = model.predict_generator(
test_generator) # don't use the multiprocessing
# get the list of true label
with open(test_file_path) as f:
label_list = []
for line in f:
line = line.strip()
seq_index = line.split(',').pop(0)
if seq_index != '':
label_list.append(int(seq_index))
else:
pass
prediction = prediction[:len(label_list)]
prediction_1d = np.array(
[np.argmax(prediction) for prediction in prediction])
# print("Length of true label:", len(label_list))
# print("Length of predict label:", len(prediction_1d))
utils.cm2csv(true_labels=label_list,
predicted_labels=prediction_1d,
dict_file=fam_dict_path,
save_dir=f"{args.log_dir}/{log_file_name}")
print('Accuracy:', accuracy_score(label_list, prediction_1d))
# generate the confusion matrix
if args.num_classes <= 20:
utils.plot_cm(true_labels=label_list,
predicted_labels=prediction_1d,
dict_file=fam_dict_path,
title=f'Confusion Matrix',
save_dir=f"{args.log_dir}/{log_file_name}")
else:
pass
# draw and save history plot
utils.plot_history(train_history, f"{args.log_dir}/{log_file_name}")
# save the classification report
utils.classification_report(true_labels=label_list,
predicted_labels=prediction_1d,
dict_file=fam_dict_path,
save_dir=f"{args.log_dir}/{log_file_name}",
std_out=True)
# save history to .csv file
with open(f"{args.log_dir}/history.csv", 'a') as csv:
print(
f'{local_time},{log_file_name},{args.dataset},{accuracy_score(label_list, prediction_1d)},{str(args.filter_sizes).replace(","," ")},{args.num_filters},{args.batch_size},{args.num_epochs},{args.keep_prob},{args.num_hidden},{args.learning_rate},{args.loss_function},{args.optimizer}, ',
file=csv)
np.array(v_rel_labels).shape)
x_train = [
np.array(v_q_words),
np.array(v_d_words),
np.array(v_rel_labels)
]
# print(np.array(x_train).shape)
history = model.fit(x=x_train,
y=np.array(y_train),
batch_size=config_model_train["batch_size"],
epochs=config_model_train["epochs"],
verbose=config_model_train["verbose"],
shuffle=config_model_train["shuffle"])
# save trained model
print("Saving model and its weights ...")
model.save_weights(config_model_train["weights"] + ".h5")
# serialize model to JSON
model_json = model.to_json()
with open(
join(config_model_train["train_details"],
config_model_param["model_name"] + ".json"),
"w") as json_file:
json_file.write(model_json)
print("Saved model to disk.")
print("Plotting history ...")
plot_history(history, config_model_train["train_details"],
config_model_param["model_name"])
print("Done.")
def main(debug: bool = False, eager: bool = False, logdir: str = "runs"):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
current_time = datetime.now().strftime("%b%d_%H-%M-%S")
logdir = os.path.join(logdir, current_time)
X_train, _1, X_test, _2 = dataget.image.mnist(global_cache=True).get()
print("X_train:", X_train.shape, X_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
class MLP(elegy.Module):
"""Standard LeNet-300-100 MLP network."""
def __init__(self, n1: int = 300, n2: int = 100, **kwargs):
super().__init__(**kwargs)
self.n1 = n1
self.n2 = n2
def call(self, image: jnp.ndarray):
image = image.astype(jnp.float32) / 255.0
x = elegy.nn.Flatten()(image)
x = elegy.nn.sequential(
elegy.nn.Linear(self.n1),
jax.nn.relu,
elegy.nn.Linear(self.n2),
jax.nn.relu,
elegy.nn.Linear(self.n1),
jax.nn.relu,
elegy.nn.Linear(x.shape[-1]),
jax.nn.sigmoid,
)(x)
return x.reshape(image.shape) * 255
class MeanSquaredError(elegy.losses.MeanSquaredError):
# we request `x` instead of `y_true` since we are don't require labels in autoencoders
def call(self, x, y_pred):
return super().call(x, y_pred)
model = elegy.Model(
module=MLP(n1=256, n2=64),
loss=MeanSquaredError(),
optimizer=optax.rmsprop(0.001),
run_eagerly=eager,
)
model.summary(X_train[:64])
# Notice we are not passing `y`
history = model.fit(
x=X_train,
epochs=20,
batch_size=64,
validation_data=(X_test,),
shuffle=True,
callbacks=[elegy.callbacks.TensorBoard(logdir=logdir, update_freq=300)],
)
plot_history(history)
# get random samples
idxs = np.random.randint(0, 10000, size=(5,))
x_sample = X_test[idxs]
# get predictions
y_pred = model.predict(x=x_sample)
# plot and save results
with SummaryWriter(os.path.join(logdir, "val")) as tbwriter:
figure = plt.figure(figsize=(12, 12))
for i in range(5):
plt.subplot(2, 5, i + 1)
plt.imshow(x_sample[i], cmap="gray")
plt.subplot(2, 5, 5 + i + 1)
plt.imshow(y_pred[i], cmap="gray")
# tbwriter.add_figure("AutoEncoder images", figure, 20)
plt.show()
print(
"\n\n\nMetrics and images can be explored using tensorboard using:",
f"\n \t\t\t tensorboard --logdir {logdir}",
)
def main():
args = get_args()
frozen_model, trainable_model, reminder_layers = get_base_models(args)
num_classes = len(os.listdir(args.target_data_train_dir))
if args.augment_images:
train_datagen = image.ImageDataGenerator(
rescale=1. / 255,
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest',
validation_split=0.1
if args.target_data_valid_dir == None else 0.0)
else:
train_datagen = image.ImageDataGenerator(
rescale=1. / 255,
validation_split=0.1
if args.target_data_valid_dir == None else 0.0)
train_data = train_datagen.flow_from_directory(args.target_data_train_dir,
target_size=(299, 299),
shuffle=True,
batch_size=args.batch_size,
subset='training')
# if validation directory not set use some training image for validation
if args.target_data_valid_dir == None:
valid_data = train_datagen.flow_from_directory(
args.target_data_train_dir,
target_size=(299, 299),
shuffle=True,
batch_size=args.batch_size,
subset='validation')
# construct rider model
rider_model = construct_rider_model(trainable_model, frozen_model,
reminder_layers, num_classes)
if args.gpu_util > 1:
# Employs multiple gpus for training if specified
model = multi_gpu_model(model, gpus=args.gpu_util)
rider_model.compile('Adadelta',
loss='categorical_crossentropy',
metrics=['accuracy'])
if not args.target_data_valid_dir == None:
valid_datagen = image.ImageDataGenerator(rescale=1. / 255)
valid_data = valid_datagen.flow_from_directory(
args.target_data_valid_dir,
target_size=(299, 299),
batch_size=args.batch_size)
history = rider_model.fit_generator(train_data,
epochs=args.max_epochs,
validation_data=valid_data)
else:
history = rider_model.fit_generator(train_data,
epochs=args.max_epochs,
validation_data=valid_data)
# Save all of the data for the run
run_name = '{}_{}_rider_network_{}_total_{}_batch'.format(
args.dataset_name, args.model_to_train, args.max_epochs,
args.batch_size)
if args.gpu_util > 1:
# if using a multi gpu model, extract the appropriate layers
rider_model = model.get_layer('model_1')
rider_model.save(
os.path.join(args.output_dir, '{}_ckpt.h5'.format(run_name)))
try:
pickle.dump(
dict(history.history),
open(
os.path.join(args.output_dir, '{}_history.p'.format(run_name)),
'wb'))
print('Successfully dumped history file')
except:
print('History dump failed')
plot_history(history, os.path.join(args.output_dir, run_name), True)
x_train = x_train[:(x_train.shape[0] - (x_train.shape[0] % seq_length))]
y_train = y_train[:(y_train.shape[0] - (y_train.shape[0] % seq_length))]
x_test = x_test[:(x_test.shape[0] - (x_test.shape[0] % seq_length))]
y_test = y_test[:(y_test.shape[0] - (y_test.shape[0] % seq_length))]
x_train = x_train.reshape((-1, seq_length, 84, 84, 1))
y_train = y_train.reshape((-1, seq_length, 128))
x_test = x_test.reshape((-1, seq_length, 84, 84, 1))
y_test = y_test.reshape((-1, seq_length, 128))
print(x_train.shape, y_train.shape)
model = model_type(layer_sizes=layer_sizes,
model_type=model_type,
seq_length=seq_length).build()
model.summary()
# sgd = optimizers.SGD(learning_rate=0.0001, momentum=0.0, nesterov=False)
model.compile(loss='mse', optimizer='adam', metrics=['mse', 'mae'])
history = model.fit(x_train,
y_train,
validation_data=(x_test, y_test),
epochs=num_epochs,
batch_size=batch_size,
shuffle=True)
utils.save_model(model,
model_type,
model_path=os.path.join('./saved_model/', args.game_name))
utils.plot_history(history, '{}_{}'.format(args.game_name, args.model))
next_neighbors=8)([points_input, x]) # conv with fixed graph
x = layers.Activation("relu")(x)
y = EdgeConv(lambda a: kernel_nn(a, nodes=32),
next_neighbors=16)([points_input, x]) # conv with fixed graph
x = layers.Activation("relu")(y)
x = layers.GlobalAveragePooling1D(data_format='channels_first',
name="embedding")(x)
out = layers.Dense(2, name="classification", activation="softmax")(x)
model = keras.models.Model([points_input, feats_input], out)
model.summary()
model.compile(loss="binary_crossentropy",
optimizer=keras.optimizers.Adam(3E-3, decay=1E-4),
metrics=['acc'])
history = model.fit(train_input_data, y_train, batch_size=64, epochs=2)
fig = utils.plot_history(history.history)
fig.savefig("./history_sphere.png")
# Draw Graph in each EdgeConv layerser
fig = utils.get_edge_graph_in_model(test_input_data, model, sample_idx=test_id)
fig.savefig("./cr_sphere_dynamic.png")
# Draw contribution (class score) of each individual cosmic ray
utils.draw_signal_contribution(model,
test_input_data,
test_id=test_id,
path="./signal_contributions_of_cosmic_ray.png")
import simulator
from utils import plot_history
init = {'victim': 0, 'predator': 2}
value_range = 3
def vp(config):
impacts = dict(victim=0, predator=0)
if config['victim'] < config['predator']:
impacts['victim'] = -1
impacts['predator'] = -1
elif config['victim'] > config['predator']:
impacts['victim'] = 1
impacts['predator'] = 1
else:
impacts['victim'] = 1
impacts['predator'] = -1
return impacts
response = simulator.run("report", value_range, init, vp, nsteps=150)
print(response)
response = plot_history("report")
print(response)
def train_model(output_path, param):
batch_size = int(sys.argv[2])
val_steps = 11407 // batch_size
train_steps = 216708 // batch_size
# val_steps = 35
# train_steps = 677
epochs = 10
with Parallel_image_transformer('fashion_data/train_95-ac.txt',
(batch_size, 224, 224, 3)) as train_gen:
with Parallel_image_transformer('fashion_data/validation_95-ac.txt',
(batch_size, 224, 224, 3)) as val_gen:
# with Parallel_np_arrays_reader(os.path.join(btl_path, 'btl_train_npz.txt'), ['attr_cls'], 5) as train_gen:
# with Parallel_np_arrays_reader(os.path.join(btl_path, 'btl_validation_npz.txt'), ['attr_cls'], 5) as val_gen:
log_path = os.path.join(output_path, 'model_train.csv')
csv_log = CSVLogger(log_path, separator=';', append=False)
filepath = os.path.join(
output_path,
"best_model-{epoch:03d}-{loss:.4f}-{val_loss:.4f}.h5")
# early_stopper = EarlyStopping(min_delta=0.001, patience=10)
checkpoint = ModelCheckpoint(filepath,
monitor='loss',
verbose=1,
save_best_only=False,
save_weights_only=False,
mode='auto',
period=1)
lrate = LearningRateScheduler(step_decay)
# callbacks_list = [csv_log, checkpoint, early_stopper]
callbacks_list = [csv_log, checkpoint, lrate]
model = create_model(False, (224, 224, 3), param)
# model = create_model(True, (7, 7, 2048), param)
# model = ResNet((160, 96, 3), 4, basic_block, repetitions=[2, 2, 2, 1])
model.load_weights('output3/best_weights.hdf5', by_name=True)
# model = load_model('output4/best_model-001-1.2537-1.3404.h5')
for layer in model.layers:
if layer.name not in {'pr_cls', 'conv_5_2'}:
layer.trainable = False
print(model.summary())
plot_model(model,
to_file=os.path.join(output_path, 'model.png'),
show_shapes=True,
show_layer_names=False)
# ## Compile
model.compile(#optimizer=SGD(lr=0.1, momentum=0.9, nesterov=True),
optimizer=Adam(lr=1e-3, decay=1e-5),
# optimizer=Adadelta(),
# loss='binary_crossentropy',
loss={'pr_attr': 'binary_crossentropy', 'pr_cls': 'categorical_crossentropy'},
# loss_weights=[1., 0.05],
metrics=['categorical_accuracy'])
t_begin = datetime.datetime.now()
## Fit
model.fit_generator(train_gen,
steps_per_epoch=train_steps,
epochs=epochs,
validation_data=val_gen,
validation_steps=val_steps,
verbose=1,
callbacks=callbacks_list)
print(datetime.datetime.now())
print('total_time: {}'.format(str(datetime.datetime.now() - t_begin)))
print('model saved to: {}'.format(output_path))
model.save(os.path.join(output_path, 'final_model.h5'))
model.save_weights(os.path.join(output_path, 'final_weights.hdf5'))
plot_history(output_path)
optimizer=optimizers.RMSprop(lr=0.003),
loss='categorical_crossentropy',
metrics=['accuracy'],
)
model.summary()
# Implementation of early stopping
my_callback = EarlyStopping(monitor='val_accuracy',
patience=10,
restore_best_weights=True)
# Train model
batch_size = 128
epochs = 500
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=[my_callback],
validation_split=0.2)
# Plotting
plot_history(history)
# Evaluate model
scores = model.evaluate(x_test, y_test)
print('Test loss: {} - Accuracy: {}'.format(*scores))
# Save the model
save_keras_model(model, "../deliverable/nn_task1.h5")
def main(debug: bool = False, eager: bool = False):
if debug:
import debugpy
print("Waiting for debugger...")
debugpy.listen(5678)
debugpy.wait_for_client()
X_train, y_train, X_test, y_test = dataget.image.mnist(
global_cache=True).get()
print("X_train:", X_train.shape, X_train.dtype)
print("y_train:", y_train.shape, y_train.dtype)
print("X_test:", X_test.shape, X_test.dtype)
print("y_test:", y_test.shape, y_test.dtype)
class MLP(elegy.Module):
"""Standard LeNet-300-100 MLP network."""
def __init__(self, n1: int = 300, n2: int = 100, **kwargs):
super().__init__(**kwargs)
self.n1 = n1
self.n2 = n2
def call(self, image: jnp.ndarray):
image = image.astype(jnp.float32) / 255.0
mlp = hk.Sequential([
hk.Flatten(),
hk.Linear(self.n1),
jax.nn.relu,
hk.Linear(self.n2),
jax.nn.relu,
hk.Linear(10),
])
return dict(outputs=mlp(image))
model = elegy.Model(
module=MLP.defer(n1=300, n2=100),
loss=[
elegy.losses.SparseCategoricalCrossentropy(from_logits=True,
on="outputs"),
elegy.regularizers.GlobalL2(l=1e-4),
],
metrics=elegy.metrics.SparseCategoricalAccuracy.defer(on="outputs"),
optimizer=optix.rmsprop(1e-3),
run_eagerly=eager,
)
history = model.fit(
x=X_train,
y=dict(outputs=y_train),
epochs=100,
steps_per_epoch=200,
batch_size=64,
validation_data=(X_test, dict(outputs=y_test)),
shuffle=True,
)
plot_history(history)
# get random samples
idxs = np.random.randint(0, 10000, size=(9, ))
x_sample = X_test[idxs]
# get predictions
y_pred = model.predict(x=x_sample)
# plot results
plt.figure(figsize=(12, 12))
for i in range(3):
for j in range(3):
k = 3 * i + j
plt.subplot(3, 3, k + 1)
plt.title(f"{np.argmax(y_pred['outputs'][k])}")
plt.imshow(x_sample[k], cmap="gray")
plt.show()
def main(args,data_dir, model_name, num, lr, epochs, batch_size = 16, download_data = False):
"""
Main function
Args:
data_dir: directory to download Pascal VOC data
model_name: resnet18, resnet34 or resnet50
num: model_num for file management purposes (can be any postive integer. Your results stored will have this number as suffix)
lr: initial learning rate list [lr for resnet_backbone, lr for resnet_fc]
epochs: number of training epochs
batch_size: batch size. Default=16
download_data: Boolean. If true will download the entire 2012 pascal VOC data as tar to the specified data_dir.
Set this to True only the first time you run it, and then set to False. Default False
save_results: Store results (boolean). Default False
Returns:
test-time loss and average precision
Example way of running this function:
if __name__ == '__main__':
main('../data/', "resnet34", num=1, lr = [1.5e-4, 5e-2], epochs = 15, batch_size=16, download_data=False, save_results=True)
"""
# Initialize cuda parameters
use_cuda = torch.cuda.is_available()
np.random.seed(2019)
torch.manual_seed(2019)
device = torch.device("cuda" if use_cuda else "cpu")
print("Available device = ", device)
model = models.__dict__[args.arch]()
for name, param in model.named_parameters():
if name not in ['fc.weight', 'fc.bias']:
param.requires_grad = False
#model.avgpool = torch.nn.AdaptiveAvgPool2d(1)
#model.load_state_dict(model_zoo.load_url(model_urls[model_name]))
checkpoint = torch.load(args.pretrained, map_location="cpu")
state_dict = checkpoint['state_dict']
for k in list(state_dict.keys()):
# retain only encoder_q up to before the embedding layer
if k.startswith('module.encoder_q') and not k.startswith('module.encoder_q.fc'):
# remove prefix
state_dict[k[len("module.encoder_q."):]] = state_dict[k]
# delete renamed or unused k
del state_dict[k]
msg = model.load_state_dict(state_dict, strict=False)
assert set(msg.missing_keys) == {"fc.weight", "fc.bias"}
print("=> loaded pre-trained model '{}'".format(args.pretrained))
num_ftrs = model.fc.in_features
model.fc = torch.nn.Linear(num_ftrs, 20)
model.fc.weight.data.normal_(mean=0.0, std=0.01)
model.fc.bias.data.zero_()
model.to(device)
parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
print("optimized parameters",parameters)
optimizer = optim.SGD([
{'params': parameters, 'lr': lr, 'momentum': 0.9}
])
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, 12, eta_min=0, last_epoch=-1)
# Imagnet values
mean=[0.457342265910642, 0.4387686270106377, 0.4073427106250871]
std=[0.26753769276329037, 0.2638145880487105, 0.2776826934044154]
# mean=[0.485, 0.456, 0.406]
# std=[0.229, 0.224, 0.225]
transformations = transforms.Compose([transforms.Resize((300, 300)),
# transforms.RandomChoice([
# transforms.CenterCrop(300),
# transforms.RandomResizedCrop(300, scale=(0.80, 1.0)),
# ]),
transforms.RandomChoice([
transforms.ColorJitter(brightness=(0.80, 1.20)),
transforms.RandomGrayscale(p = 0.25)
]),
transforms.RandomHorizontalFlip(p = 0.25),
transforms.RandomRotation(25),
transforms.ToTensor(),
transforms.Normalize(mean = mean, std = std),
])
transformations_valid = transforms.Compose([transforms.Resize(330),
transforms.CenterCrop(300),
transforms.ToTensor(),
transforms.Normalize(mean = mean, std = std),
])
# Create train dataloader
dataset_train = PascalVOC_Dataset(data_dir,
year='2007',
image_set='train',
download=download_data,
transform=transformations,
target_transform=encode_labels)
train_loader = DataLoader(dataset_train, batch_size=batch_size, num_workers=4, shuffle=True)
# Create validation dataloader
dataset_valid = PascalVOC_Dataset(data_dir,
year='2007',
image_set='val',
download=download_data,
transform=transformations_valid,
target_transform=encode_labels)
valid_loader = DataLoader(dataset_valid, batch_size=batch_size, num_workers=4)
# Load the best weights before testing
if not os.path.exists(args.log):
os.mkdir(args.log)
log_file = open(os.path.join(args.log, "log-{}.txt".format(num)), "w+")
model_dir=os.path.join(args.log,"model")
if not os.path.exists(model_dir):
os.mkdir(model_dir)
log_file.write("----------Experiment {} - {}-----------\n".format(num, model_name))
log_file.write("transformations == {}\n".format(transformations.__str__()))
trn_hist, val_hist = train_model(model, device, optimizer, scheduler, train_loader, valid_loader, model_dir, num, epochs, log_file)
torch.cuda.empty_cache()
plot_history(trn_hist[0], val_hist[0], "Loss", os.path.join(model_dir, "loss-{}".format(num)))
plot_history(trn_hist[1], val_hist[1], "Accuracy", os.path.join(model_dir, "accuracy-{}".format(num)))
log_file.close()
#---------------Test your model here---------------------------------------
# Load the best weights before testing
print("Evaluating model on test set")
print("Loading best weights")
weights_file_path = os.path.join(model_dir, "model-{}.pth".format(num))
assert os.path.isfile(weights_file_path)
print("Loading best weights")
model.load_state_dict(torch.load(weights_file_path))
transformations_test = transforms.Compose([transforms.Resize(330),
transforms.FiveCrop(300),
transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),
transforms.Lambda(lambda crops: torch.stack([transforms.Normalize(mean = mean, std = std)(crop) for crop in crops])),
])
dataset_test = PascalVOC_Dataset(data_dir,
year='2007',
image_set='test',
download=download_data,
transform=transformations_test,
target_transform=encode_labels)
test_loader = DataLoader(dataset_test, batch_size=batch_size, num_workers=0, shuffle=False)
loss, ap, scores, gt = test(model, device, test_loader, returnAllScores=True)
gt_path, scores_path, scores_with_gt_path = os.path.join(model_dir, "gt-{}.csv".format(num)), os.path.join(model_dir, "scores-{}.csv".format(num)), os.path.join(model_dir, "scores_wth_gt-{}.csv".format(num))
utils.save_results(test_loader.dataset.images, gt, utils.object_categories, gt_path)
utils.save_results(test_loader.dataset.images, scores, utils.object_categories, scores_path)
utils.append_gt(gt_path, scores_path, scores_with_gt_path)
utils.get_classification_accuracy(gt_path, scores_path, os.path.join(model_dir, "clf_vs_threshold-{}.png".format(num)))
return loss, ap
fname = "bkg_" + tag + file + ".npz"
temp = np.load(dataDir + fname)
images = temp['images'][:, 1:]
images = np.reshape(images, [len(images), 40, 40, 4])
x_test = images[:, :, :, [0, 2, 3]]
if (i == 0): predictionsB = model.predict(x_test)
else:
predictionsB = np.concatenate(
[predictionsB, model.predict(x_test)])
predictions = np.concatenate((predictionsE, predictionsB))
true = np.concatenate(
(np.ones(len(predictionsE)), np.zeros(len(predictionsB))))
y_test = keras.utils.to_categorical(true, num_classes=2)
utils.plot_history(history, plotDir, ['loss', 'accuracy'])
print()
print("Calculating and plotting confusion matrix")
cm = utils.calc_cm(y_test, predictions)
utils.plot_confusion_matrix(cm, ['bkg', 'e'], plotDir + 'cm.png')
print()
print("Plotting certainty")
utils.plot_certainty(y_test, predictions, plotDir + 'certainty.png')
print()
precision, recall = utils.calc_binary_metrics(cm)
print("Precision = TP/(TP+FP) = fraction of predicted true actually true ",
round(precision, 5))
print("Recall = TP/(TP+FN) = fraction of true class predicted to be true ",
print('shapes train; test; val:')
print(data_train.shape)
print(data_test.shape)
# corrompe o centro da imagem
cropy = 40
cropx = 40
x_train_noisy = crop_image(data_train, cropx, cropy)
x_test_noisy = crop_image(data_test, cropx, cropy)
#x_val_noisy = crop_image(data_val, cropx, cropy)
x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)
#x_val_noisy = np.clip(x_val_noisy, 0., 1.)
showOrigNoisy(data_test, x_test_noisy, num=4)
ae = Autoencoder()
if train:
ae.treina_modelo(x_train_noisy, data_train, x_test_noisy, data_test)
print(ae.history.history.keys())
plot_history(ae.history)
c10test = ae.prever(x_test_noisy)
#print("c10test: {0}\nc10val: {1}".format(np.average(c10test), np.average(c10val)))
showOrigNoisyRec(data_test, x_test_noisy, c10test)
end = time.clock()
colorprint(Color.BLUE, 'Done! Elapsed time: ' + str(end - start) + 'sec\n')
colorprint(Color.BLUE, 'Saving the model into ' + model_identifier + '.h5 \n')
model.save_weights(
'dump/patch_models/' + model_identifier + '.h5') # always save your weights after training or during training
colorprint(Color.BLUE, 'Done!\n')
if not os.path.exists('dump/patch_histories'):
os.mkdir('dump/patch_histories')
with open('dump/patch_histories/' + model_identifier + '_history.pklz', 'wb') as f:
cPickle.dump(
(history.epoch, history.history, history.params, history.validation_data, model.get_config()), f,
cPickle.HIGHEST_PROTOCOL)
# summarize history for accuracy
plot_history(history, model_identifier, metric='acc', plot_validation=TRAIN_WITH_VALIDATION, path='dump/patch_histories/')
# summarize history for loss
plot_history(history, model_identifier, metric='loss', plot_validation=TRAIN_WITH_VALIDATION, path='dump/patch_histories/')
colorprint(Color.BLUE, 'Start generating BoVW representation...\n')
start = time.clock()
model_layer = Model(inputs=model.input, outputs=model.get_layer(LAYER).output)
CLASSES = np.array(CLASSES)
y_train = []
X_train = []
for cls in CLASSES:
for imname in os.listdir(os.path.join(DATASET_DIR, 'train', cls)):
im = Image.open(os.path.join(DATASET_DIR, 'train', cls, imname))
loss_history = []
mu_awareness = np.ones((t_sample,))
mu_awareness = torch.tensor(mu_awareness, requires_grad=True).float().to(device)
data_generator = SampleDataPointsGenerator()
# %%
# ----------- TRAIN -------------
for i in range(1000):
loss = network.step(mu_item=next(data_generator), mu_awareness=mu_awareness, train=True)
# print(loss)
loss_history.append(loss)
if (i+1) % 500 == 0:
[plot_history(np.array(loss_history)[:, i, :], title='unit {}'.format(i)) for i in range(unit_count)]
loss_history = []
print("==================")
# %%
# ----------- TEST -------------
next_mu_item = next(data_generator)
for i in range(20):
loss = network.step(mu_item=next_mu_item, mu_awareness=mu_awareness, train=False)
next_mu_item = loss[0][6][0].detach().numpy()
# plot_1d(loss[0][6].detach().numpy(), title="mu")
# plot_1d(loss[0][7].detach().numpy(), title="mu_bar")
# plot_1d(loss[0][8].detach().numpy(), title="mu_hat")
args = parser.parse_args()
cifar_dir = args.cifar_root
fig_path = args.fig_path
validation_split = args.val_split
batch_size = args.batch_size
epochs = args.epochs
weight_path = args.weight_path
weight_decay = args.weight_decay
lr = args.lr
SEED = args.seed # set random seed (default as 1234)
# split train, val, test from `get_data` function
train_loader, val_loader, test_loader = get_data(cifar_dir=cifar_dir, batch_size=batch_size, augment=True, validation_split=validation_split)
# load model
model = VGG_lite()
# define loss
loss = nn.CrossEntropyLoss()
# train the model
model, history = train(model, train_loader, val_loader, epochs, loss, batch_size, optimizer='adam', weight_decay=weight_decay, lr=lr)
# save the model accordeing to `weight_path` from parser (default to './weights/final.pth')
torch.save(model.state_dict(), weight_path)
plot_history(history, fig_path) # save figures
acc, cm, cm_norm = evaluate(model, test_loader) # evaluate trained model
plot_cm(cm, cm_norm, fig_path) # save confusion matrix figures
print('Test Accuracy: {}%'.format(round(acc*100, 4))) # print the model test accuracy
