def main():
# parse the arguments
arg_parser = ArgumentParser()
spt.register_config_arguments(config, arg_parser, title='Model options')
spt.register_config_arguments(spt.settings,
arg_parser,
prefix='tfsnippet',
title='TFSnippet options')
arg_parser.parse_args(sys.argv[1:])
# print the config
print_with_title('Configurations', pformat(config.to_dict()), after='\n')
# open the result object and prepare for result directories
results = MLResults(config.result_dir)
results.save_config(config) # save experiment settings for review
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')
learning_rate = spt.AnnealingVariable('learning_rate', config.initial_lr,
config.lr_anneal_factor)
# derive the output for initialization
with tf.name_scope('initialization'), \
spt.utils.scoped_set_config(spt.settings, auto_histogram=False):
init_q_net = q_net(input_x, is_initializing=True)
init_chain = init_q_net.chain(p_net,
observed={'x': input_x},
is_initializing=True)
init_lb = tf.reduce_mean(init_chain.vi.lower_bound.elbo())
# derive the loss and lower-bound for training
with tf.name_scope('training'):
train_q_net = q_net(input_x)
train_chain = train_q_net.chain(p_net, observed={'x': input_x})
vae_loss = tf.reduce_mean(train_chain.vi.training.sgvb())
loss = vae_loss + tf.losses.get_regularization_loss()
# derive the nll and logits output for testing
with tf.name_scope('testing'):
test_q_net = q_net(input_x, n_z=config.test_n_z)
test_chain = test_q_net.chain(p_net,
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
with tf.name_scope('optimizing'):
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('plotting'):
plot_p_net = p_net(n_z=100)
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)
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) = \
spt.datasets.load_mnist(x_shape=[784])
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 spt.utils.create_session().as_default() as session, \
train_flow.threaded(5) as train_flow:
spt.utils.ensure_variables_initialized()
# initialize the network
for [x] in train_flow:
print('Network initialized, first-batch loss is {:.6g}.\n'.format(
session.run(init_lb, feed_dict={input_x: x})))
break
# train the network
with spt.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 = spt.Trainer(loop,
train_op, [input_x],
train_flow,
metrics={'loss': loss},
summaries=tf.summary.merge_all(
spt.GraphKeys.AUTO_HISTOGRAM))
trainer.anneal_after(learning_rate,
epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq)
evaluator = spt.Evaluator(loop,
metrics={
'test_nll': test_nll,
'test_lb': test_lb
},
inputs=[input_x],
data_flow=test_flow,
time_metric_name='test_time')
evaluator.events.on(
spt.EventKeys.AFTER_EXECUTION,
lambda e: 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():
# parse the arguments
arg_parser = ArgumentParser()
spt.register_config_arguments(config, arg_parser)
arg_parser.parse_args(sys.argv[1:])
# print the config
print_with_title('Configurations', pformat(config.to_dict()), after='\n')
# open the result object and prepare for result directories
results = MLResults(config.result_dir)
results.save_config(config) # save experiment settings for review
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')
learning_rate = spt.AnnealingVariable('learning_rate', config.initial_lr,
config.lr_anneal_factor)
# build the posterior flow
with tf.variable_scope('posterior_flow'):
flows = []
for i in range(config.n_flows):
flows.append(spt.layers.ActNorm())
flows.append(
spt.layers.CouplingLayer(tf.make_template(
'coupling',
coupling_layer_shift_and_scale,
create_scope_now_=True),
scale_type='exp'))
flows.append(spt.layers.InvertibleDense())
posterior_flow = spt.layers.SequentialFlow(flows=flows)
# derive the initialization op
with tf.name_scope('initialization'), \
arg_scope([spt.layers.act_norm], initializing=True):
init_q_net = q_net(input_x, posterior_flow)
init_chain = init_q_net.chain(p_net,
latent_axis=0,
observed={'x': input_x})
init_loss = tf.reduce_mean(init_chain.vi.training.sgvb())
# derive the loss and lower-bound for training
with tf.name_scope('training'):
train_q_net = q_net(input_x, posterior_flow)
train_chain = train_q_net.chain(p_net,
latent_axis=0,
observed={'x': input_x})
vae_loss = tf.reduce_mean(train_chain.vi.training.sgvb())
loss = vae_loss + tf.losses.get_regularization_loss()
# derive the nll and logits output for testing
with tf.name_scope('testing'):
test_q_net = q_net(input_x, posterior_flow, n_z=config.test_n_z)
test_chain = test_q_net.chain(p_net,
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
with tf.name_scope('optimizing'):
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('plotting'):
plot_p_net = p_net(n_z=100)
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)
save_images_collection(images=images,
filename='plotting/{}.png'.format(
loop.epoch),
grid_size=(10, 10))
# prepare for training and testing data
(x_train, y_train), (x_test, y_test) = spt.datasets.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 spt.utils.create_session().as_default() as session, \
train_flow.threaded(5) as train_flow:
# initialize the network
spt.utils.ensure_variables_initialized()
for [batch_x] in train_flow:
print('Network initialization loss: {:.6g}'.format(
session.run(init_loss, {input_x: batch_x})))
print('')
break
# train the network
with spt.TrainLoop(params,
var_groups=['p_net', 'q_net', 'posterior_flow'],
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 = spt.Trainer(loop,
train_op, [input_x],
train_flow,
metrics={'loss': loss})
trainer.anneal_after(learning_rate,
epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq)
evaluator = spt.Evaluator(loop,
metrics={
'test_nll': test_nll,
'test_lb': test_lb
},
inputs=[input_x],
data_flow=test_flow,
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():
# parse the arguments
arg_parser = ArgumentParser()
spt.register_config_arguments(config, arg_parser)
arg_parser.parse_args(sys.argv[1:])
# print the config
print_with_title('Configurations', pformat(config.to_dict()), after='\n')
# open the result object and prepare for result directories
results = MLResults(config.result_dir)
results.save_config(config) # save experiment settings for review
results.make_dirs('train_summary', exist_ok=True)
# input placeholders
input_x = tf.placeholder(dtype=tf.float32,
shape=(None, ) + config.x_shape,
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 = spt.AnnealingVariable('learning_rate', config.initial_lr,
config.lr_anneal_factor)
multi_gpu = MultiGPU()
# build the model
grads = []
losses = []
y_list = []
acc_list = []
batch_size = spt.utils.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_cls_loss = tf.losses.sparse_softmax_cross_entropy(
dev_input_y, dev_logits)
dev_loss = dev_cls_loss + tf.losses.get_regularization_loss()
dev_y = spt.ops.softmax_classification_output(dev_logits)
dev_acc = spt.ops.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
(x_train, y_train), (x_test, y_test) = \
spt.datasets.load_cifar10(x_shape=config.x_shape, normalize_x=True)
train_flow = spt.DataFlow.arrays([x_train, y_train],
config.batch_size,
shuffle=True,
skip_incomplete=True)
test_flow = spt.DataFlow.arrays([x_test, y_test], config.test_batch_size)
with spt.utils.create_session().as_default():
# train the network
with spt.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 = spt.Trainer(loop,
train_op, [input_x, input_y],
train_flow,
feed_dict={is_training: True},
metrics={
'loss': loss,
'acc': acc
})
trainer.anneal_after(learning_rate,
epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq)
evaluator = spt.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(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(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():
# parse the arguments
arg_parser = ArgumentParser()
spt.register_config_arguments(config, arg_parser, title='Model options')
spt.register_config_arguments(spt.settings,
arg_parser,
prefix='tfsnippet',
title='TFSnippet options')
arg_parser.parse_args(sys.argv[1:])
# print the config
print_with_title('Configurations', pformat(config.to_dict()), after='\n')
# open the result object and prepare for result directories
results = MLResults(config.result_dir)
results.save_config(config) # save experiment settings for review
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')
learning_rate = spt.AnnealingVariable('learning_rate', config.initial_lr,
config.lr_anneal_factor)
# derive the loss and lower-bound for training
with tf.name_scope('training'):
train_q_net = q_net(input_x, n_samples=config.train_n_samples)
train_chain = train_q_net.chain(p_net,
latent_axis=0,
observed={'x': input_x})
if config.vi_algorithm == 'reinforce':
baseline = reinforce_baseline_net(input_x)
vae_loss = tf.reduce_mean(
train_chain.vi.training.reinforce(baseline=baseline))
else:
assert (config.vi_algorithm == 'vimco')
vae_loss = tf.reduce_mean(train_chain.vi.training.vimco())
loss = vae_loss + tf.losses.get_regularization_loss()
# derive the nll and logits output for testing
with tf.name_scope('testing'):
test_q_net = q_net(input_x, n_samples=config.test_n_samples)
test_chain = test_q_net.chain(p_net,
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,
name='q_y_given_x')
# derive the optimizer
with tf.name_scope('optimizing'):
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('plotting'):
plot_p_net = p_net(
observed={'y': tf.range(config.n_clusters, dtype=tf.int32)},
n_z=10)
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)
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,
)
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,
)
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) = \
spt.datasets.load_mnist(x_shape=[784])
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 spt.utils.create_session().as_default() as session, \
train_flow.threaded(5) as train_flow:
# train the network
with spt.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 = spt.Trainer(loop,
train_op, [input_x],
train_flow,
metrics={'loss': loss},
summaries=tf.summary.merge_all(
spt.GraphKeys.AUTO_HISTOGRAM))
trainer.anneal_after(learning_rate,
epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq)
evaluator = spt.Evaluator(loop,
metrics={'test_nll': test_nll},
inputs=[input_x],
data_flow=test_flow,
time_metric_name='test_time')
evaluator.events.on(
spt.EventKeys.AFTER_EXECUTION,
lambda e: 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():
# parse the arguments
arg_parser = ArgumentParser()
spt.register_config_arguments(config, arg_parser, title='Model options')
spt.register_config_arguments(spt.settings,
arg_parser,
prefix='tfsnippet',
title='TFSnippet options')
arg_parser.parse_args(sys.argv[1:])
# print the config
print_with_title('Configurations', pformat(config.to_dict()), after='\n')
# open the result object and prepare for result directories
results = MLResults(config.result_dir)
results.save_config(config) # save experiment settings for review
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')
learning_rate = spt.AnnealingVariable('learning_rate', config.initial_lr,
config.lr_anneal_factor)
# derive the loss, output and accuracy
logits = model(input_x)
cls_loss = tf.losses.sparse_softmax_cross_entropy(input_y, logits)
loss = cls_loss + tf.losses.get_regularization_loss()
y = spt.ops.softmax_classification_output(logits)
acc = spt.ops.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) = \
spt.datasets.load_cifar10(x_shape=(config.x_dim,), normalize_x=True)
train_flow = spt.DataFlow.arrays([x_train, y_train],
config.batch_size,
shuffle=True,
skip_incomplete=True)
test_flow = spt.DataFlow.arrays([x_test, y_test], config.test_batch_size)
with spt.utils.create_session().as_default():
# train the network
with spt.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 = spt.Trainer(loop,
train_op, [input_x, input_y],
train_flow,
metrics={
'loss': loss,
'acc': acc
},
summaries=tf.summary.merge_all(
spt.GraphKeys.AUTO_HISTOGRAM))
trainer.anneal_after(learning_rate,
epochs=config.lr_anneal_epoch_freq,
steps=config.lr_anneal_step_freq)
evaluator = spt.Evaluator(loop,
metrics={'test_acc': acc},
inputs=[input_x, input_y],
data_flow=test_flow,
time_metric_name='test_time')
evaluator.events.on(
spt.EventKeys.AFTER_EXECUTION,
lambda e: 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(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()
