def train(loss, input_x, x_data, max_epoch, batch_size, valid_portion):
train_x, valid_x = split_numpy_array(x_data, portion=valid_portion)
train_flow = DataFlow.arrays([train_x],
batch_size=batch_size,
shuffle=True,
skip_incomplete=True)
valid_flow = DataFlow.arrays([valid_x], batch_size=batch_size)
# derive the optimizer
learning_rate = tf.placeholder(shape=(), dtype=tf.float32)
learning_rate_var = AnnealingDynamicValue(0.001, 0.99995)
params = tf.trainable_variables()
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss, var_list=params)
# run the training loop
with TrainLoop(params, max_epoch=max_epoch, early_stopping=True) as loop:
trainer = LossTrainer(loop, loss, train_op, [input_x], train_flow)
trainer.anneal_after_steps(learning_rate_var, freq=1)
trainer.validate_after_epochs(Validator(loop, loss, [input_x],
valid_flow),
freq=1)
trainer.after_epochs.add_hook(
lambda: trainer.loop.collect_metrics(lr=learning_rate_var), freq=1)
trainer.log_after_epochs(freq=1)
trainer.run(feed_dict={learning_rate: learning_rate_var})
def main():
# load mnist data
(x_train, y_train), (x_test, y_test) = \
load_mnist(shape=[784], dtype=np.float32, normalize=True)
# input placeholders
input_x = tf.placeholder(
dtype=tf.float32, shape=(None,) + x_train.shape[1:], name='input_x')
input_y = tf.placeholder(
dtype=tf.int32, shape=[None], name='input_y')
is_training = tf.placeholder(
dtype=tf.bool, shape=(), name='is_training')
learning_rate = tf.placeholder(shape=(), dtype=tf.float32)
learning_rate_var = AnnealingDynamicValue(config.initial_lr,
config.lr_anneal_factor)
# build the model
optimizer = tf.train.AdamOptimizer(learning_rate)
# derive the loss, output and accuracy
logits = model(input_x, is_training=is_training)
softmax_loss = softmax_classification_loss(logits, input_y)
loss = softmax_loss + regularization_loss()
y = softmax_classification_output(logits)
acc = classification_accuracy(y, input_y)
# derive the optimizer
params = tf.trainable_variables()
grads = optimizer.compute_gradients(loss, var_list=params)
with tf.control_dependencies(
tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
train_op = optimizer.apply_gradients(grads)
# prepare for training and testing data
train_flow = DataFlow.arrays(
[x_train, y_train], config.batch_size, shuffle=True,
skip_incomplete=True
)
test_flow = DataFlow.arrays([x_test, y_test], config.batch_size)
with create_session().as_default():
# train the network
with TrainLoop(params,
max_epoch=config.max_epoch,
summary_dir=results.make_dir('train_summary'),
summary_graph=tf.get_default_graph(),
summary_commit_freqs={'loss': 10, 'acc': 10},
early_stopping=False) as loop:
trainer = Trainer(
loop, train_op, [input_x, input_y], train_flow,
feed_dict={learning_rate: learning_rate_var, is_training: True},
metrics={'loss': loss, 'acc': acc}
)
anneal_after(
trainer, learning_rate_var, epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq
)
evaluator = Evaluator(
loop,
metrics={'test_acc': acc},
inputs=[input_x, input_y],
data_flow=test_flow,
feed_dict={is_training: False},
time_metric_name='test_time'
)
trainer.evaluate_after_epochs(evaluator, freq=5)
trainer.log_after_epochs(freq=1)
trainer.run()
# save test result
results.commit(evaluator.last_metrics_dict)
def main():
logging.basicConfig(
level='INFO',
format='%(asctime)s [%(levelname)s] %(name)s: %(message)s')
# load mnist data
(x_train, y_train), (x_test, y_test) = \
load_mnist(shape=[config.x_dim], dtype=np.float32, normalize=True)
# input placeholders
input_x = tf.placeholder(dtype=tf.int32,
shape=(None, ) + x_train.shape[1:],
name='input_x')
is_training = tf.placeholder(dtype=tf.bool, shape=(), name='is_training')
learning_rate = tf.placeholder(shape=(), dtype=tf.float32)
learning_rate_var = AnnealingDynamicValue(config.initial_lr,
config.lr_anneal_factor)
multi_gpu = MultiGPU(disable_prebuild=False)
# build the model
grads = []
losses = []
lower_bounds = []
test_nlls = []
batch_size = get_batch_size(input_x)
params = None
optimizer = tf.train.AdamOptimizer(learning_rate)
for dev, pre_build, [dev_input_x
] in multi_gpu.data_parallel(batch_size, [input_x]):
with tf.device(dev), multi_gpu.maybe_name_scope(dev):
if pre_build:
with arg_scope([p_net, q_net], is_training=is_training):
_ = q_net(dev_input_x).chain(p_net,
latent_names=['z'],
observed={'x': dev_input_x})
else:
with arg_scope([q_net, p_net], is_training=is_training):
# derive the loss and lower-bound for training
train_q_net = q_net(dev_input_x)
train_chain = train_q_net.chain(
p_net,
latent_names=['z'],
latent_axis=0,
observed={'x': dev_input_x})
dev_vae_loss = tf.reduce_mean(
train_chain.vi.training.sgvb())
dev_loss = dev_vae_loss + regularization_loss()
dev_lower_bound = -dev_vae_loss
losses.append(dev_loss)
lower_bounds.append(dev_lower_bound)
# derive the nll and logits output for testing
test_q_net = q_net(dev_input_x, n_z=config.test_n_z)
test_chain = test_q_net.chain(p_net,
latent_names=['z'],
latent_axis=0,
observed={'x': dev_input_x})
dev_test_nll = -tf.reduce_mean(
test_chain.vi.evaluation.is_loglikelihood())
test_nlls.append(dev_test_nll)
# derive the optimizer
params = tf.trainable_variables()
grads.append(
optimizer.compute_gradients(dev_loss, var_list=params))
# merge multi-gpu outputs and operations
[loss, lower_bound, test_nll] = \
multi_gpu.average([losses, lower_bounds, test_nlls], batch_size)
train_op = multi_gpu.apply_grads(grads=multi_gpu.average_grads(grads),
optimizer=optimizer,
control_inputs=tf.get_collection(
tf.GraphKeys.UPDATE_OPS))
# derive the plotting function
work_dev = multi_gpu.work_devices[0]
with tf.device(work_dev), tf.name_scope('plot_x'):
plot_p_net = p_net(n_z=100, is_training=is_training)
x = tf.cast(255 * tf.sigmoid(plot_p_net['x'].distribution.logits),
dtype=tf.uint8)
x_plots = tf.reshape(x, [-1, 28, 28])
def plot_samples(loop):
with loop.timeit('plot_time'):
images = session.run(x_plots, feed_dict={is_training: False})
save_images_collection(images=images,
filename=results.prepare_parent(
'plotting/{}.png'.format(loop.epoch)),
grid_size=(10, 10))
# prepare for training and testing data
def input_x_sampler(x):
return session.run([sampled_x], feed_dict={sample_input_x: x})
with tf.device('/device:CPU:0'):
sample_input_x = tf.placeholder(dtype=tf.float32,
shape=(None, config.x_dim),
name='sample_input_x')
sampled_x = sample_from_probs(sample_input_x)
train_flow = DataFlow.arrays([x_train],
config.batch_size,
shuffle=True,
skip_incomplete=True).map(input_x_sampler)
test_flow = DataFlow.arrays([x_test], config.test_batch_size). \
map(input_x_sampler)
with create_session().as_default() as session, \
train_flow.threaded(5) as train_flow:
# fix the testing flow, reducing the testing time
test_flow = test_flow.to_arrays_flow(batch_size=config.test_batch_size)
# train the network
with TrainLoop(params,
var_groups=['p_net', 'q_net', 'posterior_flow'],
max_epoch=config.max_epoch,
summary_dir=results.make_dir('train_summary'),
summary_graph=tf.get_default_graph(),
early_stopping=False) as loop:
trainer = Trainer(loop,
train_op, [input_x],
train_flow,
feed_dict={
learning_rate: learning_rate_var,
is_training: True
},
metrics={'loss': loss})
anneal_after(trainer,
learning_rate_var,
epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq)
evaluator = Evaluator(loop,
metrics={
'test_nll': test_nll,
'test_lb': lower_bound
},
inputs=[input_x],
data_flow=test_flow,
feed_dict={is_training: False},
time_metric_name='test_time')
evaluator.after_run.add_hook(
lambda: results.commit(evaluator.last_metrics_dict))
trainer.evaluate_after_epochs(evaluator, freq=10)
trainer.evaluate_after_epochs(functools.partial(
plot_samples, loop),
freq=10)
trainer.log_after_epochs(freq=1)
trainer.run()
# write the final test_nll and test_lb
results.commit_and_print(evaluator.last_metrics_dict)
def main(config, result_dir):
# print the config
print_with_title('Configurations', config.format_config(), after='\n')
# open the result object and prepare for result directories
results = MLResults(result_dir)
results.make_dirs('plotting', exist_ok=True)
results.make_dirs('train_summary', exist_ok=True)
# input placeholders
input_x = tf.placeholder(dtype=tf.int32,
shape=(None, config.x_dim),
name='input_x')
is_training = tf.placeholder(dtype=tf.bool, shape=(), name='is_training')
learning_rate = tf.placeholder(shape=(), dtype=tf.float32)
learning_rate_var = AnnealingDynamicValue(config.initial_lr,
config.lr_anneal_factor)
multi_gpu = MultiGPU(disable_prebuild=False)
# build the model
grads = []
losses = []
test_nlls = []
test_lbs = []
batch_size = get_batch_size(input_x)
params = None
optimizer = tf.train.AdamOptimizer(learning_rate)
for dev, pre_build, [dev_input_x
] in multi_gpu.data_parallel(batch_size, [input_x]):
with tf.device(dev), multi_gpu.maybe_name_scope(dev):
if pre_build:
with arg_scope([p_net, q_net],
is_training=is_training,
channels_last=True):
_ = q_net(dev_input_x).chain(p_net,
latent_names=['z'],
observed={'x': dev_input_x})
else:
with arg_scope([p_net, q_net],
is_training=is_training,
channels_last=multi_gpu.channels_last(dev)):
# derive the loss and lower-bound for training
train_q_net = q_net(dev_input_x)
train_chain = train_q_net.chain(
p_net,
latent_names=['z'],
latent_axis=0,
observed={'x': dev_input_x})
dev_vae_loss = tf.reduce_mean(
train_chain.vi.training.sgvb())
dev_loss = dev_vae_loss + regularization_loss()
losses.append(dev_loss)
# derive the nll and logits output for testing
test_q_net = q_net(dev_input_x, n_z=config.test_n_z)
test_chain = test_q_net.chain(p_net,
latent_names=['z'],
latent_axis=0,
observed={'x': dev_input_x})
dev_test_nll = -tf.reduce_mean(
test_chain.vi.evaluation.is_loglikelihood())
dev_test_lb = tf.reduce_mean(
test_chain.vi.lower_bound.elbo())
test_nlls.append(dev_test_nll)
test_lbs.append(dev_test_lb)
# derive the optimizer
params = tf.trainable_variables()
grads.append(
optimizer.compute_gradients(dev_loss, var_list=params))
# merge multi-gpu outputs and operations
[loss, test_lb, test_nll] = \
multi_gpu.average([losses, test_lbs, test_nlls], batch_size)
train_op = multi_gpu.apply_grads(grads=multi_gpu.average_grads(grads),
optimizer=optimizer,
control_inputs=tf.get_collection(
tf.GraphKeys.UPDATE_OPS))
# derive the plotting function
work_dev = multi_gpu.work_devices[0]
with tf.device(work_dev), tf.name_scope('plot_x'):
plot_p_net = p_net(n_z=100,
is_training=is_training,
channels_last=multi_gpu.channels_last(work_dev))
x_plots = tf.reshape(bernoulli_as_pixel(plot_p_net['x']), (-1, 28, 28))
def plot_samples(loop):
with loop.timeit('plot_time'):
images = session.run(x_plots, feed_dict={is_training: False})
save_images_collection(images=images,
filename='plotting/{}.png'.format(
loop.epoch),
grid_size=(10, 10),
results=results)
# prepare for training and testing data
(x_train, y_train), (x_test, y_test) = load_mnist()
train_flow = bernoulli_flow(x_train,
config.batch_size,
shuffle=True,
skip_incomplete=True)
test_flow = bernoulli_flow(x_test, config.test_batch_size, sample_now=True)
with create_session().as_default() as session, \
train_flow.threaded(5) as train_flow:
# train the network
with TrainLoop(params,
var_groups=['q_net', 'p_net'],
max_epoch=config.max_epoch,
max_step=config.max_step,
summary_dir=(results.system_path('train_summary')
if config.write_summary else None),
summary_graph=tf.get_default_graph(),
early_stopping=False) as loop:
trainer = Trainer(loop,
train_op, [input_x],
train_flow,
feed_dict={
learning_rate: learning_rate_var,
is_training: True
},
metrics={'loss': loss})
trainer.anneal_after(learning_rate_var,
epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq)
evaluator = Evaluator(loop,
metrics={
'test_nll': test_nll,
'test_lb': test_lb
},
inputs=[input_x],
data_flow=test_flow,
feed_dict={is_training: False},
time_metric_name='test_time')
evaluator.after_run.add_hook(
lambda: results.update_metrics(evaluator.last_metrics_dict))
trainer.evaluate_after_epochs(evaluator, freq=10)
trainer.evaluate_after_epochs(functools.partial(
plot_samples, loop),
freq=10)
trainer.log_after_epochs(freq=1)
trainer.run()
# print the final metrics and close the results object
print_with_title('Results', results.format_metrics(), before='\n')
results.close()
def main():
# load mnist data
(x_train, y_train), (x_test, y_test) = \
load_cifar10(dtype=np.float32, normalize=True)
print(x_train.shape)
# input placeholders
input_x = tf.placeholder(
dtype=tf.float32, shape=(None,) + x_train.shape[1:], name='input_x')
input_y = tf.placeholder(
dtype=tf.int32, shape=[None], name='input_y')
is_training = tf.placeholder(
dtype=tf.bool, shape=(), name='is_training')
learning_rate = tf.placeholder(shape=(), dtype=tf.float32)
learning_rate_var = AnnealingDynamicValue(config.initial_lr,
config.lr_anneal_factor)
multi_gpu = MultiGPU()
# build the model
grads = []
losses = []
y_list = []
acc_list = []
batch_size = get_batch_size(input_x)
params = None
optimizer = tf.train.AdamOptimizer(learning_rate)
for dev, pre_build, [dev_input_x, dev_input_y] in multi_gpu.data_parallel(
batch_size, [input_x, input_y]):
with tf.device(dev), multi_gpu.maybe_name_scope(dev):
if pre_build:
_ = model(dev_input_x, is_training, channels_last=True)
else:
# derive the loss, output and accuracy
dev_logits = model(
dev_input_x,
is_training=is_training,
channels_last=multi_gpu.channels_last(dev)
)
dev_softmax_loss = \
softmax_classification_loss(dev_logits, dev_input_y)
dev_loss = dev_softmax_loss + regularization_loss()
dev_y = softmax_classification_output(dev_logits)
dev_acc = classification_accuracy(dev_y, dev_input_y)
losses.append(dev_loss)
y_list.append(dev_y)
acc_list.append(dev_acc)
# derive the optimizer
params = tf.trainable_variables()
grads.append(
optimizer.compute_gradients(dev_loss, var_list=params))
# merge multi-gpu outputs and operations
[loss, acc] = multi_gpu.average([losses, acc_list], batch_size)
[y] = multi_gpu.concat([y_list])
train_op = multi_gpu.apply_grads(
grads=multi_gpu.average_grads(grads),
optimizer=optimizer,
control_inputs=tf.get_collection(tf.GraphKeys.UPDATE_OPS)
)
# prepare for training and testing data
train_flow = DataFlow.arrays(
[x_train, y_train], config.batch_size, shuffle=True,
skip_incomplete=True
)
test_flow = DataFlow.arrays([x_test, y_test], config.batch_size)
with create_session().as_default():
# train the network
with TrainLoop(params,
max_epoch=config.max_epoch,
summary_dir=results.make_dir('train_summary'),
summary_graph=tf.get_default_graph(),
summary_commit_freqs={'loss': 10, 'acc': 10},
early_stopping=False) as loop:
trainer = Trainer(
loop, train_op, [input_x, input_y], train_flow,
feed_dict={learning_rate: learning_rate_var, is_training: True},
metrics={'loss': loss, 'acc': acc}
)
anneal_after(
trainer, learning_rate_var, epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq
)
evaluator = Evaluator(
loop,
metrics={'test_acc': acc},
inputs=[input_x, input_y],
data_flow=test_flow,
feed_dict={is_training: False},
time_metric_name='test_time'
)
evaluator.after_run.add_hook(
lambda: results.commit(evaluator.last_metrics_dict))
trainer.evaluate_after_epochs(evaluator, freq=5)
trainer.log_after_epochs(freq=1)
trainer.run()
# save test result
results.commit_and_print(evaluator.last_metrics_dict)
def main(config, result_dir):
# print the config
print_with_title('Configurations', config.format_config(), after='\n')
# open the result object and prepare for result directories
results = MLResults(result_dir)
results.make_dirs('plotting', exist_ok=True)
results.make_dirs('train_summary', exist_ok=True)
# input placeholders
input_x = tf.placeholder(dtype=tf.int32,
shape=(None, config.x_dim),
name='input_x')
is_training = tf.placeholder(dtype=tf.bool, shape=(), name='is_training')
learning_rate = tf.placeholder(shape=(),
dtype=tf.float32,
name='learning_rate')
learning_rate_var = AnnealingDynamicValue(config.initial_lr,
config.lr_anneal_factor)
# build the model
with arg_scope([q_net, p_net], is_training=is_training):
# derive the loss and lower-bound for training
train_q_net = q_net(input_x, n_samples=config.train_n_samples)
train_chain = train_q_net.chain(p_net,
latent_names=['y', 'z'],
latent_axis=0,
observed={'x': input_x})
if config.train_n_samples is None:
baseline = reinforce_baseline_net(input_x)
vae_loss = tf.reduce_mean(
train_chain.vi.training.reinforce(baseline=baseline))
else:
vae_loss = tf.reduce_mean(train_chain.vi.training.vimco())
loss = vae_loss + regularization_loss()
# derive the nll and logits output for testing
test_q_net = q_net(input_x, n_samples=config.test_n_samples)
test_chain = test_q_net.chain(p_net,
latent_names=['y', 'z'],
latent_axis=0,
observed={'x': input_x})
test_nll = -tf.reduce_mean(test_chain.vi.evaluation.is_loglikelihood())
# derive the classifier via q(y|x)
q_y_given_x = tf.argmax(test_q_net['y'].distribution.logits, axis=-1)
# derive the optimizer
optimizer = tf.train.AdamOptimizer(learning_rate)
params = tf.trainable_variables()
grads = optimizer.compute_gradients(loss, var_list=params)
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
train_op = optimizer.apply_gradients(grads)
# derive the plotting function
with tf.name_scope('plot_x'):
plot_p_net = p_net(
observed={'y': tf.range(config.n_clusters, dtype=tf.int32)},
n_z=10,
is_training=is_training)
x_plots = tf.reshape(
tf.transpose(bernoulli_as_pixel(plot_p_net['x']), (1, 0, 2)),
(-1, 28, 28))
def plot_samples(loop):
with loop.timeit('plot_time'):
images = session.run(x_plots, feed_dict={is_training: False})
save_images_collection(images=images,
filename='plotting/{}.png'.format(
loop.epoch),
grid_size=(config.n_clusters, 10),
results=results)
# derive the final un-supervised classifier
c_classifier = ClusteringClassifier(config.n_clusters, 10)
def train_classifier(loop):
df = bernoulli_flow(x_train,
config.batch_size,
shuffle=False,
skip_incomplete=False)
with loop.timeit('cls_train_time'):
[c_pred] = collect_outputs(outputs=[q_y_given_x],
inputs=[input_x],
data_flow=df,
feed_dict={is_training: False})
c_classifier.fit(c_pred, y_train)
print(c_classifier.describe())
def evaluate_classifier(loop):
with loop.timeit('cls_test_time'):
[c_pred] = collect_outputs(outputs=[q_y_given_x],
inputs=[input_x],
data_flow=test_flow,
feed_dict={is_training: False})
y_pred = c_classifier.predict(c_pred)
cls_metrics = {'test_acc': accuracy_score(y_test, y_pred)}
loop.collect_metrics(cls_metrics)
results.update_metrics(cls_metrics)
# prepare for training and testing data
(x_train, y_train), (x_test, y_test) = load_mnist()
train_flow = bernoulli_flow(x_train,
config.batch_size,
shuffle=True,
skip_incomplete=True)
test_flow = bernoulli_flow(x_test, config.test_batch_size, sample_now=True)
with create_session().as_default() as session, \
train_flow.threaded(5) as train_flow:
# train the network
with TrainLoop(params,
var_groups=['p_net', 'q_net', 'gaussian_mixture_prior'],
max_epoch=config.max_epoch,
max_step=config.max_step,
summary_dir=(results.system_path('train_summary')
if config.write_summary else None),
summary_graph=tf.get_default_graph(),
early_stopping=False) as loop:
trainer = Trainer(loop,
train_op, [input_x],
train_flow,
feed_dict={
learning_rate: learning_rate_var,
is_training: True
},
metrics={'loss': loss})
trainer.anneal_after(learning_rate_var,
epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq)
evaluator = Evaluator(loop,
metrics={'test_nll': test_nll},
inputs=[input_x],
data_flow=test_flow,
feed_dict={is_training: False},
time_metric_name='test_time')
evaluator.after_run.add_hook(
lambda: results.update_metrics(evaluator.last_metrics_dict))
trainer.evaluate_after_epochs(evaluator, freq=10)
trainer.evaluate_after_epochs(functools.partial(
plot_samples, loop),
freq=10)
trainer.evaluate_after_epochs(functools.partial(
train_classifier, loop),
freq=10)
trainer.evaluate_after_epochs(functools.partial(
evaluate_classifier, loop),
freq=10)
trainer.log_after_epochs(freq=1)
trainer.run()
# print the final metrics and close the results object
with codecs.open('cluster_classifier.txt', 'wb', 'utf-8') as f:
f.write(c_classifier.describe())
print_with_title('Results', results.format_metrics(), before='\n')
results.close()
def main(config, result_dir):
# print the config
print_with_title('Configurations', config.format_config(), after='\n')
# open the result object and prepare for result directories
results = MLResults(result_dir)
results.make_dirs('train_summary', exist_ok=True)
# input placeholders
input_x = tf.placeholder(dtype=tf.float32,
shape=(None, config.x_dim),
name='input_x')
input_y = tf.placeholder(dtype=tf.int32, shape=[None], name='input_y')
is_training = tf.placeholder(dtype=tf.bool, shape=(), name='is_training')
learning_rate = tf.placeholder(shape=(), dtype=tf.float32)
learning_rate_var = AnnealingDynamicValue(config.initial_lr,
config.lr_anneal_factor)
# derive the loss, output and accuracy
logits = model(input_x, is_training=is_training)
softmax_loss = softmax_classification_loss(logits, input_y)
loss = softmax_loss + regularization_loss()
y = softmax_classification_output(logits)
acc = classification_accuracy(y, input_y)
# derive the optimizer
optimizer = tf.train.AdamOptimizer(learning_rate)
params = tf.trainable_variables()
grads = optimizer.compute_gradients(loss, var_list=params)
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
train_op = optimizer.apply_gradients(grads)
# prepare for training and testing data
(x_train, y_train), (x_test, y_test) = \
load_cifar10(x_shape=(config.x_dim,), normalize_x=True)
train_flow = DataFlow.arrays([x_train, y_train],
config.batch_size,
shuffle=True,
skip_incomplete=True)
test_flow = DataFlow.arrays([x_test, y_test], config.test_batch_size)
with create_session().as_default():
# train the network
with TrainLoop(params,
max_epoch=config.max_epoch,
max_step=config.max_step,
summary_dir=(results.system_path('train_summary')
if config.write_summary else None),
summary_graph=tf.get_default_graph(),
early_stopping=False) as loop:
trainer = Trainer(loop,
train_op, [input_x, input_y],
train_flow,
feed_dict={
learning_rate: learning_rate_var,
is_training: True
},
metrics={
'loss': loss,
'acc': acc
})
trainer.anneal_after(learning_rate_var,
epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq)
evaluator = Evaluator(loop,
metrics={'test_acc': acc},
inputs=[input_x, input_y],
data_flow=test_flow,
feed_dict={is_training: False},
time_metric_name='test_time')
evaluator.after_run.add_hook(
lambda: results.update_metrics(evaluator.last_metrics_dict))
trainer.evaluate_after_epochs(evaluator, freq=5)
trainer.log_after_epochs(freq=1)
trainer.run()
# print the final metrics and close the results object
print_with_title('Results', results.format_metrics(), before='\n')
results.close()
def main():
# load mnist data
(x_train, y_train), (x_test, y_test) = \
load_mnist(shape=[config.x_dim], dtype=np.float32, normalize=True)
# input placeholders
input_x = tf.placeholder(dtype=tf.int32,
shape=(None, ) + x_train.shape[1:],
name='input_x')
is_training = tf.placeholder(dtype=tf.bool, shape=(), name='is_training')
learning_rate = tf.placeholder(shape=(), dtype=tf.float32)
learning_rate_var = AnnealingDynamicValue(config.initial_lr,
config.lr_anneal_factor)
multi_gpu = MultiGPU(disable_prebuild=False)
# build the model
vae = VAE(
p_z=Bernoulli(tf.zeros([1, config.z_dim])),
p_x_given_z=Bernoulli,
q_z_given_x=Bernoulli,
h_for_p_x=functools.partial(h_for_p_x, is_training=is_training),
h_for_q_z=functools.partial(h_for_q_z, is_training=is_training),
)
grads = []
losses = []
lower_bounds = []
test_nlls = []
batch_size = get_batch_size(input_x)
params = None
optimizer = tf.train.AdamOptimizer(learning_rate)
for dev, pre_build, [dev_input_x
] in multi_gpu.data_parallel(batch_size, [input_x]):
with tf.device(dev), multi_gpu.maybe_name_scope(dev):
if pre_build:
with arg_scope([h_for_q_z, h_for_p_x]):
_ = vae.chain(dev_input_x)
else:
# derive the loss and lower-bound for training
train_chain = vae.chain(dev_input_x)
dev_baseline = baseline_net(dev_input_x)
dev_cost, dev_baseline_cost = \
train_chain.vi.training.reinforce(baseline=dev_baseline)
dev_loss = regularization_loss() + \
tf.reduce_mean(dev_cost + dev_baseline_cost)
dev_lower_bound = \
tf.reduce_mean(train_chain.vi.lower_bound.elbo())
losses.append(dev_loss)
lower_bounds.append(dev_lower_bound)
# derive the nll and logits output for testing
test_chain = vae.chain(dev_input_x, n_z=config.test_n_z)
dev_test_nll = -tf.reduce_mean(
test_chain.vi.evaluation.is_loglikelihood())
test_nlls.append(dev_test_nll)
# derive the optimizer
params = tf.trainable_variables()
grads.append(
optimizer.compute_gradients(dev_loss, var_list=params))
# merge multi-gpu outputs and operations
[loss, lower_bound, test_nll] = \
multi_gpu.average([losses, lower_bounds, test_nlls], batch_size)
train_op = multi_gpu.apply_grads(grads=multi_gpu.average_grads(grads),
optimizer=optimizer,
control_inputs=tf.get_collection(
tf.GraphKeys.UPDATE_OPS))
# derive the plotting function
work_dev = multi_gpu.work_devices[0]
with tf.device(work_dev), tf.name_scope('plot_x'), \
arg_scope([h_for_q_z, h_for_p_x],
channels_last=multi_gpu.channels_last(work_dev)):
x_plots = tf.reshape(
tf.cast(255 *
tf.sigmoid(vae.model(n_z=100)['x'].distribution.logits),
dtype=tf.uint8), [-1, 28, 28])
def plot_samples(loop):
with loop.timeit('plot_time'):
session = get_default_session_or_error()
images = session.run(x_plots, feed_dict={is_training: False})
save_images_collection(images=images,
filename=results.prepare_parent(
'plotting/{}.png'.format(loop.epoch)),
grid_size=(10, 10))
# prepare for training and testing data
def input_x_sampler(x):
sess = get_default_session_or_error()
return sess.run([sampled_x], feed_dict={sample_input_x: x})
with tf.device('/device:CPU:0'):
sample_input_x = tf.placeholder(dtype=tf.float32,
shape=(None, config.x_dim),
name='sample_input_x')
sampled_x = sample_from_probs(sample_input_x)
train_flow = DataFlow.arrays([x_train],
config.batch_size,
shuffle=True,
skip_incomplete=True).map(input_x_sampler)
test_flow = DataFlow.arrays([x_test], config.test_batch_size). \
map(input_x_sampler)
with create_session().as_default():
# fix the testing flow, reducing the testing time
test_flow = test_flow.to_arrays_flow(batch_size=config.test_batch_size)
# train the network
with TrainLoop(params,
max_epoch=config.max_epoch,
summary_dir=results.make_dir('train_summary'),
summary_graph=tf.get_default_graph(),
early_stopping=False) as loop:
trainer = Trainer(loop,
train_op, [input_x],
train_flow,
feed_dict={
learning_rate: learning_rate_var,
is_training: True
},
metrics={'loss': loss})
anneal_after(trainer,
learning_rate_var,
epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq)
evaluator = Evaluator(loop,
metrics={
'test_nll': test_nll,
'test_lb': lower_bound
},
inputs=[input_x],
data_flow=test_flow,
feed_dict={is_training: False},
time_metric_name='test_time')
trainer.evaluate_after_epochs(evaluator, freq=10)
trainer.evaluate_after_epochs(functools.partial(
plot_samples, loop),
freq=10)
trainer.log_after_epochs(freq=1)
trainer.run()
# write the final test_nll and test_lb
results.commit(evaluator.last_metrics_dict)
def main():
logging.basicConfig(
level='INFO',
format='%(asctime)s [%(levelname)s] %(name)s: %(message)s')
# load mnist data
(x_train, y_train), (x_test, y_test) = \
load_mnist(shape=[config.x_dim], dtype=np.float32, normalize=True)
# input placeholders
input_x = tf.placeholder(dtype=tf.int32,
shape=(None, ) + x_train.shape[1:],
name='input_x')
is_training = tf.placeholder(dtype=tf.bool, shape=(), name='is_training')
learning_rate = tf.placeholder(shape=(),
dtype=tf.float32,
name='learning_rate')
learning_rate_var = AnnealingDynamicValue(config.initial_lr,
config.lr_anneal_factor)
tau_p = tf.placeholder(shape=(), dtype=tf.float32, name='tau_p')
tau_p_var = AnnealingDynamicValue(config.initial_tau_p,
config.tau_p_anneal_factor,
config.min_tau_p)
tau_q = tf.placeholder(shape=(), dtype=tf.float32, name='tau_q')
tau_q_var = AnnealingDynamicValue(config.initial_tau_q,
config.tau_q_anneal_factor,
config.min_tau_q)
multi_gpu = MultiGPU(disable_prebuild=False)
# build the model
grads = []
losses = []
test_nlls = []
y_given_x_list = []
batch_size = get_batch_size(input_x)
params = None
optimizer = tf.train.AdamOptimizer(learning_rate)
for dev, pre_build, [dev_input_x
] in multi_gpu.data_parallel(batch_size, [input_x]):
with tf.device(dev), multi_gpu.maybe_name_scope(dev):
if pre_build:
with arg_scope([q_net, p_net], is_training=is_training):
_ = q_net(dev_input_x).chain(p_net,
latent_names=['y', 'z'],
observed={'x': dev_input_x})
else:
with arg_scope([q_net, p_net], is_training=is_training):
# derive the loss and lower-bound for training
train_n_samples = (config.train_n_samples_for_concrete
if config.use_concrete_distribution else
config.train_n_samples)
train_q_net = q_net(dev_input_x,
n_samples=train_n_samples,
tau=tau_q)
train_chain = train_q_net.chain(
p_net,
latent_names=['y', 'z'],
latent_axis=0,
observed={'x': dev_input_x},
tau=tau_p)
if config.use_concrete_distribution:
if train_n_samples is None:
dev_vae_loss = tf.reduce_mean(
train_chain.vi.training.sgvb())
else:
dev_vae_loss = tf.reduce_mean(
train_chain.vi.training.iwae())
else:
if train_n_samples is None:
dev_baseline = reinforce_baseline_net(dev_input_x)
dev_vae_loss = tf.reduce_mean(
train_chain.vi.training.reinforce(
baseline=dev_baseline))
else:
dev_vae_loss = tf.reduce_mean(
train_chain.vi.training.vimco())
dev_loss = dev_vae_loss + regularization_loss()
dev_loss = add_p_z_given_y_reg_loss(dev_loss)
losses.append(dev_loss)
# derive the nll and logits output for testing
test_q_net = q_net(dev_input_x,
n_samples=config.test_n_samples)
test_chain = test_q_net.chain(p_net,
latent_names=['y', 'z'],
latent_axis=0,
observed={'x': dev_input_x})
dev_test_nll = -tf.reduce_mean(
test_chain.vi.evaluation.is_loglikelihood())
test_nlls.append(dev_test_nll)
# derive the classifier via q(y|x)
dev_q_y_given_x = tf.argmax(
test_q_net['y'].distribution.logits, axis=-1)
y_given_x_list.append(dev_q_y_given_x)
# derive the optimizer
params = tf.trainable_variables()
grads.append(
optimizer.compute_gradients(dev_loss, var_list=params))
# merge multi-gpu outputs and operations
[loss, test_nll] = \
multi_gpu.average([losses, test_nlls], batch_size)
[y_given_x] = multi_gpu.concat([y_given_x_list])
train_op = multi_gpu.apply_grads(grads=multi_gpu.average_grads(grads),
optimizer=optimizer,
control_inputs=tf.get_collection(
tf.GraphKeys.UPDATE_OPS))
# derive the plotting function
work_dev = multi_gpu.work_devices[0]
with tf.device(work_dev), tf.name_scope('plot_x'):
plot_p_net = p_net(
observed={'y': tf.range(config.n_clusters, dtype=tf.int32)},
n_z=10,
is_training=is_training)
x = tf.cast(255 * tf.sigmoid(plot_p_net['x'].distribution.logits),
dtype=tf.uint8)
x_plots = tf.reshape(tf.transpose(x, [1, 0, 2]), [-1, 28, 28])
def plot_samples(loop):
with loop.timeit('plot_time'):
images = session.run(x_plots, feed_dict={is_training: False})
save_images_collection(images=images,
filename=results.prepare_parent(
'plotting/{}.png'.format(loop.epoch)),
grid_size=(config.n_clusters, 10))
# derive the final un-supervised classifier
c_classifier = ClusteringClassifier(config.n_clusters, 10)
test_metrics = {}
def train_classifier(loop):
df = DataFlow.arrays([x_train], batch_size=config.batch_size). \
map(input_x_sampler)
with loop.timeit('cls_train_time'):
[c_pred] = collect_outputs(outputs=[y_given_x],
inputs=[input_x],
data_flow=df,
feed_dict={is_training: False})
c_classifier.fit(c_pred, y_train)
print(c_classifier.describe())
def evaluate_classifier(loop):
with loop.timeit('cls_test_time'):
[c_pred] = collect_outputs(outputs=[y_given_x],
inputs=[input_x],
data_flow=test_flow,
feed_dict={is_training: False})
y_pred = c_classifier.predict(c_pred)
cls_metrics = {'test_acc': accuracy_score(y_test, y_pred)}
loop.collect_metrics(cls_metrics)
test_metrics.update(cls_metrics)
# prepare for training and testing data
def input_x_sampler(x):
return session.run([sampled_x], feed_dict={sample_input_x: x})
with tf.device('/device:CPU:0'):
sample_input_x = tf.placeholder(dtype=tf.float32,
shape=(None, config.x_dim),
name='sample_input_x')
sampled_x = sample_from_probs(sample_input_x)
train_flow = DataFlow.arrays([x_train],
config.batch_size,
shuffle=True,
skip_incomplete=True).map(input_x_sampler)
test_flow = DataFlow.arrays([x_test], config.test_batch_size). \
map(input_x_sampler)
with create_session().as_default() as session, \
train_flow.threaded(5) as train_flow:
# fix the testing flow, reducing the testing time
test_flow = test_flow.to_arrays_flow(batch_size=config.test_batch_size)
# train the network
with TrainLoop(params,
var_groups=['p_net', 'q_net', 'gaussian_mixture_prior'],
max_epoch=config.max_epoch,
summary_dir=results.make_dir('train_summary'),
summary_graph=tf.get_default_graph(),
summary_commit_freqs={'loss': 10},
early_stopping=False) as loop:
trainer = Trainer(loop,
train_op, [input_x],
train_flow,
feed_dict={
learning_rate: learning_rate_var,
tau_p: tau_p_var,
tau_q: tau_q_var,
is_training: True
},
metrics={'loss': loss})
anneal_after(trainer,
learning_rate_var,
epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq)
anneal_after(trainer,
tau_p_var,
epochs=config.tau_p_anneal_epoch_freq,
steps=config.tau_p_anneal_step_freq)
anneal_after(trainer,
tau_q_var,
epochs=config.tau_q_anneal_epoch_freq,
steps=config.tau_q_anneal_step_freq)
evaluator = Evaluator(loop,
metrics={'test_nll': test_nll},
inputs=[input_x],
data_flow=test_flow,
feed_dict={is_training: False},
time_metric_name='test_time')
evaluator.after_run.add_hook(
lambda: results.commit(evaluator.last_metrics_dict))
trainer.evaluate_after_epochs(evaluator, freq=10)
trainer.evaluate_after_epochs(functools.partial(
plot_samples, loop),
freq=10)
trainer.evaluate_after_epochs(functools.partial(
train_classifier, loop),
freq=10)
trainer.evaluate_after_epochs(functools.partial(
evaluate_classifier, loop),
freq=10)
trainer.log_after_epochs(freq=1)
trainer.run()
# write the final results
with codecs.open('cluster_classifier.txt', 'wb', 'utf-8') as f:
f.write(c_classifier.describe())
test_metrics.update(evaluator.last_metrics_dict)
results.commit_and_print(test_metrics)
def main(config, result_dir):
# print the config
print_with_title('Configurations', config.format_config(), after='\n')
# open the result object and prepare for result directories
results = MLResults(result_dir)
results.make_dirs('plotting', exist_ok=True)
results.make_dirs('train_summary', exist_ok=True)
# input placeholders
input_x = tf.placeholder(dtype=tf.int32,
shape=(None, config.x_dim),
name='input_x')
is_training = tf.placeholder(dtype=tf.bool, shape=(), name='is_training')
learning_rate = tf.placeholder(shape=(), dtype=tf.float32)
learning_rate_var = AnnealingDynamicValue(config.initial_lr,
config.lr_anneal_factor)
# build the model
with arg_scope([q_net, p_net], is_training=is_training):
# derive the loss and lower-bound for training
train_q_net = q_net(input_x)
train_chain = train_q_net.chain(p_net,
latent_names=['z'],
latent_axis=0,
observed={'x': input_x})
baseline = baseline_net(input_x)
cost, baseline_cost = \
train_chain.vi.training.reinforce(baseline=baseline)
loss = regularization_loss() + tf.reduce_mean(cost + baseline_cost)
# derive the nll and logits output for testing
test_q_net = q_net(input_x, n_z=config.test_n_z)
test_chain = test_q_net.chain(p_net,
latent_names=['z'],
latent_axis=0,
observed={'x': input_x})
test_nll = -tf.reduce_mean(test_chain.vi.evaluation.is_loglikelihood())
test_lb = tf.reduce_mean(test_chain.vi.lower_bound.elbo())
# derive the optimizer
optimizer = tf.train.AdamOptimizer(learning_rate)
params = tf.trainable_variables()
grads = optimizer.compute_gradients(loss, var_list=params)
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
train_op = optimizer.apply_gradients(grads)
# derive the plotting function
with tf.name_scope('plot_x'):
plot_p_net = p_net(n_z=100, is_training=is_training)
x_plots = tf.reshape(bernoulli_as_pixel(plot_p_net['x']), (-1, 28, 28))
def plot_samples(loop):
with loop.timeit('plot_time'):
session = get_default_session_or_error()
images = session.run(x_plots, feed_dict={is_training: False})
save_images_collection(images=images,
filename='plotting/{}.png'.format(
loop.epoch),
grid_size=(10, 10),
results=results)
# prepare for training and testing data
(x_train, y_train), (x_test, y_test) = load_mnist()
train_flow = bernoulli_flow(x_train,
config.batch_size,
shuffle=True,
skip_incomplete=True)
test_flow = bernoulli_flow(x_test, config.test_batch_size, sample_now=True)
with create_session().as_default():
# train the network
with TrainLoop(params,
max_epoch=config.max_epoch,
max_step=config.max_step,
summary_dir=(results.system_path('train_summary')
if config.write_summary else None),
summary_graph=tf.get_default_graph(),
early_stopping=False) as loop:
trainer = Trainer(loop,
train_op, [input_x],
train_flow,
feed_dict={
learning_rate: learning_rate_var,
is_training: True
},
metrics={'loss': loss})
trainer.anneal_after(learning_rate_var,
epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq)
evaluator = Evaluator(loop,
metrics={
'test_nll': test_nll,
'test_lb': test_lb
},
inputs=[input_x],
data_flow=test_flow,
feed_dict={is_training: False},
time_metric_name='test_time')
evaluator.after_run.add_hook(
lambda: results.update_metrics(evaluator.last_metrics_dict))
trainer.evaluate_after_epochs(evaluator, freq=10)
trainer.evaluate_after_epochs(functools.partial(
plot_samples, loop),
freq=10)
trainer.log_after_epochs(freq=1)
trainer.run()
# print the final metrics and close the results object
print_with_title('Results', results.format_metrics(), before='\n')
results.close()