def load_eager(name, model, optimizer=None):
checkpoint_dir = "./data/" + name
checkpoint_prefix = os.path.join(checkpoint_dir, name + "_ckpt")
if optimizer:
root = tfe.Checkpoint(optimizer=optimizer, model=model)
else:
root = tfe.Checkpoint(model=model)
root.restore(tf.train.latest_checkpoint(checkpoint_dir))
def save_eager(name, model, optimizer=None):
checkpoint_dir = "./data/" + name
os.makedirs(checkpoint_dir, exist_ok=True)
checkpoint_prefix = os.path.join(checkpoint_dir, name + "_ckpt")
if optimizer:
root = tfe.Checkpoint(optimizer=optimizer, model=model)
else:
root = tfe.Checkpoint(model=model)
root.save(file_prefix=checkpoint_prefix)
def main(_):
tfe.enable_eager_execution()
# Automatically determine device and data_format
(device, data_format) = ('/gpu:0', 'channels_first')
if FLAGS.no_gpu or tfe.num_gpus() <= 0:
(device, data_format) = ('/cpu:0', 'channels_last')
# If data_format is defined in FLAGS, overwrite automatically set value.
if FLAGS.data_format is not None:
data_format = data_format
print('Using device %s, and data format %s.' % (device, data_format))
# Load the datasets
train_ds = mnist_dataset.train(FLAGS.data_dir).shuffle(60000).batch(
FLAGS.batch_size)
test_ds = mnist_dataset.test(FLAGS.data_dir).batch(FLAGS.batch_size)
# Create the model and optimizer
model = mnist.Model(data_format)
optimizer = tf.train.MomentumOptimizer(FLAGS.lr, FLAGS.momentum)
# Create file writers for writing TensorBoard summaries.
if FLAGS.output_dir:
# Create directories to which summaries will be written
# tensorboard --logdir=<output_dir>
# can then be used to see the recorded summaries.
train_dir = os.path.join(FLAGS.output_dir, 'train')
test_dir = os.path.join(FLAGS.output_dir, 'eval')
tf.gfile.MakeDirs(FLAGS.output_dir)
else:
train_dir = None
test_dir = None
summary_writer = tf.contrib.summary.create_file_writer(train_dir,
flush_millis=10000)
test_summary_writer = tf.contrib.summary.create_file_writer(
test_dir, flush_millis=10000, name='test')
# Create and restore checkpoint (if one exists on the path)
checkpoint_prefix = os.path.join(FLAGS.model_dir, 'ckpt')
step_counter = tf.train.get_or_create_global_step()
checkpoint = tfe.Checkpoint(model=model,
optimizer=optimizer,
step_counter=step_counter)
# Restore variables on creation if a checkpoint exists.
checkpoint.restore(tf.train.latest_checkpoint(FLAGS.model_dir))
# Train and evaluate for a set number of epochs.
with tf.device(device):
for _ in range(FLAGS.train_epochs):
start = time.time()
with summary_writer.as_default():
train(model, optimizer, train_ds, step_counter,
FLAGS.log_interval)
end = time.time()
print('\nTrain time for epoch #%d (%d total steps): %f' %
(checkpoint.save_counter.numpy() + 1, step_counter.numpy(),
end - start))
with test_summary_writer.as_default():
test(model, test_ds)
checkpoint.save(checkpoint_prefix)
def save_checkpoint(self, epoch, model):
"""
Create and save a checkpoint.
"""
# Create the checkpoint directory if required
self.ckpt_dir = os.path.join(self.session_dir, 'checkpoints')
if not os.path.exists(self.ckpt_dir):
os.makedirs(self.ckpt_dir)
self.checkpoint = tfe.Checkpoint(optimizer=self.optimizer,
model=model.arch)
# Note: This allows the user to specify how many checkpoints should be saved.
# Tensorflow does not expose the parameter in tfe.Checkpoint for max_to_keep,
# however under the hood it uses a Saver object so we can hack around this.
from tensorflow.python.training.saver import Saver
default_args = list(Saver.__init__.__code__.co_varnames)
default_values = list(Saver.__init__.__defaults__)
if 'self' in default_args:
# Subtract one since default_values has no value for 'self'
idx = default_args.index('max_to_keep') - 1
default_values[idx] = self.p.max_num_ckpts_to_keep
Saver.__init__.__defaults__ = tuple(default_values)
else:
assert (False)
# Save the checkpoint
if epoch % self.p.ckpt_save_frequency == 0:
self.checkpoint.save(os.path.join(self.ckpt_dir, 'ckpt'))
else:
return
def checkpoint_load(_checkpoint_path, neural_kb, optimizer):
logger.info('Loading model...')
logger.info(' neural kb and optimizer')
checkpoint_model_prefix = os.path.join(_checkpoint_path, "model/")
model_saver_path = tf.train.latest_checkpoint(checkpoint_model_prefix)
# old format compatibility
if os.path.exists(os.path.join(_checkpoint_path, "optim/")):
import tensorflow.contrib.eager as tfe
checkpoint_optim_prefix = os.path.join(_checkpoint_path, "optim/")
optim_checkpoint_path = tf.train.latest_checkpoint(
checkpoint_optim_prefix)
if optim_checkpoint_path is not None:
optim_checkpoint = tfe.Checkpoint(
optimizer=optimizer,
optimizer_step=tf.train.get_or_create_global_step())
optim_checkpoint.restore(optim_checkpoint_path)
logger.info(' optimiser')
else:
logger.info(
" ....couldn't find optim/, ignoring it (loading old model)."
)
model_saver = tfe.Saver(neural_kb.variables)
model_saver.restore(model_saver_path)
else:
model_saver = tf.train.Saver(neural_kb.variables +
optimizer.variables() +
[tf.train.get_or_create_global_step()])
model_saver.restore(None, model_saver_path)
logger.info('... loading done.')
def load(self, model, filename):
model_objects = {'model': model}
print("=> loading checkpoint '{}'".format(filename))
ckpt = tfe.Checkpoint(**model_objects)
ckpt.restore(filename)
return model_objects['model']
def __init__(self, cfg, net, trainingset, valset, resume=False):
self.cfg = cfg
self.net = net
# Datasets
self.trainingset = trainingset
self.valset = valset
# Using Adam optimizer
self.optimizer = tf.train.AdamOptimizer(
learning_rate=self.cfg.LEARNING_RATE)
# Create global step
self.global_step = tf.train.get_or_create_global_step()
# Create checkpoint directory and save checkpoints
self.epoch = tfe.Variable(0,
name='epoch',
dtype=tf.float32,
trainable=False)
self.checkpoint_dir = self.cfg.CKPT_PATH
self.checkpoint_encoder = os.path.join(self.checkpoint_dir, 'model')
self.root1 = tfe.Checkpoint(
optimizer=self.optimizer,
model=self.net,
optimizer_step=tf.train.get_or_create_global_step())
# If resume is true continue from saved checkpoint
if resume:
self.root1.restore(tf.train.latest_checkpoint(self.checkpoint_dir))
def main(args):
xr, log_igfr_r, labels_r = loadData('NEW_GFR_TRAIN')
xe, log_igfr_e, labels_e = loadData('NEW_GFR_TEST')
train_ds = tf.data.Dataset.from_tensor_slices((xr, log_igfr_r, labels_r))
test_ds = tf.data.Dataset.from_tensor_slices((xe, log_igfr_e, labels_e))
train_ds = train_ds.shuffle(xr.shape[0]).batch(batch_size)
# test_ds = test_ds.batch(batch_size)
test_ds = test_ds.batch(1)
model = KidneyModel(n_cat)
init_lr, momentum = args.learning_rate, 0.9
lr = tfe.Variable(init_lr, name="learning_rate")
optimizer = tf.train.AdamOptimizer(lr)
with tf.device('/cpu:0'):
lr = tfe.Variable(init_lr, name="learning_rate")
optimizer = tf.train.AdamOptimizer(lr)
for epoch in range(args.epochs):
print('epoch', epoch)
train_acc = tfe.metrics.Accuracy('train_accuracy')
total_loss, total_batch = 0.0, 0.0
for (batch, (x, log_igfr,
labels)) in enumerate(tfe.Iterator(train_ds)):
with tf.GradientTape() as tape:
mean, var, logits, igfr = model(x)
loss_value = loss(mean, var, logits, igfr, labels,
log_igfr, args.enlarge, args.w_div,
args.w_l2)
total_loss += loss_value.cpu().numpy()
total_batch += 1
train_acc(tf.argmax(logits, axis=1, output_type=tf.int32),
tf.argmax(labels, axis=1, output_type=tf.int32))
grads = tape.gradient(loss_value, model.variables)
optimizer.apply_gradients(
zip(grads, model.variables),
global_step=tf.train.get_or_create_global_step())
print('Learning Rate', lr.numpy())
if (epoch + 1) % 50 == 0:
lr.assign(lr.numpy() / 2)
print('Training acc {}'.format(100 * train_acc.result()))
print('train_acc', 100 * train_acc.result().cpu().numpy())
test_acc = test(model, test_ds)
test2_acc, reses, test3_acc, reses3 = test23(model, test_ds)
print('test_acc1', test_acc)
print('avg_loss ', total_loss / total_batch)
print('test_acc2', test2_acc)
print('test_acc3', test3_acc)
for i in range(reses.shape[0]):
print('Cate %d ' % i, reses[i])
checkpoint_dir = './saved_models/'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
root = tfe.Checkpoint(optimizer=optimizer,
model=model,
optimizer_step=tf.train.get_or_create_global_step())
root.save(file_prefix=checkpoint_dir)
def run(self):
T = 0 # Worker step counter.
env = make_atari(self.config.env)
model = ActorCritic(env.action_space.n, policy=self.config.policy, device=self.config.device)
loader = tfe.Checkpoint(model=model.policy)
obs, act, rew = [], [], []
ob = env.reset()
done = False
s = model.policy.s0
cum_reward = 0
ep_len = 0
while T < self.steps:
try:
loader.restore(tf.train.latest_checkpoint(self.config.save_dir))
except:
continue
t = 0 # Batch counter.
s_init = s
epsilon = 0.6 - (T / self.steps) * 0.5
while not done and t < self.batch_size:
logits, v, s = model.forward([ob], s)
probs = tf.nn.softmax(logits)
a = greedy(probs, env.action_space.n, epsilon=epsilon)
next_ob, r, done, _ = env.step(a)
obs.append(ob)
act.append(a)
rew.append(r)
ob = next_ob
t += 1
T += 1
cum_reward += r
ep_len += 1
d_rew = discount(rew, self.config.gamma)
d_rew = (d_rew - np.mean(d_rew)) / (np.std(d_rew) + 1e-6) # Stability constant.
grads, loss = model.gradient(obs, d_rew, act, s_init)
grads, _ = tf.clip_by_global_norm(grads, self.config.max_norm)
if done:
print(f"Step: {T}, Len: {ep_len}, BR: {cum_reward}, TL: {loss:.4f}, Epsilon: {epsilon:.2f}")
s = model.policy.s0
done = False
ob = env.reset()
cum_reward = 0
ep_len = 0
obs.clear()
act.clear()
rew.clear()
for i in range(len(grads)):
grads[i] = grads[i].numpy()
self.queue.put(grads)
def restore_model(model, optimizer):
# model是重新初始化的
# 指定checkpoint目录
checkpoint_directory = 'models_checkpoints/SimpleNN/'
# Create model checkpoint
checkpoint = tfe.Checkpoint(
optimizer=optimizer,
model=model,
optimizer_step=tf.train.get_or_create_global_step())
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_directory))
def restore_checkpoint(self, model):
"""
Load a given checkpoint.
"""
# Create a checkpoint
self.checkpoint = tfe.Checkpoint(optimizer=self.create_optimizer(),
model=model.arch)
# Restore the checkpoint
self.checkpoint.restore(self.p.ckpt_path)
def initialize_process():
global env, model, loader, config, logger
config = Config("config/config.json")
env = make_atari(config.env)
# env = gym.make(config.env)
model = ActorCritic(env.action_space.n, policy=config.policy)
loader = tfe.Checkpoint(
model=model.policy,
optimizer_step=tf.train.get_or_create_global_step())
logger = Logger("Worker_{}".format(os.getpid()))
def sample(queue, env_name, steps):
env = make_atari(env_name)
model = CRPolicy(env.action_space.n)
loader = tfe.Checkpoint(model=model)
for roll in range(steps):
# TODO: REMOVE THIS CRAP WHEN YOU FIX IT
try:
loader.restore(tf.train.latest_checkpoint(CKPT_DIR))
except:
continue
obs, act, rews = [], [], []
ob = env.reset()
done = False
s = model.s0
while not done:
logits, v, s = model([ob], s)
probs = tf.nn.softmax(logits)
a = boltzmann(probs, env.action_space.n)
next_ob, r, done, _ = env.step(a)
obs.append(ob)
act.append(a)
rews.append(r)
ob = next_ob
d_rews = discount(rews, GAMMA)
d_rew = (d_rews - np.mean(d_rews)) / (np.std(d_rews) + 1e-6)
with tf.GradientTape() as tape:
logits, values, _ = model(obs, model.s0)
values = tf.squeeze(values)
advs = tf.constant(d_rew, dtype=tf.float32) - values
policy = tf.nn.softmax(logits)
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=act, logits=logits)
p_loss = xentropy * tf.stop_gradient(advs)
v_loss = tf.square(advs)
e_loss = tf.nn.softmax_cross_entropy_with_logits_v2(labels=policy,
logits=logits)
loss = tf.reduce_mean(p_loss + 0.5 * v_loss - 0.01 * e_loss)
grads = tape.gradient(loss, model.trainable_weights)
grads, _ = tf.clip_by_global_norm(grads, MAX_GRAD_NORM)
print("Step: {0}, Len: {1} BR: {2}, TL: {3:.4f}".format(
roll, len(obs), np.sum(rews), loss))
for i in range(len(grads)):
grads[i] = grads[i].numpy()
queue.put(grads)
def fit_and_save(model, optimizer, input_data, target):
model.fit(input_data, target, optimizer, num_epochs=500, verbose=50)
# Specify checkpoint directory
checkpoint_directory = 'models_checkpoints/SimpleNN/'
# Create model checkpoint
checkpoint = tfe.Checkpoint(
optimizer=optimizer,
model=model,
optimizer_step=tf.train.get_or_create_global_step())
# Save trained model
checkpoint.save(file_prefix=checkpoint_directory)
def build_model(self, initializer=tf.zeros):
self.model = AtariModel(self.obs_spec["screen"][1],
self.obs_spec["minimap"][1],
possible_action_num)
# TODO: Training
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
self.root = tfe.Checkpoint(
optimizer=optimizer,
model=self.model,
optimizer_step=tf.train.get_or_create_global_step())
def create_checkpoint(self, name):
if name:
checkpoint = tfe.Checkpoint(**self.get_str2weights())
checkpoint_dir = "./model/" + name
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
try:
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
except Exception as e:
print(e)
return checkpoint, checkpoint_prefix
else:
return None, None
def main(_):
pp = pprint.PrettyPrinter()
pp.pprint(flags.FLAGS.__flags)
filenames = glob.glob(data_dir)
(device, data_format) = ('/gpu:0', 'channels_first')
if FLAGS.no_gpu or tfe.num_gpus() <= 0:
(device, data_format) = ('/cpu:0', 'channels_last')
print('Using device %s, and data format %s.' % (device, data_format))
if not os.path.exists(FLAGS.checkpoint_dir):
os.makedirs(FLAGS.checkpoint_dir)
if not os.path.exists(FLAGS.sample_dir):
os.makedirs(FLAGS.sample_dir)
model_objects = {
'generator': Generator(data_format),
'discriminator': Discriminator(data_format),
'generator_optimizer': tf.train.AdamOptimizer(FLAGS.generator_learning_rate, FLAGS.beta1, FLAGS.beta2),
'discriminator_optimizer': tf.train.AdamOptimizer(FLAGS.discriminator_learning_rate, FLAGS.beta1, FLAGS.beta2),
'step_counter': tf.train.get_or_create_global_step()
}
summary_writer = tf.contrib.summary.create_file_writer(FLAGS.summary_dir,
flush_millis=1000)
checkpoint = tfe.Checkpoint(**model_objects)
checkpoint_prefix = os.path.join(FLAGS.checkpoint_dir, 'ckpt')
latest_cpkt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if latest_cpkt:
print('Using latest checkpoint at ' + latest_cpkt)
checkpoint.restore(latest_cpkt)
dataset = tf.data.TFRecordDataset(
filenames).map(read_and_decode_with_labels)
dataset = dataset.shuffle(10000).apply(
tf.contrib.data.batch_and_drop_remainder(FLAGS.batch_size))
with tf.device(device):
for epoch in range(FLAGS.epoch):
start = time.time()
with summary_writer.as_default():
train_one_epoch(dataset=dataset, batch_size=FLAGS.batch_size, log_interval=FLAGS.log_interval,
z_dim=FLAGS.z_dim, device=device, epoch=epoch, **model_objects)
end = time.time()
checkpoint.save(checkpoint_prefix)
print('\nTrain time for epoch #%d (step %d): %f' %
(checkpoint.save_counter.numpy(),
checkpoint.step_counter.numpy(),
end - start))
def __init__(self, cfg, net, testset):
self.cfg = cfg
self.net = net
self.testset = testset
# Restore the model
self.optimizer = tf.train.MomentumOptimizer(
learning_rate=self.cfg.LEARNING_RATE, momentum=self.cfg.MOMENTUM)
self.checkpoint_dir = self.cfg.CKPT_PATH
self.checkpoint_encoder = os.path.join(self.checkpoint_dir, 'Model')
self.root1 = tfe.Checkpoint(
optimizer=self.optimizer,
model=self.net,
optimizer_step=tf.train.get_or_create_global_step())
self.root1.restore(tf.train.latest_checkpoint(self.checkpoint_dir))
def main():
args = setup_args()
log_msg(args)
vocab_table = lookup_ops.index_table_from_file(args.vocab, default_value=args.unk_index)
train_dataset = create_dataset(args.train, vocab_table, args.bs, args.eos, args.t)
valid_dataset = create_dataset(args.valid, vocab_table, args.bs, args.eos, args.t)
loss_and_grads_fun = tfe.implicit_value_and_gradients(train_loss)
lm = LanguageModel(int(vocab_table.size()), d=args.nd, h=args.nh, cell=args.cell)
log_msg('Model built!')
best_valid_ppl = compute_ppl(lm, valid_dataset)
log_msg(f'Start ppl: {best_valid_ppl: 0.4f}')
if args.opt == 'adam':
opt = tf.train.AdamOptimizer(args.lr)
else:
opt = tf.train.GradientDescentOptimizer(args.lr)
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
ckpt_prefix = os.path.join(args.save_dir, args.ckpt_prefix)
root = tfe.Checkpoint(optimizer=opt, model=lm, optimizer_step=tf.train.get_or_create_global_step())
for epoch_num in range(args.num_epochs):
log_msg(f'Epoch: {epoch_num} START')
batch_loss = []
for step_num, train_datum in enumerate(train_dataset, start=1):
loss_value, gradients = loss_and_grads_fun(lm, train_datum)
batch_loss.append(loss_value)
if step_num % args.stats_step == 0:
log_msg(f'Epoch: {epoch_num} Step: {step_num} Avg Loss: {np.average(np.asarray(loss_value)): 0.4f}')
batch_loss = []
if step_num % args.eval_step == 0:
better, ppl = check_if_ppl_better(best_valid_ppl, lm, valid_dataset, root, ckpt_prefix, epoch_num, step_num)
if better:
best_valid_ppl = ppl
opt.apply_gradients(clip_gradients(gradients, args.clip_ratio))
log_msg(f'Epoch: {epoch_num} END')
better, ppl = check_if_ppl_better(best_valid_ppl, lm, valid_dataset, root, ckpt_prefix, epoch_num, step_num=-1)
if better:
best_valid_ppl = ppl
def test():
config = Config("config/config.json")
env = make_atari(config.env)
model = ActorCritic(env.action_space.n, policy=config.policy)
saver = tfe.Checkpoint(model=model.policy)
saver.restore(tf.train.latest_checkpoint(config.save_dir))
ob = env.reset()
s = model.policy.s0
while True:
env.render()
logits, _, s = model.forward([ob], s)
probs = tf.nn.softmax(logits)
a = greedy(probs)
ob, _, done, _ = env.step(a)
if done:
ob = env.reset()
def main(_):
(device, data_format) = ('/gpu:0', 'channels_first')
if FLAGS.no_gpu or tfe.num_gpus() <= 0:
(device, data_format) = ('/cpu:0', 'channels_last')
print('Using device %s, and data format %s.' % (device, data_format))
# Load the datasets
data = input_data.read_data_sets(FLAGS.data_dir)
dataset = (tf.data.Dataset.from_tensor_slices(
data.train.images).shuffle(60000).batch(FLAGS.batch_size))
# Create the models and optimizers.
model_objects = {
'generator': Generator(data_format),
'discriminator': Discriminator(data_format),
'generator_optimizer': tf.train.AdamOptimizer(FLAGS.lr),
'discriminator_optimizer': tf.train.AdamOptimizer(FLAGS.lr),
'step_counter': tf.train.get_or_create_global_step(),
}
# Prepare summary writer and checkpoint info
summary_writer = tf.contrib.summary.create_summary_file_writer(
FLAGS.output_dir, flush_millis=1000)
checkpoint_prefix = os.path.join(FLAGS.checkpoint_dir, 'ckpt')
latest_cpkt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
if latest_cpkt:
print('Using latest checkpoint at ' + latest_cpkt)
checkpoint = tfe.Checkpoint(**model_objects)
# Restore variables on creation if a checkpoint exists.
checkpoint.restore(latest_cpkt)
with tf.device(device):
for _ in range(100):
start = time.time()
with summary_writer.as_default():
train_one_epoch(dataset=dataset,
log_interval=FLAGS.log_interval,
noise_dim=FLAGS.noise,
**model_objects)
end = time.time()
checkpoint.save(checkpoint_prefix)
print('\nTrain time for epoch #%d (step %d): %f' %
(checkpoint.save_counter.numpy(),
checkpoint.step_counter.numpy(), end - start))
def freeze_model():
x = tf.random_normal((1, 300, 400, 3))
model = ConvModule(64, kernel_size=(3, 3))
adam = tf.train.AdamOptimizer()
checkpoint_prefix = os.path.join(flags.model_dir, 'ckpt')
global_step = tf.train.get_or_create_global_step()
y = model(x)
print("y:", y.shape)
checkpoint = tfe.Checkpoint(model=model,
optimizer=adam,
step_counter=global_step)
checkpoint.restore(tf.train.latest_checkpoint(flags.model_dir))
print("Global_step:", global_step)
checkpoint.save(checkpoint_prefix)
def train_model(train_file, validation_file, validation_interval, width, height, batch_size, n_epochs,
checkpoint_folder, training_device):
checkpoint_folder = os.path.join(checkpoint_folder, f'{width}x{height}')
training_generator = LSUNGenerator(train_file)
transform = LSUNTransform(image_dimensions=(height, width, 3))
encoder = Encoder()
decoder = Decoder()
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
checkpointer = tfe.Checkpoint(encoder=encoder, decoder=decoder, optimizer=optimizer)
best_loss = 1e10
for epoch in range(n_epochs):
iteration = 0
dataset = tf.data.Dataset.from_generator(generator=lambda: training_generator, output_types=tf.string) \
.map(transform).batch(batch_size)
for batch in dataset:
with tf.device(training_device):
loss, grads_and_vars = calculate_gradients(batch, encoder, decoder)
optimizer.apply_gradients(grads_and_vars)
iteration += 1
training_logger.info(f'Epoch = {epoch}, Iteration = {iteration}, Loss = {loss}')
if iteration % validation_interval == 0:
validation_logger.info(f'Epoch: {epoch}, Iteration: {iteration}. Beginning validation pass...')
validation_generator = LSUNGenerator(validation_file)
validation_dataset = tf.data.Dataset.from_generator(generator=lambda: validation_generator,
output_types=tf.string) \
.map(transform).batch(batch_size)
losses = list()
for val_batch in validation_dataset:
with tf.device(training_device):
val_batch = tf.constant(val_batch)
loss = evaluate(val_batch, encoder, decoder)
losses.append(loss)
losses = np.array(losses)
avg_loss = np.mean(losses)
min_loss = np.min(losses)
max_loss = np.max(losses)
std_loss = np.std(losses)
validation_logger.info(f'avg: {avg_loss}, std: {std_loss}, min: {min_loss}, max: {max_loss}')
if avg_loss < best_loss:
best_loss = avg_loss
validation_logger.info(
f'Validation loss is best seen so far. Checkpointing to {checkpoint_folder}...')
checkpointer.save(checkpoint_folder)
def train(self, steps=300, name=None):
"""
:param steps:
:param name:
:return: the loss history
"""
str2weights = {
str(key): value
for key, value in self.rule_weights.items()
}
if name:
checkpoint = tfe.Checkpoint(**str2weights)
checkpoint_dir = "./model/" + name
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
try:
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
except Exception as e:
print(e)
losses = []
optimizer = tf.train.RMSPropOptimizer(learning_rate=0.5)
for i in range(steps):
grads = self.grad()
optimizer.apply_gradients(
zip(grads, self.__all_variables()),
global_step=tf.train.get_or_create_global_step())
loss_avg = float(self.loss().numpy())
losses.append(loss_avg)
print("-" * 20)
print("step " + str(i) + " loss is " + str(loss_avg))
if i % 5 == 0:
self.show_definition()
valuation_dict = self.valuation2atoms(self.deduction()).items()
for atom, value in valuation_dict:
print(str(atom) + ": " + str(value))
if name:
checkpoint.save(checkpoint_prefix)
pd.Series(np.array(losses)).to_csv(name + ".csv")
print("-" * 20 + "\n")
return losses
def train():
mp.set_start_method('spawn', force=True)
config = Config("config/config.json")
env = make_atari(config.env)
# env = gym.make(config.env)
step = tf.train.get_or_create_global_step()
optimizer = tf.train.AdamOptimizer(learning_rate=config.lr)
model = ActorCritic(env.action_space.n, policy=config.policy)
saver = tfe.Checkpoint(optimizer=optimizer,
model=model.policy,
optimizer_step=step)
pool = mp.Pool(processes=config.processes, initializer=initialize_process)
saver.restore(tf.train.latest_checkpoint(config.save_dir))
logger = Logger('global')
# Initialize model.
model.forward([env.reset()])
ts = time.time()
for t in range(config.steps):
gradients = []
roll = pool.map(generate_gradients, [t] * config.processes)
for tup in zip(*roll):
averaged = np.mean(tup, axis=0)
gradients.append(tf.constant(averaged, dtype=tf.float32))
clipped, _ = tf.clip_by_global_norm(gradients, config.max_norm)
gnorms = [tf.norm(grad) for grad in clipped]
logger.log_gradients(gnorms)
logger.log_weights(model.policy.trainable_variables)
optimizer.apply_gradients(zip(clipped, model.policy.trainable_weights),
global_step=step)
saver.save(file_prefix=config.file_prefix)
print("Epoch took: {}".format(time.time() - ts))
ts = time.time()
def train(self, steps=6000, name="test"):
str2weights = {str(key):value for key,value in self.rule_weights.items()}
checkpoint = tfe.Checkpoint(**str2weights)
optimizer = tf.train.RMSPropOptimizer(learning_rate=0.5)
checkpoint_dir = "./model/"+name
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
try:
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
except Exception as e:
print(e)
for i in range(steps):
grads = self.grad()
optimizer.apply_gradients(zip(grads, self.rule_weights.values()),
global_step=tf.train.get_or_create_global_step())
loss_avg = self.loss()
print("-"*20)
print("step "+str(i)+" loss is "+str(loss_avg))
if i%5==0:
self.show_definition()
for atom, value in self.valuation2atoms(self.deduction()).items():
print(str(atom)+": "+str(value))
checkpoint.save(checkpoint_prefix)
print("-"*20+"\n")
def call_umm_segmentation(features, pad, contiguous, random_wins):
'''
Parameters
----------
list of features in size (128,201)
length of padding
number of contiguous segments
[(start,end)] for all the random windows
'''
model = CRNN.Model(utils.hidden_dim, utils.num_layers, utils.input_dim)
# load checkpoint
checkpoint_prefix = os.path.join(utils.model_dir, utils.model_name)
step_counter = tf.train.get_or_create_global_step()
checkpoint = tfe.Checkpoint(model=model, step_counter=step_counter)
if tf.train.checkpoint_exists(checkpoint_prefix):
checkpoint.restore(checkpoint_prefix)
norm_feats = normalization(tf.convert_to_tensor(features))
logit = model(norm_feats, training=False)
time_segments = compute_timeline(logit, pad, contiguous, random_wins)
return time_segments
def train_or_infer_spinn(embed,
word2index,
train_data,
dev_data,
test_data,
config):
"""Perform Training or Inference on a SPINN model.
Args:
embed: The embedding matrix as a float32 numpy array with shape
[vocabulary_size, word_vector_len]. word_vector_len is the length of a
word embedding vector.
word2index: A `dict` mapping word to word index.
train_data: An instance of `data.SnliData`, for the train split.
dev_data: Same as above, for the dev split.
test_data: Same as above, for the test split.
config: A configuration object. See the argument to this Python binary for
details.
Returns:
If `config.inference_premise ` and `config.inference_hypothesis` are not
`None`, i.e., inference mode: the logits for the possible labels of the
SNLI data set, as a `Tensor` of three floats.
else:
The trainer object.
Raises:
ValueError: if only one of config.inference_premise and
config.inference_hypothesis is specified.
"""
# TODO(cais): Refactor this function into separate one for training and
# inference.
use_gpu = tfe.num_gpus() > 0 and not config.force_cpu
device = "gpu:0" if use_gpu else "cpu:0"
print("Using device: %s" % device)
if ((config.inference_premise and not config.inference_hypothesis) or
(not config.inference_premise and config.inference_hypothesis)):
raise ValueError(
"--inference_premise and --inference_hypothesis must be both "
"specified or both unspecified, but only one is specified.")
if config.inference_premise:
# Inference mode.
inference_sentence_pair = [
data.encode_sentence(config.inference_premise, word2index),
data.encode_sentence(config.inference_hypothesis, word2index)]
else:
inference_sentence_pair = None
log_header = (
" Time Epoch Iteration Progress (%Epoch) Loss Dev/Loss"
" Accuracy Dev/Accuracy")
log_template = (
"{:>6.0f} {:>5.0f} {:>9.0f} {:>5.0f}/{:<5.0f} {:>7.0f}% {:>8.6f} {} "
"{:12.4f} {}")
dev_log_template = (
"{:>6.0f} {:>5.0f} {:>9.0f} {:>5.0f}/{:<5.0f} {:>7.0f}% {:>8.6f} "
"{:8.6f} {:12.4f} {:12.4f}")
summary_writer = tf.contrib.summary.create_file_writer(
config.logdir, flush_millis=10000)
with tf.device(device), \
summary_writer.as_default(), \
tf.contrib.summary.always_record_summaries():
model = SNLIClassifier(config, embed)
global_step = tf.train.get_or_create_global_step()
trainer = SNLIClassifierTrainer(model, config.lr)
checkpoint = tfe.Checkpoint(trainer=trainer, global_step=global_step)
checkpoint.restore(tf.train.latest_checkpoint(config.logdir))
if inference_sentence_pair:
# Inference mode.
prem, prem_trans = inference_sentence_pair[0]
hypo, hypo_trans = inference_sentence_pair[1]
hypo_trans = inference_sentence_pair[1][1]
inference_logits = model(
tf.constant(prem), tf.constant(prem_trans),
tf.constant(hypo), tf.constant(hypo_trans), training=False)
inference_logits = inference_logits[0][1:]
max_index = tf.argmax(inference_logits)
print("\nInference logits:")
for i, (label, logit) in enumerate(
zip(data.POSSIBLE_LABELS, inference_logits)):
winner_tag = " (winner)" if max_index == i else ""
print(" {0:<16}{1:.6f}{2}".format(label + ":", logit, winner_tag))
return inference_logits
train_len = train_data.num_batches(config.batch_size)
start = time.time()
iterations = 0
mean_loss = tfe.metrics.Mean()
accuracy = tfe.metrics.Accuracy()
print(log_header)
for epoch in xrange(config.epochs):
batch_idx = 0
for label, prem, prem_trans, hypo, hypo_trans in _get_dataset_iterator(
train_data, config.batch_size):
if use_gpu:
label, prem, hypo = label.gpu(), prem.gpu(), hypo.gpu()
# prem_trans and hypo_trans are used for dynamic control flow and can
# remain on CPU. Same in _evaluate_on_dataset().
iterations += 1
batch_train_loss, batch_train_logits = trainer.train_batch(
label, prem, prem_trans, hypo, hypo_trans)
batch_size = tf.shape(label)[0]
mean_loss(batch_train_loss.numpy(),
weights=batch_size.gpu() if use_gpu else batch_size)
accuracy(tf.argmax(batch_train_logits, axis=1), label)
if iterations % config.save_every == 0:
checkpoint.save(os.path.join(config.logdir, "ckpt"))
if iterations % config.dev_every == 0:
dev_loss, dev_frac_correct = _evaluate_on_dataset(
dev_data, config.batch_size, trainer, use_gpu)
print(dev_log_template.format(
time.time() - start,
epoch, iterations, 1 + batch_idx, train_len,
100.0 * (1 + batch_idx) / train_len,
mean_loss.result(), dev_loss,
accuracy.result() * 100.0, dev_frac_correct * 100.0))
tf.contrib.summary.scalar("dev/loss", dev_loss)
tf.contrib.summary.scalar("dev/accuracy", dev_frac_correct)
elif iterations % config.log_every == 0:
mean_loss_val = mean_loss.result()
accuracy_val = accuracy.result()
print(log_template.format(
time.time() - start,
epoch, iterations, 1 + batch_idx, train_len,
100.0 * (1 + batch_idx) / train_len,
mean_loss_val, " " * 8, accuracy_val * 100.0, " " * 12))
tf.contrib.summary.scalar("train/loss", mean_loss_val)
tf.contrib.summary.scalar("train/accuracy", accuracy_val)
# Reset metrics.
mean_loss = tfe.metrics.Mean()
accuracy = tfe.metrics.Accuracy()
batch_idx += 1
if (epoch + 1) % config.lr_decay_every == 0:
trainer.decay_learning_rate(config.lr_decay_by)
test_loss, test_frac_correct = _evaluate_on_dataset(
test_data, config.batch_size, trainer, use_gpu)
print("Final test loss: %g; accuracy: %g%%" %
(test_loss, test_frac_correct * 100.0))
return trainer
from __future__ import absolute_import, division, print_function
import os
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.contrib.eager as tfe
tf.enable_eager_execution()
from iris import Iris
iris = Iris()
model = iris.model
optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
checkpoint_dir = './iris_model'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
root = tfe.Checkpoint(optimizer=optimizer,
model=model,
optimizer_step=tf.train.get_or_create_global_step())
root.restore(tf.train.latest_checkpoint(checkpoint_dir))
iris.test()
def main():
open('output_summary.csv', 'w').close()
# Constants variables
NUM_TRAIN_SAMPLES = 72485
NUM_TEST_SAMPLES = 26528
# Editable variables
num_labeled_samples = 5126
num_validation_samples = 0
batch_size = 25
epochs = 200
max_learning_rate = 0.003
initial_beta1 = 0.9
final_beta1 = 0.5
checkpoint_directory = './checkpoints/PiModel'
tensorboard_logs_directory = './logs/PiModel'
# Assign it as tfe.variable since we will change it across epochs
learning_rate = tfe.Variable(max_learning_rate)
beta_1 = tfe.Variable(initial_beta1)
outputArr = np.array([])
# Download and Save Dataset in Tfrecords
#loader = SvnhLoader('./data', NUM_TRAIN_SAMPLES,
# num_validation_samples, num_labeled_samples)
#loader.download_images_and_generate_tf_record()
loader = FnLoader('./fn_data', NUM_TRAIN_SAMPLES, num_validation_samples,
num_labeled_samples)
# print ("hello")
loader.download_images_and_generate_tf_record()
#sys.exit()
# Generate data loaders
train_labeled_iterator, train_unlabeled_iterator, validation_iterator, test_iterator = loader.load_dataset(
batch_size, epochs)
#print (train_labeled_iterator)
batches_per_epoch = int(num_labeled_samples / batch_size)
batches_per_epoch_val = int(num_validation_samples / batch_size)
# sys.exit()
model = PiModel()
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=beta_1,
beta2=0.999)
max_unsupervised_weight = 100 * num_labeled_samples / \
(NUM_TRAIN_SAMPLES - num_validation_samples)
best_val_accuracy = 0
global_step = tf.train.get_or_create_global_step()
writer = tf.contrib.summary.create_file_writer(tensorboard_logs_directory)
writer.set_as_default()
#sys.exit()
for epoch in range(epochs):
rampdown_value = ramp_down_function(epoch, epochs)
rampup_value = ramp_up_function(epoch)
if epoch == 0:
unsupervised_weight = 0
else:
unsupervised_weight = max_unsupervised_weight * \
rampup_value
learning_rate.assign(rampup_value * rampdown_value * max_learning_rate)
beta_1.assign(rampdown_value * initial_beta1 +
(1.0 - rampdown_value) * final_beta1)
epoch_loss_avg = tfe.metrics.Mean()
epoch_accuracy = tfe.metrics.Accuracy()
epoch_loss_avg_val = tfe.metrics.Mean()
epoch_accuracy_val = tfe.metrics.Accuracy()
for batch_nr in range(batches_per_epoch):
X_labeled_train, y_labeled_train = train_labeled_iterator.get_next(
)
#print(y_labeled_train[0:20,0])
#print(y_labeled_train[0:20,1])
#print(y_labeled_train.shape)
X_unlabeled_train, _ = train_unlabeled_iterator.get_next()
loss_val, grads = pi_model_gradients(X_labeled_train,
y_labeled_train,
X_unlabeled_train, model,
unsupervised_weight)
optimizer.apply_gradients(zip(grads, model.variables),
global_step=global_step)
#sys.exit()
epoch_loss_avg(loss_val)
#print(X_labeled_train)
num_test_batches = int(NUM_TEST_SAMPLES / batch_size)
pred = model(X_labeled_train)
#sys.exit()
outputArr = np.array([])
epoch_accuracy(tf.argmax(pred, 1), tf.argmax(y_labeled_train, 1))
if (batch_nr == batches_per_epoch - 1):
for test_batch in range(num_test_batches):
X_val, y_val = test_iterator.get_next()
y_val_predictions = model(X_val, training=False)
y_pred = tf.argmax(y_val_predictions, 1)
y_true = tf.argmax(y_val, 1)
y_pred_epoch = np.asarray(y_pred)
y_true_epoch = np.asarray(y_true)
#print(y_pred, y_true)
prec_epch = sk.metrics.precision_score(
y_true_epoch, y_pred_epoch)
rec_epch = sk.metrics.recall_score(y_true_epoch,
y_pred_epoch)
f1_epch = sk.metrics.f1_score(y_true_epoch, y_pred_epoch)
epoch_loss_avg_val(
tf.losses.softmax_cross_entropy(
y_val, y_val_predictions))
epoch_accuracy_val(tf.argmax(y_val_predictions, 1),
tf.argmax(y_val, 1))
#value1 = epoch+1
#value2 = epoch_accuracy.result()
#value3 =
#value4 =
#value5 =
#value6 =
#arrResult = [epoch+1, epoch_accuracy.result(), epoch_accuracy_val, a, b, c ]
arrResult = "{:03d}, {:02.6%}, {:02.6%}, {:.4%}, {:.4%}, {:.4%} ".format(
epoch + 1, epoch_accuracy.result(), epoch_accuracy_val.result(),
prec_epch, rec_epch, f1_epch)
out = open('output_summary.csv', 'a+')
out.write(arrResult + '\n')
#writef = csv.writer(out, delimiter=' ')
#writef.writerow(arrResult)
# print("Epoch {:03d}/{:03d}: Train Loss: {:9.7f}, Train Accuracy: {:02.6%}, Validation Loss: {:9.7f}, "
# "Validation Accuracy: {:02.6%}, lr={:.9f}, unsupervised weight={:5.3f}, beta1={:.9f}".format(epoch+1,
# epochs,
# epoch_loss_avg.result(),
# epoch_accuracy.result(),
# epoch_loss_avg_val.result(),
# epoch_accuracy_val.result(),
# learning_rate.numpy(),
# unsupervised_weight,
# beta_1.numpy()))
print(
"Epoch {:03d}/{:03d}: Train Loss: {:9.7f}, Train Accuracy: {:02.6%}, lr={:.9f}, unsupervised weight={:5.3f}, beta1={:.9f}"
.format(epoch + 1, epochs, epoch_loss_avg.result(),
epoch_accuracy.result(), learning_rate.numpy(),
unsupervised_weight, beta_1.numpy()))
print(epoch_accuracy_val)
#print (epoch_accuracy.result())
# If the accuracy of validation improves save a checkpoint Best 85%
if best_val_accuracy < epoch_accuracy.result():
best_val_accuracy = epoch_accuracy.result()
checkpoint = tfe.Checkpoint(optimizer=optimizer,
model=model,
optimizer_step=global_step)
checkpoint.save(file_prefix=checkpoint_directory)
# Record summaries
#with tf.contrib.summary.record_summaries_every_n_global_steps(1):
# tf.contrib.summary.scalar('Train Loss', epoch_loss_avg.result())
# tf.contrib.summary.scalar(
# 'Train Accuracy', epoch_accuracy.result())
# tf.contrib.summary.scalar(
# 'Validation Loss', epoch_loss_avg_val.result())
# tf.contrib.summary.scalar(
# 'Validation Accuracy', epoch_accuracy_val.result())
# tf.contrib.summary.scalar(
# 'Unsupervised Weight', unsupervised_weight)
# tf.contrib.summary.scalar('Learning Rate', learning_rate.numpy())
# tf.contrib.summary.scalar('Ramp Up Function', rampup_value)
# tf.contrib.summary.scalar('Ramp Down Function', rampdown_value)
#print('\nTrain Ended! Best Validation accuracy = {}\n'.format(best_val_accuracy))
#sys.exit()
# Load the best model
root = tfe.Checkpoint(optimizer=optimizer,
model=model,
optimizer_step=tf.train.get_or_create_global_step())
root.restore(tf.train.latest_checkpoint(checkpoint_directory))
# Evaluate on the final test set
#num_test_batches = NUM_TEST_SAMPLES/batch_size
test_accuracy = tfe.metrics.Accuracy()
#recall_eval = tf.metrics.recall(y_test_predictions, y_test)
#precision_eval = tf.metrics.precision(y_test_predictions, y_test)
for test_batch in range(int(num_test_batches)):
X_test, y_test = test_iterator.get_next()
#print(y_test[0:20,1])
y_test_predictions = model(X_test, training=False)
test_accuracy(tf.argmax(y_test_predictions, 1), tf.argmax(y_test, 1))
y_pred = tf.argmax(y_test_predictions, 1)
y_true = tf.argmax(y_test, 1)
y_pred = np.asarray(y_pred)
y_true = np.asarray(y_true)
#print(y_pred, y_true)
a = sk.metrics.precision_score(y_true, y_pred)
b = sk.metrics.recall_score(y_true, y_pred)
c = sk.metrics.f1_score(y_true, y_pred)
print("Precision", a)
print("Recall", b)
print("f1_score", c)
#print ("confusion_matrix")
#print (sk.metrics.confusion_matrix(y_true, y_pred))
#fpr, tpr, tresholds = sk.metrics.roc_curve(y_true, y_pred)
#precision_eval = tf.metrics.precision(y_test_predictions, y_test)
#precision_eval = tf.contrib.metrics.precision_at_recall(tf.argmax(y_test_predictions, 1), tf.argmax(y_test, 1), 1)
print(tf.argmax(y_test_predictions))
print(tf.argmax(y_test))
#f1_score(y_test_predictions, y_test, average='macro')
print("Final Test Accuracy: {:.6%}".format(test_accuracy.result()))
