def train(reader, model, max_epochs):
# Input variables denoting the features and label data
query = Input(input_dim, is_sparse=False)
slot_labels = Input(num_labels, is_sparse=True) # TODO: make sparse once it works
# apply model to input
z = model(query)
# loss and metric
ce = cross_entropy_with_softmax(z, slot_labels)
pe = classification_error (z, slot_labels)
# training config
epoch_size = 36000
minibatch_size = 70
num_mbs_to_show_result = 100
momentum_time_constant = momentum_as_time_constant_schedule(minibatch_size / -math.log(0.9)) # TODO: Change to round number. This is 664.39. 700?
lr_schedule = [0.003]*2+[0.0015]*12+[0.0003] # LR schedule over epochs (we don't run that many epochs, but if we did, these are good values)
# trainer object
lr_per_sample = learning_rate_schedule(lr_schedule, UnitType.sample, epoch_size)
learner = adam_sgd(z.parameters,
lr=lr_per_sample, momentum=momentum_time_constant,
unit_gain=True,
low_memory=True,
gradient_clipping_threshold_per_sample=15, gradient_clipping_with_truncation=True)
trainer = Trainer(z, (ce, pe), [learner])
# define mapping from reader streams to network inputs
input_map = {
query : reader.streams.query,
slot_labels : reader.streams.slot_labels
}
# process minibatches and perform model training
log_number_of_parameters(z) ; print()
# more detailed logging
progress_printer = ProgressPrinter(freq=100, first=10, tag='Training', tensorboard_log_dir='atis_log', model=z)
#progress_printer = ProgressPrinter(tag='Training')
t = 0
for epoch in range(max_epochs): # loop over epochs
epoch_end = (epoch+1) * epoch_size
while t < epoch_end: # loop over minibatches on the epoch
# BUGBUG? The change of minibatch_size parameter vv has no effect.
data = reader.next_minibatch(min(minibatch_size, epoch_end-t), input_map=input_map) # fetch minibatch
trainer.train_minibatch(data) # update model with it
t += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
#def trace_node(name):
# nl = [n for n in z.parameters if n.name() == name]
# if len(nl) > 0:
# print (name, np.asarray(nl[0].value))
#trace_node('W')
#trace_node('stabilizer_param')
loss, metric, actual_samples = progress_printer.epoch_summary(with_metric=True)
return loss, metric
def test_learner_logging():
from cntk import Trainer
from cntk.logging import ProgressPrinter
from cntk import cross_entropy_with_softmax, classification_error
features = C.input_variable(shape=(1,), needs_gradient=True, name='a')
w_init = 1
w = parameter(shape=(1,), init=w_init)
z = features * w
labels = C.input_variable(shape=(1,), name='b')
ce = cross_entropy_with_softmax(z, labels)
errs = classification_error(z, labels)
writer = TestProgressWriter();
lr_values = [0.3, 0.2, 0.1, 0]
m_values = [0.6, 0.7, 0.8]
learner = C.momentum_sgd(z.parameters,
learning_rate_schedule(lr_values, UnitType.sample, 1),
C.momentum_schedule(m_values, 1))
trainer = Trainer(z, (ce, errs), [learner], writer)
for i in range(10):
trainer.train_minibatch({features: [[2.]], labels: [[1.]]})
assert len(writer.log_output) == len(lr_values + m_values)
values = [j for i in zip(lr_values,m_values) for j in i] + [0]
for i in range(len(values)):
assert (values[i] == writer.log_output[i])
def train_model(reader, model, criterion, epoch_size=50000, max_epochs=80):
minibatch_size = 64
# learning parameters
learner = momentum_sgd(model.parameters,
lr = learning_rate_schedule([0.0015625]*20+[0.00046875]*20+[0.00015625]*20+[0.000046875]*10+[0.000015625], unit=UnitType.sample, epoch_size=epoch_size),
momentum = momentum_as_time_constant_schedule([0]*20+[600]*20+[1200], epoch_size=epoch_size),
l2_regularization_weight = 0.002)
# trainer object
trainer = Trainer(None, criterion, learner)
# perform model training
log_number_of_parameters(model) ; print()
progress_printer = ProgressPrinter(tag='Training', num_epochs=max_epochs)
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
mb = reader.next_minibatch(min(minibatch_size, epoch_size - sample_count)) # fetch minibatch.
#trainer.train_minibatch(mb[reader.streams.features], mb[reader.streams.labels])
trainer.train_minibatch({criterion.arguments[0]: mb[reader.streams.features], criterion.arguments[1]: mb[reader.streams.labels]})
sample_count += mb[reader.streams.labels].num_samples # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
loss, metric, actual_samples = progress_printer.epoch_summary(with_metric=True)
model.save(os.path.join(model_path, "ConvNet_CIFAR10_DataAug_{}.dnn".format(epoch)))
# return evaluation error.
return loss, metric # return values from last epoch
def entrenar(checkpoint, entrRuedas, entrOperaciones, input_dim, num_output_classes, testRuedas, testOperaciones):
minibatch_size = 100;
epocs=900;
minibatchIteraciones = int(len(entrOperaciones) / minibatch_size);
# Input variables denoting the features and label data
feature = input((input_dim), np.float32)
label = input((num_output_classes), np.float32)
netout = crearRed(input_dim, num_output_classes, feature);
ce = cross_entropy_with_softmax(netout, label)
pe = classification_error(netout, label)
lr_per_minibatch=learning_rate_schedule(0.25, UnitType.minibatch)
# Instantiate the trainer object to drive the model training
learner = sgd(netout.parameters, lr=lr_per_minibatch)
progress_printer = ProgressPrinter(log_to_file=checkpoint+".log", num_epochs=epocs);
trainer = Trainer(netout, (ce, pe), learner, progress_printer)
if os.path.isfile(checkpoint):
trainer.restore_from_checkpoint(checkpoint);
npentrRuedas = np.array(entrRuedas).astype(np.float32);
npentrOperaciones = np.array(entrOperaciones).astype(np.float32);
#iteramos una vez por cada "epoc"
for i in range(0, epocs):
p = np.random.permutation(len(entrRuedas));
npentrOperaciones = npentrOperaciones[p];
npentrRuedas = npentrRuedas[p];
#ahora partimos los datos en "minibatches" y entrenamos
for j in range(0, minibatchIteraciones):
features = npentrRuedas[j*minibatch_size:(j+1)*minibatch_size];
labels = npentrOperaciones[j*minibatch_size:(j+1)*minibatch_size];
trainer.train_minibatch({feature: features, label: labels});
trainer.summarize_training_progress()
trainer.save_checkpoint(checkpoint);
minibatchIteraciones = int(len(testOperaciones) / minibatch_size);
avg_error = 0;
for j in range(0, minibatchIteraciones):
test_features = np.array(testRuedas[j*minibatch_size:(j+1)*minibatch_size]).astype(np.float32);
test_labels = np.array(testOperaciones[j*minibatch_size:(j+1)*minibatch_size]).astype(np.float32);
#test_features = np.array( entrRuedas[0:minibatch_size]).astype(np.float32);
#test_labels = np.array(entrOperaciones[0:minibatch_size]).astype(np.float32);
avg_error = avg_error + ( trainer.test_minibatch(
{feature: test_features, label: test_labels}) / minibatchIteraciones)
return avg_error
def train_lm():
data = DataReader(token_to_id_path, segment_sepparator)
# Create model nodes for the source and target inputs
input_sequence, label_sequence = create_inputs(data.vocab_dim)
# Create the model. It has three output nodes
# z: the input to softmax that provides the latent representation of the next token
# cross_entropy: this is used training criterion
# error: this a binary indicator if the model predicts the correct token
z, cross_entropy, error = create_model(input_sequence, label_sequence, data.vocab_dim, hidden_dim)
# For measurement we use the (build in) full softmax.
full_ce = C.cross_entropy_with_softmax(z, label_sequence)
# print out some useful training information
log_number_of_parameters(z) ; print()
# Run the training loop
num_trained_samples = 0
num_trained_samples_since_last_report = 0
# Instantiate the trainer object to drive the model training
lr_schedule = learning_rate_schedule(learning_rate, UnitType.sample)
momentum_schedule = momentum_as_time_constant_schedule(momentum_as_time_constant)
gradient_clipping_with_truncation = True
learner = momentum_sgd(z.parameters, lr_schedule, momentum_schedule,
gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
gradient_clipping_with_truncation=gradient_clipping_with_truncation)
trainer = Trainer(z, (cross_entropy, error), learner)
for epoch_count in range(num_epochs):
for features, labels, token_count in data.minibatch_generator(train_file_path, sequence_length, sequences_per_batch):
arguments = ({input_sequence : features, label_sequence : labels})
t_start = timeit.default_timer()
trainer.train_minibatch(arguments)
t_end = timeit.default_timer()
samples_per_second = token_count / (t_end - t_start)
# Print progress report every num_samples_between_progress_report samples
if num_trained_samples_since_last_report >= num_samples_between_progress_report or num_trained_samples == 0:
av_ce = average_cross_entropy(full_ce, input_sequence, label_sequence, data)
print_progress(samples_per_second, av_ce, num_trained_samples, t_start)
num_trained_samples_since_last_report = 0
num_trained_samples += token_count
num_trained_samples_since_last_report += token_count
# after each epoch save the model
model_filename = "models/lm_epoch%d.dnn" % epoch_count
z.save_model(model_filename)
print("Saved model to '%s'" % model_filename)
def train_lm(training_file, epochs, max_num_minibatches):
# load the data and vocab
data, char_to_ix, ix_to_char, data_size, vocab_dim = load_data_and_vocab(training_file)
# Model the source and target inputs to the model
input_sequence, label_sequence = create_inputs(vocab_dim)
# create the model
model = create_model(vocab_dim)
# and apply it to the input sequence
z = model(input_sequence)
# setup the criterions (loss and metric)
ce = cross_entropy_with_softmax(z, label_sequence)
errs = classification_error(z, label_sequence)
# Instantiate the trainer object to drive the model training
lr_per_sample = learning_parameter_schedule_per_sample(0.001)
momentum_schedule = momentum_schedule_per_sample(0.9990913221888589)
clipping_threshold_per_sample = 5.0
gradient_clipping_with_truncation = True
learner = momentum_sgd(z.parameters, lr_per_sample, momentum_schedule,
gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
gradient_clipping_with_truncation=gradient_clipping_with_truncation)
progress_printer = ProgressPrinter(freq=100, tag='Training')
trainer = Trainer(z, (ce, errs), learner, progress_printer)
sample_freq = 1000
minibatches_per_epoch = min(data_size // minibatch_size, max_num_minibatches // epochs)
# print out some useful training information
log_number_of_parameters(z)
print ("Running %d epochs with %d minibatches per epoch" % (epochs, minibatches_per_epoch))
print()
for e in range(0, epochs):
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
# If it's the start of the data, we specify that we are looking at a new sequence (True)
mask = [True]
for b in range(0, minibatches_per_epoch):
# get the data
features, labels = get_data(b, minibatch_size, data, char_to_ix, vocab_dim)
arguments = ({input_sequence : features, label_sequence : labels}, mask)
mask = [False]
trainer.train_minibatch(arguments)
global_minibatch = e*minibatches_per_epoch + b
if global_minibatch % sample_freq == 0:
print(sample(z, ix_to_char, vocab_dim, char_to_ix))
model_filename = "models/shakespeare_epoch%d.dnn" % (e+1)
z.save(model_filename)
print("Saved model to '%s'" % model_filename)
def train_fast_rcnn(debug_output=False):
if debug_output:
print("Storing graphs and intermediate models to %s." % os.path.join(abs_path, "Output"))
# Create the minibatch source
minibatch_source = create_mb_source(image_height, image_width, num_channels,
num_classes, num_rois, base_path, "train")
# Input variables denoting features, rois and label data
image_input = input_variable((num_channels, image_height, image_width))
roi_input = input_variable((num_rois, 4))
label_input = input_variable((num_rois, num_classes))
# define mapping from reader streams to network inputs
input_map = {
image_input: minibatch_source[features_stream_name],
roi_input: minibatch_source[roi_stream_name],
label_input: minibatch_source[label_stream_name]
}
# Instantiate the Fast R-CNN prediction model and loss function
frcn_output = frcn_predictor(image_input, roi_input, num_classes)
ce = cross_entropy_with_softmax(frcn_output, label_input, axis=1)
pe = classification_error(frcn_output, label_input, axis=1)
if debug_output:
plot(frcn_output, os.path.join(abs_path, "Output", "graph_frcn.png"))
# Set learning parameters
l2_reg_weight = 0.0005
lr_per_sample = [0.00001] * 10 + [0.000001] * 5 + [0.0000001]
lr_schedule = learning_rate_schedule(lr_per_sample, unit=UnitType.sample)
mm_schedule = momentum_as_time_constant_schedule(momentum_time_constant)
# Instantiate the trainer object
learner = momentum_sgd(frcn_output.parameters, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight)
trainer = Trainer(frcn_output, (ce, pe), learner)
# Get minibatches of images and perform model training
print("Training Fast R-CNN model for %s epochs." % max_epochs)
log_number_of_parameters(frcn_output)
progress_printer = ProgressPrinter(tag='Training', num_epochs=max_epochs)
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = minibatch_source.next_minibatch(min(mb_size, epoch_size-sample_count), input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
progress_printer.epoch_summary(with_metric=True)
if debug_output:
frcn_output.save(os.path.join(abs_path, "Output", "frcn_py_%s.model" % (epoch+1)))
return frcn_output
def train_sequence_classifier(debug_output=False):
input_dim = 2000
cell_dim = 25
hidden_dim = 25
embedding_dim = 50
num_output_classes = 5
# Input variables denoting the features and label data
features = input_variable(shape=input_dim, is_sparse=True)
label = input_variable(num_output_classes, dynamic_axes=[
Axis.default_batch_axis()])
# Instantiate the sequence classification model
classifier_output = LSTM_sequence_classifer_net(
features, num_output_classes, embedding_dim, hidden_dim, cell_dim)
ce = cross_entropy_with_softmax(classifier_output, label)
pe = classification_error(classifier_output, label)
rel_path = r"../../../../Tests/EndToEndTests/Text/SequenceClassification/Data/Train.ctf"
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), rel_path)
reader = create_reader(path, True, input_dim, num_output_classes)
input_map = {
features : reader.streams.features,
label : reader.streams.labels
}
lr_per_sample = learning_rate_schedule(0.0005, UnitType.sample)
# Instantiate the trainer object to drive the model training
trainer = Trainer(classifier_output, (ce, pe),
sgd(classifier_output.parameters, lr=lr_per_sample))
# Get minibatches of sequences to train with and perform model training
minibatch_size = 200
training_progress_output_freq = 10
if debug_output:
training_progress_output_freq = training_progress_output_freq/3
for i in range(251):
mb = reader.next_minibatch(minibatch_size, input_map=input_map)
trainer.train_minibatch(mb)
print_training_progress(trainer, i, training_progress_output_freq)
import copy
evaluation_average = copy.copy(
trainer.previous_minibatch_evaluation_average)
loss_average = copy.copy(trainer.previous_minibatch_loss_average)
return evaluation_average, loss_average
def train(reader, model, max_epochs):
# declare the model's input dimension, so that the saved model is usable
model.update_signature(Sequence[SparseTensor[vocab_size]])
#model.declare_args(vocab_size)
# criterion: (model args, labels) -> (loss, metric)
# here (query, slot_labels) -> (ce, errs)
criterion = create_criterion_function(model)
labels = reader.streams.slot_labels
#labels = reader.streams.intent_labels # for intent classification
#from cntk.graph import plot
#plot(criterion, filename=data_dir + "/model.pdf")
# iteration parameters --needed here because learner schedule needs it
epoch_size = 36000
minibatch_size = 70
#epoch_size = 1000 ; max_epochs = 1 # uncomment for faster testing
# SGD parameters
learner = adam_sgd(criterion.parameters,
lr = learning_rate_schedule([0.003]*2+[0.0015]*12+[0.0003], UnitType.sample, epoch_size),
momentum = momentum_as_time_constant_schedule(minibatch_size / -math.log(0.9)),
low_memory = True,
gradient_clipping_threshold_per_sample = 15,
gradient_clipping_with_truncation = True)
# trainer
trainer = Trainer(None, criterion, learner)
# process minibatches and perform model training
log_number_of_parameters(model) ; print()
progress_printer = ProgressPrinter(freq=100, first=10, tag='Training') # more detailed logging
#progress_printer = ProgressPrinter(tag='Training')
t = 0
for epoch in range(max_epochs): # loop over epochs
peek(model, epoch) # log some interesting info
epoch_end = (epoch+1) * epoch_size
while t < epoch_end: # loop over minibatches on the epoch
# BUGBUG: The change of minibatch_size parameter vv has no effect.
# TODO: change all examples to this pattern; then remove this comment
data = reader.next_minibatch(min(minibatch_size, epoch_end-t)) # fetch minibatch
#trainer.train_minibatch(data[reader.streams.query], data[labels]) # update model with it
trainer.train_minibatch({criterion.arguments[0]: data[reader.streams.query], criterion.arguments[1]: data[labels]}) # update model with it
t += data[labels].num_samples # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
loss, metric, actual_samples = progress_printer.epoch_summary(with_metric=True)
return loss, metric # return values from last epoch
def simple_mnist():
input_dim = 784
num_output_classes = 10
num_hidden_layers = 1
hidden_layers_dim = 200
# Input variables denoting the features and label data
input = input_variable(input_dim, np.float32)
label = input_variable(num_output_classes, np.float32)
# Instantiate the feedforward classification model
scaled_input = element_times(constant((), 0.00390625), input)
netout = fully_connected_classifier_net(scaled_input, num_output_classes, hidden_layers_dim, num_hidden_layers, sigmoid)
ce = cross_entropy_with_softmax(netout, label)
pe = classification_error(netout, label)
rel_path = os.path.join(*"../../../../Examples/Image/MNIST/Data/Train-28x28_cntk_text.txt".split("/"))
path = os.path.normpath(os.path.join(abs_path, rel_path))
if not os.path.exists(path):
readme_file = os.path.normpath(os.path.join(os.path.dirname(path), "..", "README.md"))
raise RuntimeError("File '%s' does not exist. Please follow the instructions at %s to download and prepare it."%(path, readme_file))
feature_stream_name = 'features'
labels_stream_name = 'labels'
mb_source = text_format_minibatch_source(path, [
StreamConfiguration( feature_stream_name, input_dim ),
StreamConfiguration( labels_stream_name, num_output_classes) ])
features_si = mb_source.stream_info(feature_stream_name)
labels_si = mb_source.stream_info(labels_stream_name)
# Instantiate the trainer object to drive the model training
lr = learning_rates_per_sample(0.003125)
trainer = Trainer(netout, ce, pe, [sgd_learner(netout.owner.parameters(), lr)])
# Get minibatches of images to train with and perform model training
minibatch_size = 32
num_samples_per_sweep = 60000
num_sweeps_to_train_with = 1
num_minibatches_to_train = (num_samples_per_sweep * num_sweeps_to_train_with) / minibatch_size
training_progress_output_freq = 20
for i in range(0, int(num_minibatches_to_train)):
mb = mb_source.get_next_minibatch(minibatch_size)
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
arguments = {input : mb[features_si].m_data, label : mb[labels_si].m_data}
trainer.train_minibatch(arguments)
print_training_progress(trainer, i, training_progress_output_freq)
def train_sequence_classifier():
input_dim = 2000;
cell_dim = 25;
hidden_dim = 25;
embedding_dim = 50;
num_output_classes = 5;
# Input variables denoting the features and label data
features = input_variable(shape=input_dim, is_sparse=True)
label = input_variable(num_output_classes, dynamic_axes = [Axis.default_batch_axis()])
# Instantiate the sequence classification model
classifier_output = LSTM_sequence_classifer_net(features, num_output_classes, embedding_dim, hidden_dim, cell_dim)
ce = cross_entropy_with_softmax(classifier_output, label)
pe = classification_error(classifier_output, label)
rel_path = r"../../../../Tests/EndToEndTests/Text/SequenceClassification/Data/Train.ctf"
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), rel_path)
feature_stream_name = 'features'
labels_stream_name = 'labels'
mb_source = text_format_minibatch_source(path, [
StreamConfiguration( feature_stream_name, input_dim, True, 'x' ),
StreamConfiguration( labels_stream_name, num_output_classes, False, 'y')], 0)
features_si = mb_source.stream_info(features)
labels_si = mb_source.stream_info(label)
# Instantiate the trainer object to drive the model training
lr = lr = learning_rates_per_sample(0.0005)
trainer = Trainer(classifier_output, ce, pe, [sgd_learner(classifier_output.owner.parameters(), lr)])
# Get minibatches of sequences to train with and perform model training
minibatch_size = 200
training_progress_output_freq = 10
i = 0;
while True:
mb = mb_source.get_next_minibatch(minibatch_size)
if len(mb) == 0:
break
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
arguments = {features : mb[features_si].m_data, label : mb[labels_si].m_data}
trainer.train_minibatch(arguments)
print_training_progress(trainer, i, training_progress_output_freq)
i += 1
def train_model(base_model_file, feature_node_name, last_hidden_node_name,
image_width, image_height, num_channels, num_classes, train_map_file,
num_epochs, max_images=-1, freeze=False):
epoch_size = sum(1 for line in open(train_map_file))
if max_images > 0:
epoch_size = min(epoch_size, max_images)
# Create the minibatch source and input variables
minibatch_source = create_mb_source(train_map_file, image_width, image_height, num_channels, num_classes)
image_input = input_variable((num_channels, image_height, image_width))
label_input = input_variable(num_classes)
# Define mapping from reader streams to network inputs
input_map = {
image_input: minibatch_source[features_stream_name],
label_input: minibatch_source[label_stream_name]
}
# Instantiate the transfer learning model and loss function
tl_model = create_model(base_model_file, feature_node_name, last_hidden_node_name, num_classes, image_input, freeze)
ce = cross_entropy_with_softmax(tl_model, label_input)
pe = classification_error(tl_model, label_input)
# Instantiate the trainer object
lr_schedule = learning_rate_schedule(lr_per_mb, unit=UnitType.minibatch)
mm_schedule = momentum_schedule(momentum_per_mb)
learner = momentum_sgd(tl_model.parameters, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight)
trainer = Trainer(tl_model, (ce, pe), learner)
# Get minibatches of images and perform model training
print("Training transfer learning model for {0} epochs (epoch_size = {1}).".format(num_epochs, epoch_size))
log_number_of_parameters(tl_model)
progress_printer = ProgressPrinter(tag='Training', num_epochs=num_epochs)
for epoch in range(num_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = minibatch_source.next_minibatch(min(mb_size, epoch_size-sample_count), input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
if sample_count % (100 * mb_size) == 0:
print ("Processed {0} samples".format(sample_count))
progress_printer.epoch_summary(with_metric=True)
return tl_model
def train_sequence_classifier():
input_dim = 2000
cell_dim = 25
hidden_dim = 25
embedding_dim = 50
num_output_classes = 5
# Input variables denoting the features and label data
features = sequence.input_variable(shape=input_dim, is_sparse=True)
label = input_variable(num_output_classes)
# Instantiate the sequence classification model
classifier_output = LSTM_sequence_classifier_net(
features, num_output_classes, embedding_dim, hidden_dim, cell_dim)
ce = cross_entropy_with_softmax(classifier_output, label)
pe = classification_error(classifier_output, label)
rel_path = ("../../../Tests/EndToEndTests/Text/" +
"SequenceClassification/Data/Train.ctf")
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), rel_path)
reader = create_reader(path, True, input_dim, num_output_classes)
input_map = {
features: reader.streams.features,
label: reader.streams.labels
}
lr_per_sample = learning_parameter_schedule_per_sample(0.0005)
# Instantiate the trainer object to drive the model training
progress_printer = ProgressPrinter(0)
trainer = Trainer(classifier_output, (ce, pe),
sgd(classifier_output.parameters, lr=lr_per_sample),
progress_printer)
# Get minibatches of sequences to train with and perform model training
minibatch_size = 200
for i in range(255):
mb = reader.next_minibatch(minibatch_size, input_map=input_map)
trainer.train_minibatch(mb)
evaluation_average = float(trainer.previous_minibatch_evaluation_average)
loss_average = float(trainer.previous_minibatch_loss_average)
return evaluation_average, loss_average
def Evaluator(criterion):
loss, metric = Trainer._get_loss_metric(criterion)
parameters = set(loss.parameters)
if metric:
parameters |= set(metric.parameters)
dummy_learner = momentum_sgd(tuple(parameters),
lr = learning_rate_schedule(1, UnitType.minibatch),
momentum = momentum_as_time_constant_schedule(0))
return Trainer(None, (loss, metric), dummy_learner)
def train_model(reader, reader_test, model, epoch_size=50000, max_epochs=80):
# declare the model's input dimension
# Training does not require this, but it is needed for deployment.
model.update_signature((num_channels, image_height, image_width))
# criterion function. This is what is being trained trained.
# Model gets "sandwiched" between normalization (not part of model proper) and criterion.
criterion = create_criterion_function(model, normalize=lambda x: x / 256)
#debughelpers.dump_function(criterion, 'criterion')
#from cntk.logging.graph import plot
#plot(criterion, filename=os.path.join(model_path, "ConvNet_CIFAR10_DataAug.pdf"))
# iteration parameters
minibatch_size = 64
#epoch_size = 1000 ; max_epochs = 1 # for faster testing
# learning parameters
learner = momentum_sgd(model.parameters,
lr = learning_rate_schedule([0.0015625]*20+[0.00046875]*20+[0.00015625]*20+[0.000046875]*10+[0.000015625], unit=UnitType.sample, epoch_size=epoch_size),
momentum = momentum_as_time_constant_schedule([0]*20+[600]*20+[1200], epoch_size=epoch_size),
l2_regularization_weight = 0.002)
# trainer object
trainer = Trainer(None, criterion, learner)
# perform model training
log_number_of_parameters(model) ; print()
progress_printer = ProgressPrinter(tag='Training', num_epochs=max_epochs)
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
mb = reader.next_minibatch(min(minibatch_size, epoch_size - sample_count)) # fetch minibatch.
#trainer.train_minibatch(mb[reader.streams.features], mb[reader.streams.labels])
trainer.train_minibatch({criterion.arguments[0]: mb[reader.streams.features], criterion.arguments[1]: mb[reader.streams.labels]})
sample_count += mb[reader.streams.labels].num_samples # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
loss, metric, actual_samples = progress_printer.epoch_summary(with_metric=True)
model.save(os.path.join(model_path, "ConvNet_CIFAR10_DataAug_{}.dnn".format(epoch)))
# return evaluation error.
return loss, metric # return values from last epoch
def ffnet(debug_output=False):
input_dim = 2
num_output_classes = 2
num_hidden_layers = 2
hidden_layers_dim = 50
# Input variables denoting the features and label data
input = input_variable((input_dim), np.float32)
label = input_variable((num_output_classes), np.float32)
# Instantiate the feedforward classification model
netout = fully_connected_classifier_net(
input, num_output_classes, hidden_layers_dim, num_hidden_layers, sigmoid)
ce = cross_entropy_with_softmax(netout, label)
pe = classification_error(netout, label)
# Instantiate the trainer object to drive the model training
trainer = Trainer(netout, ce, pe, [sgd(netout.parameters(), lr=0.02)])
# Get minibatches of training data and perform model training
minibatch_size = 25
num_samples_per_sweep = 10000
num_sweeps_to_train_with = 2
num_minibatches_to_train = (
num_samples_per_sweep * num_sweeps_to_train_with) / minibatch_size
training_progress_output_freq = 60
if debug_output:
training_progress_output_freq = training_progress_output_freq/3
for i in range(0, int(num_minibatches_to_train)):
features, labels = generate_random_data(
minibatch_size, input_dim, num_output_classes)
# Specify the mapping of input variables in the model to actual
# minibatch data to be trained with
trainer.train_minibatch({input: features, label: labels})
print_training_progress(trainer, i, training_progress_output_freq)
test_features, test_labels = generate_random_data(
minibatch_size, input_dim, num_output_classes)
avg_error = trainer.test_minibatch(
{input: test_features, label: test_labels})
return avg_error
def _train_backcompatible_test(z, loss, eval_error,
f_input, l_input,
num_output_classes,
steps):
np.random.seed(0)
input_dim = 2
lr_schedule = learning_parameter_schedule(0.5)
learner = sgd(z.parameters, lr_schedule)
trainer = Trainer(z, (loss, eval_error), [learner])
minibatch_size = 10
for i in range(steps):
features, labels = _generate_random_data_sample(
minibatch_size, input_dim, num_output_classes)
trainer.train_minibatch({f_input: features, l_input: labels})
def _train(z, loss, eval_error,
f_input, l_input,
num_output_classes,
steps):
np.random.seed(0)
input_dim = 2
lr_schedule = C.learning_parameter_schedule(0.5)
#now we want the learning be compatible with the way in the literature without the per sample benefit:
learner = sgd(z.parameters, lr_schedule, minibatch_size = C.learners.IGNORE)
trainer = Trainer(z, (loss, eval_error), [learner])
minibatch_size = 10
for i in range(steps):
features, labels = _generate_random_data_sample(
minibatch_size, input_dim, num_output_classes)
trainer.train_minibatch({f_input: features, l_input: labels})
def cargarRedDesdeArchivo(archivo):
input_dim = 800;
num_output_classes = 3;
feature = input((input_dim), np.float32);
label = input((num_output_classes), np.float32)
netout = crearRed(input_dim, 3, feature);
ce = cross_entropy_with_softmax(netout, label)
pe = classification_error(netout, label)
lr_per_minibatch=learning_rate_schedule(0.5, UnitType.minibatch)
# Instantiate the trainer object to drive the model training
learner = sgd(netout.parameters, lr=lr_per_minibatch)
progress_printer = ProgressPrinter(1)
trainer = Trainer(netout, (ce, pe), learner, progress_printer)
trainer.restore_from_checkpoint(archivo);
return netout;
def Evaluator(model, criterion):
from cntk import Trainer
from cntk.learners import momentum_sgd, momentum_schedule_per_sample
loss, metric = Trainer._get_loss_metric(criterion)
parameters = set(loss.parameters)
if model:
parameters |= set(model.parameters)
if metric:
parameters |= set(metric.parameters)
dummy_learner = momentum_sgd(tuple(parameters),
lr = learning_parameter_schedule(1),
momentum = momentum_schedule_per_sample(0))
return Trainer(model, (loss, metric), dummy_learner)
def Evaluator(model, criterion):
from cntk import Trainer
from cntk.learners import momentum_sgd, learning_rate_schedule, UnitType, momentum_as_time_constant_schedule
loss, metric = Trainer._get_loss_metric(criterion)
parameters = set(loss.parameters)
if model:
parameters |= set(model.parameters)
if metric:
parameters |= set(metric.parameters)
dummy_learner = momentum_sgd(tuple(parameters),
lr = learning_rate_schedule(1, UnitType.minibatch),
momentum = momentum_as_time_constant_schedule(0))
return Trainer(model, (loss, metric), dummy_learner)
def ffnet():
input_dim = 2
num_output_classes = 2
num_hidden_layers = 2
hidden_layers_dim = 50
# Input variables denoting the features and label data
input = input_variable((input_dim), np.float32)
label = input_variable((num_output_classes), np.float32)
# Instantiate the feedforward classification model
netout = fully_connected_classifier_net(
input, num_output_classes, hidden_layers_dim, num_hidden_layers, sigmoid)
ce = cross_entropy_with_softmax(netout, label)
pe = classification_error(netout, label)
lr_per_minibatch=learning_rate_schedule(0.5, UnitType.minibatch)
# Instantiate the trainer object to drive the model training
trainer = Trainer(netout, ce, pe, sgd(netout.parameters, lr=lr_per_minibatch))
# Get minibatches of training data and perform model training
minibatch_size = 25
pp = ProgressPrinter(128)
for i in range(1024):
features, labels = generate_random_data(
minibatch_size, input_dim, num_output_classes)
# Specify the mapping of input variables in the model to actual
# minibatch data to be trained with
trainer.train_minibatch({input: features, label: labels})
pp.update_with_trainer(trainer)
pp.epoch_summary()
test_features, test_labels = generate_random_data(
minibatch_size, input_dim, num_output_classes)
avg_error = trainer.test_minibatch(
{input: test_features, label: test_labels})
return avg_error
def cifar_resnet():
image_height = 32
image_width = 32
num_channels = 3
num_classes = 10
feats_stream_name = 'features'
labels_stream_name = 'labels'
minibatch_source = create_mb_source(feats_stream_name, labels_stream_name,
image_height, image_width, num_channels, num_classes)
features_si = minibatch_source.stream_info(feats_stream_name)
labels_si = minibatch_source.stream_info(labels_stream_name)
# Input variables denoting the features and label data
image_input = input_variable((num_channels, image_height, image_width), features_si.m_element_type)
label_var = input_variable((num_classes), features_si.m_element_type)
# Instantiate the resnet classification model
classifier_output = resnet_classifer(image_input, num_classes)
ce = cross_entropy_with_softmax(classifier_output, label_var)
pe = classification_error(classifier_output, label_var)
# Instantiate the trainer object to drive the model training
lr = learning_rates_per_sample(0.0078125)
trainer = Trainer(classifier_output, ce, pe, [sgd_learner(classifier_output.owner.parameters(), lr)])
# Get minibatches of images to train with and perform model training
mb_size = 32
training_progress_output_freq = 20
num_mbs = 1000
for i in range(0, num_mbs):
mb=minibatch_source.get_next_minibatch(mb_size)
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
arguments = {image_input : mb[features_si].m_data, label_var : mb[labels_si].m_data}
trainer.train_minibatch(arguments)
print_training_progress(trainer, i, training_progress_output_freq)
def ffnet(data, labels):
input_dim = 800
num_output_classes = 3
num_hidden_layers = 2
hidden_layers_dim = 50
# Input variables denoting the features and label data
feature = input((input_dim), np.float32)
label = input((num_output_classes), np.float32)
netout = Sequential([For(range(num_hidden_layers), lambda i: Dense(hidden_layers_dim, activation=sigmoid)),
Dense(num_output_classes)])(feature)
ce = cross_entropy_with_softmax(netout, label)
pe = classification_error(netout, label)
lr_per_minibatch=learning_rate_schedule(0.5, UnitType.minibatch)
# Instantiate the trainer object to drive the model training
learner = sgd(netout.parameters, lr=lr_per_minibatch)
progress_printer = ProgressPrinter(128)
trainer = Trainer(netout, (ce, pe), learner, progress_printer)
# Get minibatches of training data and perform model training
minibatch_size = 25
features, labels = generate_stock_data(minibatch_size);
for i in range(1024):
# features, labels = generate_random_data(
# minibatch_size, input_dim, num_output_classes)
# Specify the mapping of input variables in the model to actual
# minibatch data to be trained with
trainer.train_minibatch({feature: features, label: labels})
trainer.summarize_training_progress()
test_features, test_labels = generate_random_data(
minibatch_size, input_dim, num_output_classes)
avg_error = trainer.test_minibatch(
{feature: test_features, label: test_labels})
return avg_error
def simple_mnist():
input_dim = 784
num_output_classes = 10
num_hidden_layers = 1
hidden_layers_dim = 200
# Input variables denoting the features and label data
features = input_variable(input_dim, np.float32)
label = input_variable(num_output_classes, np.float32)
# Instantiate the feedforward classification model
scaled_input = element_times(constant(0.00390625), features)
netout = fully_connected_classifier_net(
scaled_input, num_output_classes, hidden_layers_dim, num_hidden_layers, relu)
ce = cross_entropy_with_softmax(netout, label)
pe = classification_error(netout, label)
try:
rel_path = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
*"Image/MNIST/v0/Train-28x28_cntk_text.txt".split("/"))
except KeyError:
rel_path = os.path.join(*"../Image/DataSets/MNIST/Train-28x28_cntk_text.txt".split("/"))
path = os.path.normpath(os.path.join(abs_path, rel_path))
check_path(path)
reader_train = create_reader(path, True, input_dim, num_output_classes)
input_map = {
features: reader_train.streams.features,
label: reader_train.streams.labels
}
# Instantiate progress writers.
logdir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "mnist_log")
tensorboard_writer = TensorBoardProgressWriter(freq=1, log_dir=logdir, model=netout)
progress_printer = ProgressPrinter(freq=10, tag='Training')
# Instantiate the trainer object to drive the model training
lr_per_minibatch = learning_rate_schedule(0.2, UnitType.minibatch)
learner = sgd(netout.parameters, lr=lr_per_minibatch)
trainer = Trainer(netout, (ce, pe), learner, [tensorboard_writer, progress_printer])
# Get minibatches of images to train with and perform model training
minibatch_size = 64
num_samples_per_sweep = 6000
num_sweeps_to_train_with = 2
num_minibatches_to_train = (num_samples_per_sweep * num_sweeps_to_train_with) / minibatch_size
for minibatch_idx in range(0, int(num_minibatches_to_train)):
trainer.train_minibatch(reader_train.next_minibatch(minibatch_size, input_map=input_map))
# Log max/min/mean of each parameter tensor, so that we can confirm that the parameters change indeed.
# Don't want to do that very often though, otherwise will spend too much time computing min/max/mean.
if minibatch_idx % 10 == 9:
for p in netout.parameters:
tensorboard_writer.write_value(p.uid + "/max", reduce_max(p).eval(), minibatch_idx)
tensorboard_writer.write_value(p.uid + "/min", reduce_min(p).eval(), minibatch_idx)
tensorboard_writer.write_value(p.uid + "/mean", reduce_mean(p).eval(), minibatch_idx)
trainer.summarize_training_progress()
# Load test data
try:
rel_path = os.path.join(os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
*"Image/MNIST/v0/Test-28x28_cntk_text.txt".split("/"))
except KeyError:
rel_path = os.path.join(*"../Image/DataSets/MNIST/Test-28x28_cntk_text.txt".split("/"))
path = os.path.normpath(os.path.join(abs_path, rel_path))
check_path(path)
reader_test = create_reader(path, False, input_dim, num_output_classes)
input_map = {
features: reader_test.streams.features,
label: reader_test.streams.labels
}
# Test data for trained model
test_minibatch_size = 1024
num_samples = 10000
num_minibatches_to_test = num_samples / test_minibatch_size
test_result = 0.0
for i in range(0, int(num_minibatches_to_test)):
mb = reader_test.next_minibatch(test_minibatch_size, input_map=input_map)
test_result += trainer.test_minibatch(mb)
# Average of evaluation errors of all test minibatches
trainer.summarize_test_progress()
return test_result / num_minibatches_to_test
def train_and_evaluate(reader_train, reader_test, max_epochs):
# Input variables denoting the features and label data
input_var = input_variable((num_channels, image_height, image_width))
label_var = input_variable((num_classes))
# apply model to input
model = create_basic_model(input_var)
#model = create_basic_model_layer(input_var)
z = model['fc5']
#
# Training action
#
# loss and metric
ce = cross_entropy_with_softmax(z, label_var)
pe = classification_error(z, label_var)
# training config
epoch_size = 50000
minibatch_size = 64
# For basic model
lr_per_sample = [0.00015625]*10+[0.000046875]*10+[0.0000156]
momentum_per_sample = 0.9 ** (1.0 / minibatch_size) # BUGBUG: why does this work? Should be as time const, no?
l2_reg_weight = 0.03
# For basic model with batch normalization
# lr_per_sample = [0.00046875]*7+[0.00015625]*10+[0.000046875]*10+[0.000015625]
# momentum_per_sample = 0
# l2_reg_weight = 0
# trainer object
lr_schedule = learning_rate_schedule(lr_per_sample, units=epoch_size)
learner = momentum_sgd(z.parameters, lr_schedule, momentum_per_sample,
l2_regularization_weight = l2_reg_weight)
trainer = Trainer(z, ce, pe, [learner])
# define mapping from reader streams to network inputs
input_map = {
input_var: reader_train.streams.features,
label_var: reader_train.streams.labels
}
log_number_of_parameters(z) ; print()
progress_printer = ProgressPrinter(tag='Training')
# perform model training
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = reader_train.next_minibatch(min(minibatch_size, epoch_size - sample_count), input_map=input_map) # fetch minibatch.
trainer.train_minibatch(data) # update model with it
sample_count += data[label_var].num_samples # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
progress_printer.epoch_summary(with_metric=True)
#
# Evaluation action
# TODO: This should be a separate function call.
#
epoch_size = 10000
minibatch_size = 16
# process minibatches and evaluate the model
metric_numer = 0
metric_denom = 0
sample_count = 0
minibatch_index = 0
#progress_printer = ProgressPrinter(freq=100, first=10, tag='Eval')
while sample_count < epoch_size:
current_minibatch = min(minibatch_size, epoch_size - sample_count)
# Fetch next test min batch.
data = reader_train.next_minibatch(current_minibatch, input_map=input_map)
#data = reader_test.next_minibatch(current_minibatch)
# minibatch data to be trained with
#metric_numer += trainer.test_minibatch(input_map) * current_minibatch
metric_numer += trainer.test_minibatch(data) * current_minibatch
metric_denom += current_minibatch
# Keep track of the number of samples processed so far.
sample_count += data[label_var].num_samples
minibatch_index += 1
if current_minibatch != minibatch_size:
break
print("")
print("Final Results: Minibatch[1-{}]: errs = {:0.1f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom))
print("")
def train_lm(training_file, epochs, max_num_minibatches):
# load the data and vocab
data, char_to_ix, ix_to_char, data_size, vocab_dim = load_data_and_vocab(
training_file)
# Model the source and target inputs to the model
input_sequence, label_sequence = create_inputs(vocab_dim)
# create the model
model = create_model(vocab_dim)
# and apply it to the input sequence
z = model(input_sequence)
# setup the criterions (loss and metric)
ce = cross_entropy_with_softmax(z, label_sequence)
errs = classification_error(z, label_sequence)
# Instantiate the trainer object to drive the model training
lr_per_sample = learning_rate_schedule(0.001, UnitType.sample)
momentum_time_constant = momentum_as_time_constant_schedule(1100)
clipping_threshold_per_sample = 5.0
gradient_clipping_with_truncation = True
learner = momentum_sgd(
z.parameters,
lr_per_sample,
momentum_time_constant,
gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
gradient_clipping_with_truncation=gradient_clipping_with_truncation)
progress_printer = ProgressPrinter(freq=100, tag='Training')
trainer = Trainer(z, (ce, errs), learner, progress_printer)
sample_freq = 1000
minibatches_per_epoch = min(data_size // minibatch_size,
max_num_minibatches // epochs)
# print out some useful training information
log_number_of_parameters(z)
print("Running %d epochs with %d minibatches per epoch" %
(epochs, minibatches_per_epoch))
print()
for e in range(0, epochs):
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
# If it's the start of the data, we specify that we are looking at a new sequence (True)
mask = [True]
for b in range(0, minibatches_per_epoch):
# get the data
features, labels = get_data(b, minibatch_size, data, char_to_ix,
vocab_dim)
arguments = ({
input_sequence: features,
label_sequence: labels
}, mask)
mask = [False]
trainer.train_minibatch(arguments)
global_minibatch = e * minibatches_per_epoch + b
if global_minibatch % sample_freq == 0:
print(sample(z, ix_to_char, vocab_dim, char_to_ix))
model_filename = "models/shakespeare_epoch%d.dnn" % (e + 1)
z.save_model(model_filename)
print("Saved model to '%s'" % model_filename)
def train(reader, model, max_epochs):
# Input variables denoting the features and label data
query = Input(input_dim, is_sparse=False)
slot_labels = Input(num_labels,
is_sparse=True) # TODO: make sparse once it works
# apply model to input
z = model(query)
# loss and metric
ce = cross_entropy_with_softmax(z, slot_labels)
pe = classification_error(z, slot_labels)
# training config
epoch_size = 36000
minibatch_size = 70
num_mbs_to_show_result = 100
momentum_as_time_constant = minibatch_size / -math.log(
0.9) # TODO: Change to round number. This is 664.39. 700?
lr_per_sample = [0.003] * 2 + [0.0015] * 12 + [
0.0003
] # LR schedule over epochs (we don't run that mayn epochs, but if we did, these are good values)
# trainer object
lr_schedule = learning_rate_schedule(lr_per_sample, units=epoch_size)
learner = adam_sgd(z.parameters,
lr_per_sample=lr_schedule,
momentum_time_constant=momentum_as_time_constant,
low_memory=True,
gradient_clipping_threshold_per_sample=15,
gradient_clipping_with_truncation=True)
trainer = Trainer(z, ce, pe, [learner])
# define mapping from reader streams to network inputs
input_map = {
query: reader.streams.query,
slot_labels: reader.streams.slot_labels
}
# process minibatches and perform model training
log_number_of_parameters(z)
print()
progress_printer = ProgressPrinter(freq=100, first=10,
tag='Training') # more detailed logging
#progress_printer = ProgressPrinter(tag='Training')
t = 0
for epoch in range(max_epochs): # loop over epochs
epoch_end = (epoch + 1) * epoch_size
while t < epoch_end: # loop over minibatches on the epoch
# BUGBUG? The change of minibatch_size parameter vv has no effect.
data = reader.next_minibatch(
min(minibatch_size,
epoch_end - t), input_map=input_map) # fetch minibatch
trainer.train_minibatch(data) # update model with it
t += data[
slot_labels].num_samples # count samples processed so far
progress_printer.update_with_trainer(
trainer, with_metric=True) # log progress
#def trace_node(name):
# nl = [n for n in z.parameters if n.name() == name]
# if len(nl) > 0:
# print (name, np.asarray(nl[0].value))
#trace_node('W')
#trace_node('stabilizer_param')
loss, metric, actual_samples = progress_printer.epoch_summary(
with_metric=True)
return loss, metric
def train_model(base_model_file, train_map_file, test_map_file, input_resolution,
num_epochs, mb_size, max_train_images, lr_per_mb, momentum_per_mb, l2_reg_weight,
dropout_rate, freeze_weights, num_channels = 3):
#init
image_width = input_resolution
image_height = input_resolution
epoch_size_test = len(readTable(test_map_file))
epoch_size_train = len(readTable(train_map_file))
epoch_size_train = min(epoch_size_train, max_train_images)
num_classes = max(ToIntegers(getColumn(readTable(train_map_file), 1))) + 1
# Create the minibatch source
minibatch_source_train = create_mb_source(train_map_file, image_width, image_height, num_channels, num_classes, True)
minibatch_source_test = create_mb_source(test_map_file, image_width, image_height, num_channels, num_classes, False)
# Define mapping from reader streams to network inputs
label_input = input_variable(num_classes)
image_input = input_variable((num_channels, image_height, image_width), name = "input")
input_map = {
image_input: minibatch_source_train['features'],
label_input: minibatch_source_train['labels']
}
# Instantiate the transfer learning model and loss function
cntkModel = create_model(base_model_file, image_input, num_classes, dropout_rate, freeze_weights)
ce = cross_entropy_with_softmax(cntkModel, label_input)
pe = classification_error(cntkModel, label_input)
# Instantiate the trainer object
lr_schedule = learning_rate_schedule(lr_per_mb, unit=UnitType.minibatch)
mm_schedule = momentum_schedule(momentum_per_mb)
learner = momentum_sgd(cntkModel.parameters, lr_schedule, mm_schedule, l2_regularization_weight=l2_reg_weight)
progress_writers = [ProgressPrinter(tag='Training', num_epochs=num_epochs)]
trainer = Trainer(cntkModel, (ce, pe), learner, progress_writers)
# Run training epochs
print("Training transfer learning model for {0} epochs (epoch_size_train = {1}).".format(num_epochs, epoch_size_train))
errsTest = []
errsTrain = []
log_number_of_parameters(cntkModel)
for epoch in range(num_epochs):
# Train model
err_numer = 0
sample_counts = 0
while sample_counts < epoch_size_train: # Loop over minibatches in the epoch
sample_count = min(mb_size, epoch_size_train - sample_counts)
data = minibatch_source_train.next_minibatch(sample_count, input_map = input_map)
trainer.train_minibatch(data) # Update model with it
sample_counts += sample_count # Count samples processed so far
err_numer += trainer.previous_minibatch_evaluation_average * sample_count
if sample_counts % (100 * mb_size) == 0:
print ("Training: processed {0} samples".format(sample_counts))
# Visualize training images
# img_data = data[image_input].asarray()
# for i in range(len(img_data)):
# debugImg = img_data[i].squeeze().swapaxes(0, 1).swapaxes(1, 2) / 255.0
# imshow(debugImg)
# Compute accuracy on training and test sets
errsTrain.append(err_numer / float(sample_counts))
trainer.summarize_training_progress()
errsTest.append(cntkComputeTestError(trainer, minibatch_source_test, mb_size, epoch_size_test, input_map))
trainer.summarize_test_progress()
# Plot training progress
plt.plot(errsTrain, 'b-', errsTest, 'g-')
plt.xlabel('Epoch number')
plt.ylabel('Error')
plt.title('Training error (blue), test error (green)')
plt.draw()
return cntkModel
def train_and_evaluate(reader_train, reader_test, network_name, epoch_size, max_epochs, profiler_dir=None,
model_dir=None, log_dir=None, tensorboard_logdir=None, gen_heartbeat=False):
set_computation_network_trace_level(0)
# Input variables denoting the features and label data
input_var = C.input_variable((num_channels, image_height, image_width), name='features')
label_var = C.input_variable((num_classes))
# create model, and configure learning parameters
if network_name == 'resnet20':
z = create_cifar10_model(input_var, 3, num_classes)
lr_per_mb = [1.0]*80+[0.1]*40+[0.01]
elif network_name == 'resnet110':
z = create_cifar10_model(input_var, 18, num_classes)
lr_per_mb = [0.1]*1+[1.0]*80+[0.1]*40+[0.01]
else:
raise RuntimeError("Unknown model name!")
# loss and metric
ce = cross_entropy_with_softmax(z, label_var)
pe = classification_error(z, label_var)
# shared training parameters
minibatch_size = 128
momentum_time_constant = -minibatch_size/np.log(0.9)
l2_reg_weight = 0.0001
# Set learning parameters
lr_per_sample = [lr/minibatch_size for lr in lr_per_mb]
lr_schedule = learning_rate_schedule(lr_per_sample, epoch_size=epoch_size, unit=UnitType.sample)
mm_schedule = momentum_as_time_constant_schedule(momentum_time_constant)
# progress writers
progress_writers = [ProgressPrinter(tag='Training', log_to_file=log_dir, num_epochs=max_epochs, gen_heartbeat=gen_heartbeat)]
tensorboard_writer = None
if tensorboard_logdir is not None:
tensorboard_writer = TensorBoardProgressWriter(freq=10, log_dir=tensorboard_logdir, model=z)
progress_writers.append(tensorboard_writer)
# trainer object
learner = momentum_sgd(z.parameters, lr_schedule, mm_schedule,
l2_regularization_weight = l2_reg_weight)
trainer = Trainer(z, (ce, pe), learner, progress_writers)
# define mapping from reader streams to network inputs
input_map = {
input_var: reader_train.streams.features,
label_var: reader_train.streams.labels
}
log_number_of_parameters(z) ; print()
# perform model training
if profiler_dir:
start_profiler(profiler_dir, True)
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = reader_train.next_minibatch(min(minibatch_size, epoch_size-sample_count), input_map=input_map) # fetch minibatch.
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
trainer.summarize_training_progress()
# Log mean of each parameter tensor, so that we can confirm that the parameters change indeed.
if tensorboard_writer:
for parameter in z.parameters:
tensorboard_writer.write_value(parameter.uid + "/mean", reduce_mean(parameter).eval(), epoch)
if model_dir:
z.save(os.path.join(model_dir, network_name + "_{}.dnn".format(epoch)))
enable_profiler() # begin to collect profiler data after first epoch
if profiler_dir:
stop_profiler()
# Evaluation parameters
test_epoch_size = 10000
minibatch_size = 16
# process minibatches and evaluate the model
metric_numer = 0
metric_denom = 0
sample_count = 0
while sample_count < test_epoch_size:
current_minibatch = min(minibatch_size, test_epoch_size - sample_count)
# Fetch next test min batch.
data = reader_test.next_minibatch(current_minibatch, input_map=input_map)
# minibatch data to be trained with
metric_numer += trainer.test_minibatch(data) * current_minibatch
metric_denom += current_minibatch
# Keep track of the number of samples processed so far.
sample_count += data[label_var].num_samples
print("")
trainer.summarize_test_progress()
print("")
return metric_numer/metric_denom
def train_model(reader, reader_test, model, epoch_size=50000, max_epochs=80):
# declare the model's input dimension
# Training does not require this, but it is needed for deployment.
model.update_signature((num_channels, image_height, image_width))
# criterion function. This is what is being trained trained.
# Model gets "sandwiched" between normalization (not part of model proper) and criterion.
criterion = create_criterion_function(model, normalize=lambda x: x / 256)
#debughelpers.dump_function(criterion, 'criterion')
#from cntk.logging.graph import plot
#plot(criterion, filename=os.path.join(model_path, "ConvNet_CIFAR10_DataAug.pdf"))
# iteration parameters
minibatch_size = 64
#epoch_size = 1000 ; max_epochs = 1 # for faster testing
# learning parameters
learner = momentum_sgd(
model.parameters,
lr=learning_rate_schedule([0.0015625] * 20 + [0.00046875] * 20 +
[0.00015625] * 20 + [0.000046875] * 10 +
[0.000015625],
unit=UnitType.sample,
epoch_size=epoch_size),
momentum=momentum_as_time_constant_schedule([0] * 20 + [600] * 20 +
[1200],
epoch_size=epoch_size),
l2_regularization_weight=0.002)
# trainer object
trainer = Trainer(None, criterion, learner)
# perform model training
log_number_of_parameters(model)
print()
progress_printer = ProgressPrinter(tag='Training', num_epochs=max_epochs)
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
mb = reader.next_minibatch(
min(minibatch_size,
epoch_size - sample_count)) # fetch minibatch.
#trainer.train_minibatch(mb[reader.streams.features], mb[reader.streams.labels])
trainer.train_minibatch({
criterion.arguments[0]:
mb[reader.streams.features],
criterion.arguments[1]:
mb[reader.streams.labels]
})
sample_count += mb[
reader.streams.
labels].num_samples # count samples processed so far
progress_printer.update_with_trainer(
trainer, with_metric=True) # log progress
loss, metric, actual_samples = progress_printer.epoch_summary(
with_metric=True)
model.save(
os.path.join(model_path,
"ConvNet_CIFAR10_DataAug_{}.dnn".format(epoch)))
# return evaluation error.
return loss, metric # return values from last epoch
def sequence_to_sequence_translator(debug_output=False, run_test=False):
input_vocab_dim = 69
label_vocab_dim = 69
# network complexity; initially low for faster testing
hidden_dim = 256
num_layers = 1
# Source and target inputs to the model
batch_axis = Axis.default_batch_axis()
input_seq_axis = Axis('inputAxis')
label_seq_axis = Axis('labelAxis')
input_dynamic_axes = [batch_axis, input_seq_axis]
raw_input = input_variable(shape=(input_vocab_dim),
dynamic_axes=input_dynamic_axes,
name='raw_input')
label_dynamic_axes = [batch_axis, label_seq_axis]
raw_labels = input_variable(shape=(label_vocab_dim),
dynamic_axes=label_dynamic_axes,
name='raw_labels')
# Instantiate the sequence to sequence translation model
input_sequence = raw_input
# Drop the sentence start token from the label, for decoder training
label_sequence = sequence.slice(raw_labels, 1,
0) # <s> A B C </s> --> A B C </s>
label_sentence_start = sequence.first(raw_labels) # <s>
is_first_label = sequence.is_first(label_sequence) # <s> 0 0 0 ...
label_sentence_start_scattered = sequence.scatter(label_sentence_start,
is_first_label)
# Encoder
encoder_outputH = stabilize(input_sequence)
for i in range(0, num_layers):
(encoder_outputH,
encoder_outputC) = LSTMP_component_with_self_stabilization(
encoder_outputH.output, hidden_dim, hidden_dim, future_value,
future_value)
thought_vectorH = sequence.first(encoder_outputH)
thought_vectorC = sequence.first(encoder_outputC)
thought_vector_broadcastH = sequence.broadcast_as(thought_vectorH,
label_sequence)
thought_vector_broadcastC = sequence.broadcast_as(thought_vectorC,
label_sequence)
# Decoder
decoder_history_hook = alias(
label_sequence, name='decoder_history_hook') # copy label_sequence
decoder_input = element_select(is_first_label,
label_sentence_start_scattered,
past_value(decoder_history_hook))
decoder_outputH = stabilize(decoder_input)
for i in range(0, num_layers):
if (i > 0):
recurrence_hookH = past_value
recurrence_hookC = past_value
else:
isFirst = sequence.is_first(label_sequence)
recurrence_hookH = lambda operand: element_select(
isFirst, thought_vector_broadcastH, past_value(operand))
recurrence_hookC = lambda operand: element_select(
isFirst, thought_vector_broadcastC, past_value(operand))
(decoder_outputH,
encoder_outputC) = LSTMP_component_with_self_stabilization(
decoder_outputH.output, hidden_dim, hidden_dim, recurrence_hookH,
recurrence_hookC)
decoder_output = decoder_outputH
# Softmax output layer
z = linear_layer(stabilize(decoder_output), label_vocab_dim)
# Criterion nodes
ce = cross_entropy_with_softmax(z, label_sequence)
errs = classification_error(z, label_sequence)
# network output for decoder history
net_output = hardmax(z)
# make a clone of the graph where the ground truth is replaced by the network output
ng = z.clone(CloneMethod.share,
{decoder_history_hook.output: net_output.output})
# Instantiate the trainer object to drive the model training
lr_per_minibatch = learning_rate_schedule(0.5, UnitType.minibatch)
momentum_time_constant = momentum_as_time_constant_schedule(1100)
clipping_threshold_per_sample = 2.3
gradient_clipping_with_truncation = True
learner = momentum_sgd(
z.parameters,
lr_per_minibatch,
momentum_time_constant,
gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
gradient_clipping_with_truncation=gradient_clipping_with_truncation)
trainer = Trainer(z, ce, errs, learner)
# setup data
train_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..",
"Data", "cmudict-0.7b.train-dev-20-21.ctf")
valid_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..",
"Data", "tiny.ctf")
# readers
randomize_data = True
if run_test:
randomize_data = False # because we want to get an exact error
train_reader = create_reader(train_path, randomize_data, input_vocab_dim,
label_vocab_dim)
train_bind = {
raw_input: train_reader.streams.features,
raw_labels: train_reader.streams.labels
}
# get the vocab for printing output sequences in plaintext
vocab_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..",
"Data", "cmudict-0.7b.mapping")
vocab = [w.strip() for w in open(vocab_path).readlines()]
i2w = {i: ch for i, ch in enumerate(vocab)}
# Get minibatches of sequences to train with and perform model training
i = 0
mbs = 0
minibatch_size = 72
epoch_size = 908241
max_epochs = 10
training_progress_output_freq = 500
# make things more basic for running a quicker test
if run_test:
epoch_size = 5000
max_epochs = 1
training_progress_output_freq = 30
valid_reader = create_reader(valid_path, False, input_vocab_dim,
label_vocab_dim)
valid_bind = {
find_arg_by_name('raw_input', ng): valid_reader.streams.features,
find_arg_by_name('raw_labels', ng): valid_reader.streams.labels
}
for epoch in range(max_epochs):
loss_numer = 0
metric_numer = 0
denom = 0
while i < (epoch + 1) * epoch_size:
# get next minibatch of training data
mb_train = train_reader.next_minibatch(minibatch_size,
input_map=train_bind)
trainer.train_minibatch(mb_train)
# collect epoch-wide stats
samples = trainer.previous_minibatch_sample_count
loss_numer += trainer.previous_minibatch_loss_average * samples
metric_numer += trainer.previous_minibatch_evaluation_average * samples
denom += samples
# every N MBs evaluate on a test sequence to visually show how we're doing
if mbs % training_progress_output_freq == 0:
mb_valid = valid_reader.next_minibatch(minibatch_size,
input_map=valid_bind)
e = ng.eval(mb_valid)
print_sequences(e, i2w)
print_training_progress(trainer, mbs,
training_progress_output_freq)
i += mb_train[raw_labels].num_samples
mbs += 1
print("--- EPOCH %d DONE: loss = %f, errs = %f ---" %
(epoch, loss_numer / denom, 100.0 * (metric_numer / denom)))
error1 = translator_test_error(z, trainer, input_vocab_dim,
label_vocab_dim)
z.save_model("seq2seq.dnn")
z.restore_model("seq2seq.dnn")
label_seq_axis = Axis('labelAxis')
label_sequence = sequence.slice(find_arg_by_name('raw_labels', z), 1, 0)
ce = cross_entropy_with_softmax(z, label_sequence)
errs = classification_error(z, label_sequence)
trainer = Trainer(z, ce, errs, [
momentum_sgd(z.parameters, lr_per_minibatch, momentum_time_constant,
clipping_threshold_per_sample,
gradient_clipping_with_truncation)
])
error2 = translator_test_error(z, trainer, input_vocab_dim,
label_vocab_dim)
assert error1 == error2
return error1
def train_model(base_model_file,
feature_node_name,
last_hidden_node_name,
image_width,
image_height,
num_channels,
num_classes,
train_map_file,
num_epochs,
max_images=-1,
freeze=False):
epoch_size = sum(1 for line in open(train_map_file))
if max_images > 0:
epoch_size = min(epoch_size, max_images)
# Create the minibatch source and input variables
minibatch_source = create_mb_source(train_map_file, image_width,
image_height, num_channels,
num_classes)
image_input = C.input_variable((num_channels, image_height, image_width))
label_input = C.input_variable(num_classes)
# Define mapping from reader streams to network inputs
input_map = {
image_input: minibatch_source[features_stream_name],
label_input: minibatch_source[label_stream_name]
}
# Instantiate the transfer learning model and loss function
tl_model = create_model(base_model_file, feature_node_name,
last_hidden_node_name, num_classes, image_input,
freeze)
ce = cross_entropy_with_softmax(tl_model, label_input)
pe = classification_error(tl_model, label_input)
# Instantiate the trainer object
lr_schedule = learning_rate_schedule(lr_per_mb, unit=UnitType.minibatch)
mm_schedule = momentum_schedule(momentum_per_mb)
learner = momentum_sgd(tl_model.parameters,
lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight)
progress_printer = ProgressPrinter(tag='Training', num_epochs=num_epochs)
trainer = Trainer(tl_model, (ce, pe), learner, progress_printer)
# Get minibatches of images and perform model training
print(
"Training transfer learning model for {0} epochs (epoch_size = {1}).".
format(num_epochs, epoch_size))
log_number_of_parameters(tl_model)
for epoch in range(num_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = minibatch_source.next_minibatch(min(
mb_size, epoch_size - sample_count),
input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
if sample_count % (100 * mb_size) == 0:
print("Processed {0} samples".format(sample_count))
trainer.summarize_training_progress()
return tl_model
def convnet_cifar10(debug_output=False):
set_computation_network_trace_level(0)
image_height = 32
image_width = 32
num_channels = 3
input_dim = image_height * image_width * num_channels
num_output_classes = 10
# Input variables denoting the features and label data
input_var = input_variable((num_channels, image_height, image_width), np.float32)
label_var = input_variable(num_output_classes, np.float32)
# Instantiate the feedforward classification model
input_removemean = minus(input_var, constant(128))
scaled_input = element_times(constant(0.00390625), input_removemean)
with default_options (activation=relu, pad=True):
z = Sequential([
LayerStack(2, lambda : [
Convolution((3,3), 64),
Convolution((3,3), 64),
MaxPooling((3,3), (2,2))
]),
LayerStack(2, lambda i: [
Dense([256,128][i]),
Dropout(0.5)
]),
Dense(num_output_classes, activation=None)
])(scaled_input)
ce = cross_entropy_with_softmax(z, label_var)
pe = classification_error(z, label_var)
reader_train = create_reader(os.path.join(data_path, 'Train_cntk_text.txt'), True, input_dim, num_output_classes)
# training config
epoch_size = 50000 # for now we manually specify epoch size
minibatch_size = 64
# Set learning parameters
lr_per_sample = [0.0015625]*10+[0.00046875]*10+[0.00015625]
lr_schedule = learning_rate_schedule(lr_per_sample, UnitType.sample, epoch_size)
mm_time_constant = [0]*20+[-minibatch_size/np.log(0.9)]
mm_schedule = momentum_as_time_constant_schedule(mm_time_constant, epoch_size)
l2_reg_weight = 0.002
# Instantiate the trainer object to drive the model training
learner = momentum_sgd(z.parameters, lr_schedule, mm_schedule, l2_regularization_weight = l2_reg_weight)
trainer = Trainer(z, ce, pe, learner)
# define mapping from reader streams to network inputs
input_map = {
input_var : reader_train.streams.features,
label_var : reader_train.streams.labels
}
log_number_of_parameters(z) ; print()
progress_printer = ProgressPrinter(tag='Training')
# Get minibatches of images to train with and perform model training
max_epochs = 30
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = reader_train.next_minibatch(min(minibatch_size, epoch_size - sample_count), input_map=input_map) # fetch minibatch.
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
progress_printer.epoch_summary(with_metric=True)
persist.save_model(z, os.path.join(model_path, "ConvNet_CIFAR10_{}.dnn".format(epoch)))
# Load test data
reader_test = create_reader(os.path.join(data_path, 'Test_cntk_text.txt'), False, input_dim, num_output_classes)
input_map = {
input_var : reader_test.streams.features,
label_var : reader_test.streams.labels
}
# Test data for trained model
epoch_size = 10000
minibatch_size = 16
# process minibatches and evaluate the model
metric_numer = 0
metric_denom = 0
sample_count = 0
minibatch_index = 0
while sample_count < epoch_size:
current_minibatch = min(minibatch_size, epoch_size - sample_count)
# Fetch next test min batch.
data = reader_test.next_minibatch(current_minibatch, input_map=input_map)
# minibatch data to be trained with
metric_numer += trainer.test_minibatch(data) * current_minibatch
metric_denom += current_minibatch
# Keep track of the number of samples processed so far.
sample_count += data[label_var].num_samples
minibatch_index += 1
print("")
print("Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom))
print("")
return metric_numer/metric_denom
def train_and_evaluate(create_train_reader,
test_reader,
network_name,
max_epochs,
create_dist_learner,
scale_up=False):
set_computation_network_trace_level(0)
# Input variables denoting the features and label data
input_var = input_variable((num_channels, image_height, image_width))
label_var = input_variable((num_classes))
# create model, and configure learning parameters
if network_name == 'resnet20':
z = create_cifar10_model(input_var, 3, num_classes)
lr_per_mb = [1.0] * 80 + [0.1] * 40 + [0.01]
elif network_name == 'resnet110':
z = create_cifar10_model(input_var, 18, num_classes)
lr_per_mb = [0.1] * 1 + [1.0] * 80 + [0.1] * 40 + [0.01]
else:
return RuntimeError("Unknown model name!")
# loss and metric
ce = cross_entropy_with_softmax(z, label_var)
pe = classification_error(z, label_var)
# shared training parameters
epoch_size = 50000 # for now we manually specify epoch size
# NOTE: scaling up minibatch_size increases sample throughput. In 8-GPU machine,
# ResNet110 samples-per-second is ~7x of single GPU, comparing to ~3x without scaling
# up. However, bigger minimatch size on the same number of samples means less updates,
# thus leads to higher training error. This is a trade-off of speed and accuracy
minibatch_size = 128 * (distributed.Communicator.num_workers()
if scale_up else 1)
momentum_time_constant = -minibatch_size / np.log(0.9)
l2_reg_weight = 0.0001
# Set learning parameters
lr_per_sample = [lr / minibatch_size for lr in lr_per_mb]
lr_schedule = learning_rate_schedule(lr_per_sample,
epoch_size=epoch_size,
unit=UnitType.sample)
mm_schedule = momentum_as_time_constant_schedule(momentum_time_constant)
# trainer object
learner = create_dist_learner(
momentum_sgd(z.parameters,
lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight))
trainer = Trainer(z, ce, pe, learner)
total_number_of_samples = max_epochs * epoch_size
train_reader = create_train_reader(total_number_of_samples)
# define mapping from reader streams to network inputs
input_map = {
input_var: train_reader.streams.features,
label_var: train_reader.streams.labels
}
log_number_of_parameters(z)
print()
progress_printer = ProgressPrinter(tag='Training')
# perform model training
current_epoch = 0
updated = True
while updated:
data = train_reader.next_minibatch(
minibatch_size, input_map=input_map) # fetch minibatch.
updated = trainer.train_minibatch(data) # update model with it
progress_printer.update_with_trainer(trainer,
with_metric=True) # log progress
epoch_index = int(trainer.total_number_of_samples_seen / epoch_size)
if current_epoch != epoch_index: # new epoch reached
progress_printer.epoch_summary(with_metric=True)
current_epoch = epoch_index
trainer.save_checkpoint(
os.path.join(model_path,
network_name + "_{}.dnn".format(current_epoch)))
# Evaluation parameters
epoch_size = 10000
minibatch_size = 16
# process minibatches and evaluate the model
metric_numer = 0
metric_denom = 0
sample_count = 0
minibatch_index = 0
while True:
data = test_reader.next_minibatch(minibatch_size, input_map=input_map)
if not data: break
local_mb_samples = data[label_var].num_samples
metric_numer += trainer.test_minibatch(data) * local_mb_samples
metric_denom += local_mb_samples
minibatch_index += 1
print("")
print("Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(
minibatch_index + 1, (metric_numer * 100.0) / metric_denom,
metric_denom))
print("")
return metric_numer / metric_denom
def simple_mnist(debug_output=False):
input_dim = 784
num_output_classes = 10
num_hidden_layers = 1
hidden_layers_dim = 200
# Input variables denoting the features and label data
input = input_variable(input_dim, np.float32)
label = input_variable(num_output_classes, np.float32)
# Instantiate the feedforward classification model
scaled_input = element_times(constant(0.00390625), input)
netout = fully_connected_classifier_net(scaled_input, num_output_classes,
hidden_layers_dim,
num_hidden_layers, sigmoid)
ce = cross_entropy_with_softmax(netout, label)
pe = classification_error(netout, label)
try:
rel_path = os.path.join(
os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
*"Image/MNIST/v0/Train-28x28_cntk_text.txt".split("/"))
except KeyError:
rel_path = os.path.join(
*
"../../../../Examples/Image/Datasets/MNIST/Train-28x28_cntk_text.txt"
.split("/"))
path = os.path.normpath(os.path.join(abs_path, rel_path))
check_path(path)
feature_stream_name = 'features'
labels_stream_name = 'labels'
mb_source = text_format_minibatch_source(path, [
StreamConfiguration(feature_stream_name, input_dim),
StreamConfiguration(labels_stream_name, num_output_classes)
])
features_si = mb_source[feature_stream_name]
labels_si = mb_source[labels_stream_name]
# Instantiate the trainer object to drive the model training
trainer = Trainer(netout, ce, pe, [sgd(netout.parameters, lr=0.003125)])
# Get minibatches of images to train with and perform model training
minibatch_size = 32
num_samples_per_sweep = 60000
num_sweeps_to_train_with = 1
num_minibatches_to_train = (num_samples_per_sweep *
num_sweeps_to_train_with) / minibatch_size
training_progress_output_freq = 80
if debug_output:
training_progress_output_freq = training_progress_output_freq / 4
for i in range(0, int(num_minibatches_to_train)):
mb = mb_source.next_minibatch(minibatch_size)
# Specify the mapping of input variables in the model to actual
# minibatch data to be trained with
arguments = {input: mb[features_si], label: mb[labels_si]}
trainer.train_minibatch(arguments)
print_training_progress(trainer, i, training_progress_output_freq)
# Load test data
try:
rel_path = os.path.join(
os.environ['CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'],
*"Image/MNIST/v0/Test-28x28_cntk_text.txt".split("/"))
except KeyError:
rel_path = os.path.join(
*
"../../../../Examples/Image/Datasets/MNIST/Test-28x28_cntk_text.txt"
.split("/"))
path = os.path.normpath(os.path.join(abs_path, rel_path))
check_path(path)
test_mb_source = text_format_minibatch_source(path, [
StreamConfiguration(feature_stream_name, input_dim),
StreamConfiguration(labels_stream_name, num_output_classes)
],
randomize=False)
features_si = test_mb_source[feature_stream_name]
labels_si = test_mb_source[labels_stream_name]
# Test data for trained model
test_minibatch_size = 512
num_samples = 10000
num_minibatches_to_test = num_samples / test_minibatch_size
test_result = 0.0
for i in range(0, int(num_minibatches_to_test)):
mb = test_mb_source.next_minibatch(test_minibatch_size)
# Specify the mapping of input variables in the model to actual
# minibatch data to be tested with
arguments = {input: mb[features_si], label: mb[labels_si]}
eval_error = trainer.test_minibatch(arguments)
test_result = test_result + eval_error
# Average of evaluation errors of all test minibatches
return test_result / num_minibatches_to_test
#available activation function: relu, leaky_relu, sigmoid, tanh
if nettype == "cls":
model = create_model(input, output_dim, [25, 25, 25], sigmoid)
ce = cross_entropy_with_softmax(model, label)
lr_per_minibatch=learning_rate_schedule(0.25, UnitType.minibatch)
else:
if output_dim == 3:
model = create_model(input, output_dim, [100, 80, 50, 20], sigmoid)
else:
model = create_model(input, output_dim, [50, 30, 20], sigmoid)
lr_per_minibatch=learning_rate_schedule(0.25, UnitType.minibatch)
ce = squared_error(model, label)
pe = classification_error(model, label)
trainer = Trainer(model, (ce, pe), sgd(model.parameters, lr=lr_per_minibatch))
mini_batch_sz = 25
error = 0.0;
expFactor = 0.001;
for ep in range(epochs):
for i in range(0, N, mini_batch_sz):
j = i + mini_batch_sz
trainer.train_minibatch({input: features[i:j], label: labels[i:j]})
error = (1-expFactor)*error + expFactor*trainer.previous_minibatch_loss_average * mini_batch_sz;
if (ep+1) % refreshFreq == 0:
LogReport(ep+1, error)
if ep % refreshFreq != 0 :
LogReport(ep, 1.00)
def train_fast_rcnn(cfg):
# Train only if no model exists yet
model_path = cfg['MODEL_PATH']
if os.path.exists(model_path) and cfg["CNTK"].MAKE_MODE:
print("Loading existing model from %s" % model_path)
return load_model(model_path)
else:
# Input variables denoting features and labeled ground truth rois (as 5-tuples per roi)
image_input = input_variable(shape=(cfg.NUM_CHANNELS, cfg.IMAGE_HEIGHT,
cfg.IMAGE_WIDTH),
dynamic_axes=[Axis.default_batch_axis()],
name=cfg["MODEL"].FEATURE_NODE_NAME)
roi_proposals = input_variable(
(cfg.NUM_ROI_PROPOSALS, 4),
dynamic_axes=[Axis.default_batch_axis()],
name="roi_proposals")
label_targets = input_variable(
(cfg.NUM_ROI_PROPOSALS, cfg["DATA"].NUM_CLASSES),
dynamic_axes=[Axis.default_batch_axis()])
bbox_targets = input_variable(
(cfg.NUM_ROI_PROPOSALS, 4 * cfg["DATA"].NUM_CLASSES),
dynamic_axes=[Axis.default_batch_axis()])
bbox_inside_weights = input_variable(
(cfg.NUM_ROI_PROPOSALS, 4 * cfg["DATA"].NUM_CLASSES),
dynamic_axes=[Axis.default_batch_axis()])
# Instantiate the Fast R-CNN prediction model and loss function
loss, pred_error = create_fast_rcnn_model(image_input, roi_proposals,
label_targets, bbox_targets,
bbox_inside_weights, cfg)
if isinstance(loss, cntk.Variable):
loss = combine([loss])
if cfg["CNTK"].DEBUG_OUTPUT:
print("Storing graphs and models to %s." % cfg.OUTPUT_PATH)
plot(
loss,
os.path.join(cfg.OUTPUT_PATH,
"graph_frcn_train." + cfg["CNTK"].GRAPH_TYPE))
# Set learning parameters
lr_factor = cfg["CNTK"].LR_FACTOR
lr_per_sample_scaled = [
x * lr_factor for x in cfg["CNTK"].LR_PER_SAMPLE
]
mm_schedule = momentum_schedule(cfg["CNTK"].MOMENTUM_PER_MB)
l2_reg_weight = cfg["CNTK"].L2_REG_WEIGHT
epochs_to_train = cfg["CNTK"].MAX_EPOCHS
print("Using base model: {}".format(cfg["MODEL"].BASE_MODEL))
print("lr_per_sample: {}".format(lr_per_sample_scaled))
# --- train ---
# Instantiate the learners and the trainer object
params = loss.parameters
biases = [p for p in params if '.b' in p.name or 'b' == p.name]
others = [p for p in params if not p in biases]
bias_lr_mult = cfg["CNTK"].BIAS_LR_MULT
lr_schedule = learning_rate_schedule(lr_per_sample_scaled,
unit=UnitType.sample)
learner = momentum_sgd(others,
lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight,
unit_gain=False,
use_mean_gradient=True)
bias_lr_per_sample = [
v * bias_lr_mult for v in cfg["CNTK"].LR_PER_SAMPLE
]
bias_lr_schedule = learning_rate_schedule(bias_lr_per_sample,
unit=UnitType.sample)
bias_learner = momentum_sgd(biases,
bias_lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight,
unit_gain=False,
use_mean_gradient=True)
trainer = Trainer(None, (loss, pred_error), [learner, bias_learner])
# Get minibatches of images and perform model training
print("Training model for %s epochs." % epochs_to_train)
log_number_of_parameters(loss)
# Create the minibatch source
if cfg.USE_PRECOMPUTED_PROPOSALS:
proposal_provider = ProposalProvider.fromfile(
cfg["DATA"].TRAIN_PRECOMPUTED_PROPOSALS_FILE,
cfg.NUM_ROI_PROPOSALS)
else:
proposal_provider = ProposalProvider.fromconfig(cfg)
od_minibatch_source = ObjectDetectionMinibatchSource(
cfg["DATA"].TRAIN_MAP_FILE,
cfg["DATA"].TRAIN_ROI_FILE,
max_annotations_per_image=cfg.INPUT_ROIS_PER_IMAGE,
pad_width=cfg.IMAGE_WIDTH,
pad_height=cfg.IMAGE_HEIGHT,
pad_value=cfg["MODEL"].IMG_PAD_COLOR,
randomize=True,
use_flipping=cfg["TRAIN"].USE_FLIPPED,
max_images=cfg["DATA"].NUM_TRAIN_IMAGES,
num_classes=cfg["DATA"].NUM_CLASSES,
proposal_provider=proposal_provider,
provide_targets=True,
proposal_iou_threshold=cfg.BBOX_THRESH,
normalize_means=None
if not cfg.BBOX_NORMALIZE_TARGETS else cfg.BBOX_NORMALIZE_MEANS,
normalize_stds=None
if not cfg.BBOX_NORMALIZE_TARGETS else cfg.BBOX_NORMALIZE_STDS)
# define mapping from reader streams to network inputs
input_map = {
od_minibatch_source.image_si: image_input,
od_minibatch_source.proposals_si: roi_proposals,
od_minibatch_source.label_targets_si: label_targets,
od_minibatch_source.bbox_targets_si: bbox_targets,
od_minibatch_source.bbiw_si: bbox_inside_weights
}
progress_printer = ProgressPrinter(tag='Training',
num_epochs=epochs_to_train,
gen_heartbeat=True)
for epoch in range(epochs_to_train): # loop over epochs
sample_count = 0
while sample_count < cfg[
"DATA"].NUM_TRAIN_IMAGES: # loop over minibatches in the epoch
data = od_minibatch_source.next_minibatch(min(
cfg.MB_SIZE, cfg["DATA"].NUM_TRAIN_IMAGES - sample_count),
input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(
trainer, with_metric=True) # log progress
if sample_count % 100 == 0:
print("Processed {} samples".format(sample_count))
progress_printer.epoch_summary(with_metric=True)
eval_model = create_fast_rcnn_eval_model(loss, image_input,
roi_proposals, cfg)
eval_model.save(cfg['MODEL_PATH'])
return eval_model
def train_sequence_classifier(debug_output=False):
input_dim = 2000
cell_dim = 25
hidden_dim = 25
embedding_dim = 50
num_output_classes = 5
# Input variables denoting the features and label data
features = input_variable(shape=input_dim, is_sparse=True)
label = input_variable(num_output_classes, dynamic_axes=[
Axis.default_batch_axis()])
# Instantiate the sequence classification model
classifier_output = LSTM_sequence_classifer_net(
features, num_output_classes, embedding_dim, hidden_dim, cell_dim)
ce = cross_entropy_with_softmax(classifier_output, label)
pe = classification_error(classifier_output, label)
rel_path = r"../../../../Tests/EndToEndTests/Text/SequenceClassification/Data/Train.ctf"
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), rel_path)
feature_stream_name = 'features'
labels_stream_name = 'labels'
mb_source = text_format_minibatch_source(path, [
StreamConfiguration(feature_stream_name, input_dim, True, 'x'),
StreamConfiguration(labels_stream_name, num_output_classes, False, 'y')], 0)
features_si = mb_source[features]
labels_si = mb_source[label]
# Instantiate the trainer object to drive the model training
trainer = Trainer(classifier_output, ce, pe,
[sgd(classifier_output.parameters(), lr=0.0005)])
# Get minibatches of sequences to train with and perform model training
minibatch_size = 200
training_progress_output_freq = 10
i = 0
if debug_output:
training_progress_output_freq = training_progress_output_freq/3
while True:
mb = mb_source.get_next_minibatch(minibatch_size)
if len(mb) == 0:
break
# Specify the mapping of input variables in the model to actual
# minibatch data to be trained with
arguments = {features: mb[features_si],
label: mb[labels_si]}
trainer.train_minibatch(arguments)
print_training_progress(trainer, i, training_progress_output_freq)
i += 1
import copy
evaluation_average = copy.copy(
trainer.previous_minibatch_evaluation_average())
loss_average = copy.copy(trainer.previous_minibatch_loss_average())
return evaluation_average, loss_average
def train_and_evaluate(reader_train, reader_test, max_epochs):
# Input variables denoting the features and label data
input_var = input_variable((num_channels, image_height, image_width))
label_var = input_variable((num_classes))
# Normalize the input
feature_scale = 1.0 / 256.0
input_var_norm = element_times(feature_scale, input_var)
# apply model to input
z = create_vgg9_model(input_var_norm, 10)
#
# Training action
#
# loss and metric
ce = cross_entropy_with_softmax(z, label_var)
pe = classification_error(z, label_var)
# training config
epoch_size = 50000
minibatch_size = 64
# Set learning parameters
lr_per_minibatch = learning_rate_schedule([0.01]*10 + [0.003]*10 + [0.001], epoch_size, UnitType.minibatch)
momentum_time_constant = momentum_as_time_constant_schedule(-minibatch_size/np.log(0.9))
l2_reg_weight = 0.0001
# trainer object
learner = momentum_sgd(z.parameters,
lr = lr_per_minibatch, momentum = momentum_time_constant,
l2_regularization_weight = l2_reg_weight)
trainer = Trainer(z, ce, pe, learner)
# define mapping from reader streams to network inputs
input_map = {
input_var: reader_train.streams.features,
label_var: reader_train.streams.labels
}
log_number_of_parameters(z) ; print()
progress_printer = ProgressPrinter(tag='Training')
# perform model training
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = reader_train.next_minibatch(min(minibatch_size, epoch_size - sample_count), input_map=input_map) # fetch minibatch.
trainer.train_minibatch(data) # update model with it
sample_count += data[label_var].num_samples # count samples processed so far
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
progress_printer.epoch_summary(with_metric=True)
#
# Evaluation action
#
epoch_size = 10000
minibatch_size = 16
# process minibatches and evaluate the model
metric_numer = 0
metric_denom = 0
sample_count = 0
minibatch_index = 0
#progress_printer = ProgressPrinter(freq=100, first=10, tag='Eval')
while sample_count < epoch_size:
current_minibatch = min(minibatch_size, epoch_size - sample_count)
# Fetch next test min batch.
data = reader_test.next_minibatch(current_minibatch, input_map=input_map)
# minibatch data to be trained with
metric_numer += trainer.test_minibatch(data) * current_minibatch
metric_denom += current_minibatch
# Keep track of the number of samples processed so far.
sample_count += data[label_var].num_samples
minibatch_index += 1
print("")
print("Final Results: Minibatch[1-{}]: errs = {:0.1f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom))
print("")
# return evaluation error.
return metric_numer/metric_denom
def train_and_evaluate(reader_train, reader_test, max_epochs,
distributed_trainer):
# Input variables denoting the features and label data
input_var = input_variable((num_channels, image_height, image_width))
label_var = input_variable((num_classes))
# Normalize the input
feature_scale = 1.0 / 256.0
input_var_norm = element_times(feature_scale, input_var)
# apply model to input
z = create_resnet_model(input_var_norm, 10)
#
# Training action
#
# loss and metric
ce = cross_entropy_with_softmax(z, label_var)
pe = classification_error(z, label_var)
# training config
epoch_size = 50000
minibatch_size = 128
# Set learning parameters
lr_per_minibatch = learning_rate_schedule([1] * 80 + [0.1] * 40 + [0.01],
UnitType.minibatch, epoch_size)
momentum_time_constant = momentum_as_time_constant_schedule(
-minibatch_size / np.log(0.9))
l2_reg_weight = 0.0001
# trainer object
learner = momentum_sgd(z.parameters,
lr=lr_per_minibatch,
momentum=momentum_time_constant,
l2_regularization_weight=l2_reg_weight)
trainer = Trainer(z, ce, pe, learner, distributed_trainer)
# define mapping from reader streams to network inputs
input_map = {
input_var: reader_train.streams.features,
label_var: reader_train.streams.labels
}
log_number_of_parameters(z)
print()
progress_printer = ProgressPrinter(tag='Training')
# perform model training
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = reader_train.next_minibatch(
minibatch_size, input_map=input_map) # fetch minibatch.
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
progress_printer.update_with_trainer(
trainer, with_metric=True) # log progress
progress_printer.epoch_summary(with_metric=True)
if distributed_trainer.communicator().current_worker(
).global_rank == 0:
persist.save_model(
z,
os.path.join(model_path,
"CifarResNet_Distributed_{}.dnn".format(epoch)))
#
# Evaluation action
#
epoch_size = 10000
minibatch_size = 16
# process minibatches and evaluate the model
metric_numer = 0
metric_denom = 0
sample_count = 0
minibatch_index = 0
#progress_printer = ProgressPrinter(freq=100, first=10, tag='Eval')
while sample_count < epoch_size:
current_minibatch = min(minibatch_size, epoch_size - sample_count)
# Fetch next test min batch.
data = reader_test.next_minibatch(current_minibatch,
input_map=input_map)
# minibatch data to be trained with
metric_numer += trainer.test_minibatch(data) * current_minibatch
metric_denom += current_minibatch
# Keep track of the number of samples processed so far.
sample_count += data[label_var].num_samples
minibatch_index += 1
print("")
print("Final Results: Minibatch[1-{}]: errs = {:0.1f}% * {}".format(
minibatch_index + 1, (metric_numer * 100.0) / metric_denom,
metric_denom))
print("")
# return evaluation error.
return metric_numer / metric_denom
labels_arr = [np.array([0,1],dtype=np.float32) if x==-1 else np.array([1,0],dtype=np.float32) for x in labels]
input_var = C.input_variable((input_size,vocab_size))
label_var = C.input_variable((2))
model = Sequential([Dense(500,activation=C.ops.relu),Dense(2,activation=None)])
z = model(input_var)
ce = cross_entropy_with_softmax(z, label_var)
errs = classification_error(z, label_var)
lr_per_sample = learning_rate_schedule(0.02, UnitType.minibatch)
learner = C.learners.sgd(z.parameters, lr_per_sample)
progress_printer = ProgressPrinter(freq=100, tag='Training')
trainer = Trainer(z, (ce, errs), learner, progress_printer)
log_number_of_parameters(z)
minibatch_size=10
for ep in range(20):
print("Epoch={}".format(ep))
for mb in range(0,len(words),minibatch_size):
trainer.train_minibatch({input_var: words_arr[mb:mb+minibatch_size], label_var: labels_arr[mb:mb+minibatch_size]})
words_test, labels_test = read("sentiment-test.txt")
def check(net,dofill=True):
total = 0
def sequence_to_sequence_translator(debug_output=False, run_test=False):
input_vocab_dim = 69
label_vocab_dim = 69
# network complexity; initially low for faster testing
hidden_dim = 256
num_layers = 1
# Source and target inputs to the model
batch_axis = Axis.default_batch_axis()
input_seq_axis = Axis('inputAxis')
label_seq_axis = Axis('labelAxis')
input_dynamic_axes = [batch_axis, input_seq_axis]
raw_input = input_variable(
shape=(input_vocab_dim), dynamic_axes=input_dynamic_axes, name='raw_input')
label_dynamic_axes = [batch_axis, label_seq_axis]
raw_labels = input_variable(
shape=(label_vocab_dim), dynamic_axes=label_dynamic_axes, name='raw_labels')
# Instantiate the sequence to sequence translation model
input_sequence = raw_input
# Drop the sentence start token from the label, for decoder training
label_sequence = sequence.slice(raw_labels, 1, 0) # <s> A B C </s> --> A B C </s>
label_sentence_start = sequence.first(raw_labels) # <s>
is_first_label = sequence.is_first(label_sequence) # <s> 0 0 0 ...
label_sentence_start_scattered = sequence.scatter(
label_sentence_start, is_first_label)
# Encoder
encoder_outputH = stabilize(input_sequence)
for i in range(0, num_layers):
(encoder_outputH, encoder_outputC) = LSTMP_component_with_self_stabilization(
encoder_outputH.output, hidden_dim, hidden_dim, future_value, future_value)
thought_vectorH = sequence.first(encoder_outputH)
thought_vectorC = sequence.first(encoder_outputC)
thought_vector_broadcastH = sequence.broadcast_as(
thought_vectorH, label_sequence)
thought_vector_broadcastC = sequence.broadcast_as(
thought_vectorC, label_sequence)
# Decoder
decoder_history_hook = alias(label_sequence, name='decoder_history_hook') # copy label_sequence
decoder_input = element_select(is_first_label, label_sentence_start_scattered, past_value(
decoder_history_hook))
decoder_outputH = stabilize(decoder_input)
for i in range(0, num_layers):
if (i > 0):
recurrence_hookH = past_value
recurrence_hookC = past_value
else:
isFirst = sequence.is_first(label_sequence)
recurrence_hookH = lambda operand: element_select(
isFirst, thought_vector_broadcastH, past_value(operand))
recurrence_hookC = lambda operand: element_select(
isFirst, thought_vector_broadcastC, past_value(operand))
(decoder_outputH, encoder_outputC) = LSTMP_component_with_self_stabilization(
decoder_outputH.output, hidden_dim, hidden_dim, recurrence_hookH, recurrence_hookC)
decoder_output = decoder_outputH
# Softmax output layer
z = linear_layer(stabilize(decoder_output), label_vocab_dim)
# Criterion nodes
ce = cross_entropy_with_softmax(z, label_sequence)
errs = classification_error(z, label_sequence)
# network output for decoder history
net_output = hardmax(z)
# make a clone of the graph where the ground truth is replaced by the network output
ng = z.clone(CloneMethod.share, {decoder_history_hook.output : net_output.output})
# Instantiate the trainer object to drive the model training
lr_per_minibatch = learning_rate_schedule(0.5, UnitType.minibatch)
momentum_time_constant = momentum_as_time_constant_schedule(1100)
clipping_threshold_per_sample = 2.3
gradient_clipping_with_truncation = True
learner = momentum_sgd(z.parameters,
lr_per_minibatch, momentum_time_constant,
gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
gradient_clipping_with_truncation=gradient_clipping_with_truncation)
trainer = Trainer(z, ce, errs, learner)
# setup data
train_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "Data", "cmudict-0.7b.train-dev-20-21.ctf")
valid_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "Data", "tiny.ctf")
# readers
randomize_data = True
if run_test:
randomize_data = False # because we want to get an exact error
train_reader = create_reader(train_path, randomize_data, input_vocab_dim, label_vocab_dim)
train_bind = {
raw_input : train_reader.streams.features,
raw_labels : train_reader.streams.labels
}
# get the vocab for printing output sequences in plaintext
vocab_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "Data", "cmudict-0.7b.mapping")
vocab = [w.strip() for w in open(vocab_path).readlines()]
i2w = { i:ch for i,ch in enumerate(vocab) }
# Get minibatches of sequences to train with and perform model training
i = 0
mbs = 0
minibatch_size = 72
epoch_size = 908241
max_epochs = 10
training_progress_output_freq = 500
# make things more basic for running a quicker test
if run_test:
epoch_size = 5000
max_epochs = 1
training_progress_output_freq = 30
valid_reader = create_reader(valid_path, False, input_vocab_dim, label_vocab_dim)
valid_bind = {
find_arg_by_name('raw_input',ng) : valid_reader.streams.features,
find_arg_by_name('raw_labels',ng) : valid_reader.streams.labels
}
for epoch in range(max_epochs):
loss_numer = 0
metric_numer = 0
denom = 0
while i < (epoch+1) * epoch_size:
# get next minibatch of training data
mb_train = train_reader.next_minibatch(minibatch_size, input_map=train_bind)
trainer.train_minibatch(mb_train)
# collect epoch-wide stats
samples = trainer.previous_minibatch_sample_count
loss_numer += trainer.previous_minibatch_loss_average * samples
metric_numer += trainer.previous_minibatch_evaluation_average * samples
denom += samples
# every N MBs evaluate on a test sequence to visually show how we're doing
if mbs % training_progress_output_freq == 0:
mb_valid = valid_reader.next_minibatch(minibatch_size, input_map=valid_bind)
e = ng.eval(mb_valid)
print_sequences(e, i2w)
print_training_progress(trainer, mbs, training_progress_output_freq)
i += mb_train[raw_labels].num_samples
mbs += 1
print("--- EPOCH %d DONE: loss = %f, errs = %f ---" % (epoch, loss_numer/denom, 100.0*(metric_numer/denom)))
error1 = translator_test_error(z, trainer, input_vocab_dim, label_vocab_dim)
z.save_model("seq2seq.dnn")
z.restore_model("seq2seq.dnn")
label_seq_axis = Axis('labelAxis')
label_sequence = sequence.slice(find_arg_by_name('raw_labels',z), 1, 0)
ce = cross_entropy_with_softmax(z, label_sequence)
errs = classification_error(z, label_sequence)
trainer = Trainer(z, ce, errs, [momentum_sgd(
z.parameters, lr_per_minibatch, momentum_time_constant, clipping_threshold_per_sample, gradient_clipping_with_truncation)])
error2 = translator_test_error(z, trainer, input_vocab_dim, label_vocab_dim)
assert error1 == error2
return error1
def simple_mnist(tensorboard_logdir=None):
input_dim = 784
num_output_classes = 10
num_hidden_layers = 1
hidden_layers_dim = 200
# Input variables denoting the features and label data
input = input_variable(input_dim, np.float32)
label = input_variable(num_output_classes, np.float32)
# Instantiate the feedforward classification model
scaled_input = element_times(constant(0.00390625), input)
z = fully_connected_classifier_net(scaled_input, num_output_classes,
hidden_layers_dim, num_hidden_layers,
relu)
ce = cross_entropy_with_softmax(z, label)
pe = classification_error(z, label)
data_dir = os.path.join(abs_path, "..", "..", "..", "DataSets", "MNIST")
path = os.path.normpath(os.path.join(data_dir,
"Train-28x28_cntk_text.txt"))
check_path(path)
reader_train = create_reader(path, True, input_dim, num_output_classes)
input_map = {
input: reader_train.streams.features,
label: reader_train.streams.labels
}
# Training config
minibatch_size = 64
num_samples_per_sweep = 60000
num_sweeps_to_train_with = 10
# Instantiate progress writers.
#training_progress_output_freq = 100
progress_writers = [
ProgressPrinter(
#freq=training_progress_output_freq,
tag='Training',
num_epochs=num_sweeps_to_train_with)
]
if tensorboard_logdir is not None:
progress_writers.append(
TensorBoardProgressWriter(freq=10,
log_dir=tensorboard_logdir,
model=z))
# Instantiate the trainer object to drive the model training
lr_per_minibatch = learning_rate_schedule(0.2, UnitType.minibatch)
trainer = Trainer(z, (ce, pe), sgd(z.parameters, lr=lr_per_minibatch),
progress_writers)
training_session(trainer=trainer,
mb_source=reader_train,
mb_size=minibatch_size,
var_to_stream=input_map,
max_samples=num_samples_per_sweep *
num_sweeps_to_train_with,
progress_frequency=num_samples_per_sweep).train()
# Load test data
path = os.path.normpath(os.path.join(data_dir, "Test-28x28_cntk_text.txt"))
check_path(path)
reader_test = create_reader(path, False, input_dim, num_output_classes)
input_map = {
input: reader_test.streams.features,
label: reader_test.streams.labels
}
# Test data for trained model
test_minibatch_size = 1024
num_samples = 10000
num_minibatches_to_test = num_samples / test_minibatch_size
test_result = 0.0
for i in range(0, int(num_minibatches_to_test)):
mb = reader_test.next_minibatch(test_minibatch_size,
input_map=input_map)
eval_error = trainer.test_minibatch(mb)
test_result = test_result + eval_error
# Average of evaluation errors of all test minibatches
return test_result / num_minibatches_to_test
def train_fast_rcnn(debug_output=False):
if debug_output:
print("Storing graphs and intermediate models to %s." %
os.path.join(abs_path, "Output"))
# Create the minibatch source
minibatch_source = create_mb_source(image_height, image_width,
num_channels, num_classes, num_rois,
base_path, "train")
# Input variables denoting features, rois and label data
image_input = input((num_channels, image_height, image_width))
roi_input = input((num_rois, 4))
label_input = input((num_rois, num_classes))
# define mapping from reader streams to network inputs
input_map = {
image_input: minibatch_source[features_stream_name],
roi_input: minibatch_source[roi_stream_name],
label_input: minibatch_source[label_stream_name]
}
# Instantiate the Fast R-CNN prediction model and loss function
frcn_output = frcn_predictor(image_input, roi_input, num_classes)
ce = cross_entropy_with_softmax(frcn_output, label_input, axis=1)
pe = classification_error(frcn_output, label_input, axis=1)
if debug_output:
plot(frcn_output, os.path.join(abs_path, "Output", "graph_frcn.png"))
# Set learning parameters
l2_reg_weight = 0.0005
lr_per_sample = [0.00001] * 10 + [0.000001] * 5 + [0.0000001]
lr_schedule = learning_rate_schedule(lr_per_sample, unit=UnitType.sample)
mm_schedule = momentum_as_time_constant_schedule(momentum_time_constant)
# Instantiate the trainer object
learner = momentum_sgd(frcn_output.parameters,
lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight)
progress_printer = ProgressPrinter(tag='Training', num_epochs=max_epochs)
trainer = Trainer(frcn_output, (ce, pe), learner, progress_printer)
# Get minibatches of images and perform model training
print("Training Fast R-CNN model for %s epochs." % max_epochs)
log_number_of_parameters(frcn_output)
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = minibatch_source.next_minibatch(min(
mb_size, epoch_size - sample_count),
input_map=input_map)
trainer.train_minibatch(data) # update model with it
sample_count += trainer.previous_minibatch_sample_count # count samples processed so far
trainer.summarize_training_progress()
if debug_output:
frcn_output.save(
os.path.join(abs_path, "Output",
"frcn_py_%s.model" % (epoch + 1)))
return frcn_output
def test_sweep_based_schedule(tmpdir, device_id):
from cntk.io import MinibatchSource, CTFDeserializer, StreamDef, StreamDefs
from cntk import cross_entropy_with_softmax, classification_error, plus, reduce_sum, sequence
from cntk import Trainer
input_dim = 69
ctf_data = '''\
0 |S0 3:1 |S1 3:1 |# <s>
0 |S0 4:1 |# A |S1 32:1 |# ~AH
0 |S0 5:1 |# B |S1 36:1 |# ~B
0 |S0 4:1 |# A |S1 31:1 |# ~AE
0 |S0 7:1 |# D |S1 38:1 |# ~D
0 |S0 12:1 |# I |S1 47:1 |# ~IY
0 |S0 1:1 |# </s> |S1 1:1 |# </s>
2 |S0 60:1 |# <s> |S1 3:1 |# <s>
2 |S0 61:1 |# A |S1 32:1 |# ~AH
'''
ctf_file = str(tmpdir / '2seqtest.txt')
with open(ctf_file, 'w') as f:
f.write(ctf_data)
mbs = MinibatchSource(CTFDeserializer(
ctf_file,
StreamDefs(features=StreamDef(field='S0',
shape=input_dim,
is_sparse=True),
labels=StreamDef(field='S1',
shape=input_dim,
is_sparse=True))),
randomize=False)
in1 = sequence.input(shape=(input_dim, ))
labels = sequence.input(shape=(input_dim, ))
p = parameter(shape=(input_dim, ), init=10)
z = plus(in1, reduce_sum(p), name='z')
ce = cross_entropy_with_softmax(z, labels)
errs = classification_error(z, labels)
lr_per_sample = learning_rate_schedule([0.3, 0.2, 0.1, 0.0],
UnitType.sample)
learner = sgd(z.parameters, lr_per_sample)
trainer = Trainer(z, (ce, errs), [learner])
input_map = {in1: mbs.streams.features, labels: mbs.streams.labels}
# fetch minibatch (first sequence)
data = mbs.next_minibatch(1, input_map=input_map)
trainer.train_minibatch(data)
assert learner.learning_rate() == 0.3
# fetch minibatch (second sequence, sweep ends at this point)
data = mbs.next_minibatch(1, input_map=input_map)
trainer.train_minibatch(data)
assert learner.learning_rate() == 0.2
# fetch minibatch (both sequences -- entire sweep in one go)
data = mbs.next_minibatch(9, input_map=input_map)
trainer.train_minibatch(data)
assert learner.learning_rate() == 0.1
# fetch minibatch (multiple sweeps)
data = mbs.next_minibatch(30, input_map=input_map)
trainer.train_minibatch(data, outputs=[z.output])
assert learner.learning_rate() == 0.0
def train_sequence_classifier(debug_output=False):
input_dim = 2000
cell_dim = 25
hidden_dim = 25
embedding_dim = 50
num_output_classes = 5
# Input variables denoting the features and label data
features = input_variable(shape=input_dim, is_sparse=True)
label = input_variable(num_output_classes,
dynamic_axes=[Axis.default_batch_axis()])
# Instantiate the sequence classification model
classifier_output = LSTM_sequence_classifer_net(features,
num_output_classes,
embedding_dim, hidden_dim,
cell_dim)
ce = cross_entropy_with_softmax(classifier_output, label)
pe = classification_error(classifier_output, label)
rel_path = r"../../../../Tests/EndToEndTests/Text/SequenceClassification/Data/Train.ctf"
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), rel_path)
feature_stream_name = 'features'
labels_stream_name = 'labels'
mb_source = text_format_minibatch_source(path, [
StreamConfiguration(feature_stream_name, input_dim, True, 'x'),
StreamConfiguration(labels_stream_name, num_output_classes, False, 'y')
], 0)
features_si = mb_source[features]
labels_si = mb_source[label]
# Instantiate the trainer object to drive the model training
trainer = Trainer(classifier_output, ce, pe,
[sgd(classifier_output.parameters, lr=0.0005)])
# Get minibatches of sequences to train with and perform model training
minibatch_size = 200
training_progress_output_freq = 10
i = 0
if debug_output:
training_progress_output_freq = training_progress_output_freq / 3
while True:
mb = mb_source.next_minibatch(minibatch_size)
if len(mb) == 0:
break
# Specify the mapping of input variables in the model to actual
# minibatch data to be trained with
arguments = {features: mb[features_si], label: mb[labels_si]}
trainer.train_minibatch(arguments)
print_training_progress(trainer, i, training_progress_output_freq)
i += 1
import copy
evaluation_average = copy.copy(
trainer.previous_minibatch_evaluation_average)
loss_average = copy.copy(trainer.previous_minibatch_loss_average)
return evaluation_average, loss_average
def conv3d_ucf11(train_reader, test_reader, max_epochs=30):
# Replace 0 with 1 to get detailed log.
set_computation_network_trace_level(0)
# These values must match for both train and test reader.
image_height = train_reader.height
image_width = train_reader.width
num_channels = train_reader.channel_count
sequence_length = train_reader.sequence_length
num_output_classes = train_reader.label_count
# Input variables denoting the features and label data
input_var = input_variable((num_channels, sequence_length, image_height, image_width), np.float32)
label_var = input_variable(num_output_classes, np.float32)
# Instantiate simple 3D Convolution network inspired by VGG network
# and http://vlg.cs.dartmouth.edu/c3d/c3d_video.pdf
with default_options (activation=relu):
z = Sequential([
Convolution3D((3,3,3), 64, pad=True),
MaxPooling((1,2,2), (1,2,2)),
For(range(3), lambda i: [
Convolution3D((3,3,3), [96, 128, 128][i], pad=True),
Convolution3D((3,3,3), [96, 128, 128][i], pad=True),
MaxPooling((2,2,2), (2,2,2))
]),
For(range(2), lambda : [
Dense(1024),
Dropout(0.5)
]),
Dense(num_output_classes, activation=None)
])(input_var)
# loss and classification error.
ce = cross_entropy_with_softmax(z, label_var)
pe = classification_error(z, label_var)
# training config
epoch_size = 1322 # for now we manually specify epoch size
minibatch_size = 4
# Set learning parameters
lr_per_sample = [0.01]*10+[0.001]*10+[0.0001]
lr_schedule = learning_rate_schedule(lr_per_sample, epoch_size=epoch_size, unit=UnitType.sample)
momentum_time_constant = 4096
mm_schedule = momentum_as_time_constant_schedule(momentum_time_constant, epoch_size=epoch_size)
# Instantiate the trainer object to drive the model training
learner = momentum_sgd(z.parameters, lr_schedule, mm_schedule, True)
trainer = Trainer(z, (ce, pe), learner)
log_number_of_parameters(z) ; print()
progress_printer = ProgressPrinter(tag='Training')
# Get minibatches of images to train with and perform model training
for epoch in range(max_epochs): # loop over epochs
train_reader.reset()
while train_reader.has_more():
videos, labels, current_minibatch = train_reader.next_minibatch(minibatch_size)
trainer.train_minibatch({input_var : videos, label_var : labels})
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
progress_printer.epoch_summary(with_metric=True)
# Test data for trained model
epoch_size = 332
minibatch_size = 2
# process minibatches and evaluate the model
metric_numer = 0
metric_denom = 0
minibatch_index = 0
test_reader.reset()
while test_reader.has_more():
videos, labels, current_minibatch = test_reader.next_minibatch(minibatch_size)
# minibatch data to be trained with
metric_numer += trainer.test_minibatch({input_var : videos, label_var : labels}) * current_minibatch
metric_denom += current_minibatch
# Keep track of the number of samples processed so far.
minibatch_index += 1
print("")
print("Final Results: Minibatch[1-{}]: errs = {:0.2f}% * {}".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom))
print("")
return metric_numer/metric_denom
mydict['w'], mydict['b'] = weight_param, bias_param
return times(input_var, weight_param) + bias_param
output_dim = num_output_classes
z = linear_layer(input, output_dim)
label = input_variable((num_output_classes), np.float32)
loss = cross_entropy_with_softmax(z, label)
eval_error = classification_error(z, label)
learning_rate = 0.5
lr_schedule = learning_rate_schedule(learning_rate, UnitType.minibatch)
learner = sgd(z.parameters, lr_schedule)
trainer = Trainer(z, loss, eval_error, [learner])
from cntk.utils import get_train_eval_criterion, get_train_loss
# Define a utility function to compute the moving average sum.
# A more efficient implementation is possible with np.cumsum() function
def moving_average(a, w=10):
if len(a) < w:
return a[:]
return [val if idx < w else sum(a[(idx-w):idx])/w for idx, val in enumerate(a)]
# Defines a utility that prints the training progress
def print_training_progress(trainer, mb, frequency, verbose=1):
training_loss, eval_error = "NA", "NA"
def train(train_reader, valid_reader, vocab, i2w, s2smodel, max_epochs, epoch_size):
# Note: We would like to set the signature of 's2smodel' (s2smodel.update_signature()), but that will cause
# an error since the training criterion uses a reduced sequence axis for the labels.
# This is because it removes the initial <s> symbol. Hence, we must leave the model
# with unspecified input shapes and axes.
# create the training wrapper for the s2smodel, as well as the criterion function
model_train = create_model_train(s2smodel)
criterion = create_criterion_function(model_train)
# also wire in a greedy decoder so that we can properly log progress on a validation example
# This is not used for the actual training process.
model_greedy = create_model_greedy(s2smodel)
# This does not need to be done in training generally though
# Instantiate the trainer object to drive the model training
minibatch_size = 72
lr = 0.001 if use_attention else 0.005 # TODO: can we use the same value for both?
learner = fsadagrad(
model_train.parameters,
lr=learning_parameter_schedule_per_sample(
[lr] * 2 + [lr / 2] * 3 + [lr / 4], epoch_size=epoch_size
),
momentum=momentum_schedule_per_sample(0.9990913221888589),
gradient_clipping_threshold_per_sample=2.3,
gradient_clipping_with_truncation=True,
)
trainer = Trainer(None, criterion, learner)
# Get minibatches of sequences to train with and perform model training
total_samples = 0
mbs = 0
eval_freq = 100
# print out some useful training information
log_number_of_parameters(model_train)
print()
progress_printer = ProgressPrinter(freq=30, tag="Training")
# progress_printer = ProgressPrinter(freq=30, tag='Training', log_to_file=model_path_stem + ".log") # use this to log to file
sparse_to_dense = create_sparse_to_dense(input_vocab_dim)
for epoch in range(max_epochs):
print("Saving model to '%s'" % model_path(epoch))
s2smodel.save(model_path(epoch))
while total_samples < (epoch + 1) * epoch_size:
# get next minibatch of training data
mb_train = train_reader.next_minibatch(minibatch_size)
# trainer.train_minibatch(mb_train[train_reader.streams.features], mb_train[train_reader.streams.labels])
trainer.train_minibatch(
{
criterion.arguments[0]: mb_train[train_reader.streams.features],
criterion.arguments[1]: mb_train[train_reader.streams.labels],
}
)
progress_printer.update_with_trainer(
trainer, with_metric=True
) # log progress
# every N MBs evaluate on a test sequence to visually show how we're doing
if mbs % eval_freq == 0:
mb_valid = valid_reader.next_minibatch(1)
# run an eval on the decoder output model (i.e. don't use the groundtruth)
e = model_greedy(mb_valid[valid_reader.streams.features])
print(
format_sequences(
sparse_to_dense(mb_valid[valid_reader.streams.features]), i2w
)
)
print("->")
print(format_sequences(e, i2w))
# debugging attention
if use_attention:
debug_attention(
model_greedy, mb_valid[valid_reader.streams.features]
)
total_samples += mb_train[train_reader.streams.labels].num_samples
mbs += 1
# log a summary of the stats for the epoch
progress_printer.epoch_summary(with_metric=True)
# done: save the final model
print("Saving final model to '%s'" % model_path(max_epochs))
s2smodel.save(model_path(max_epochs))
print("%d epochs complete." % max_epochs)
def train_lm(training_file, max_num_minibatches):
# load the data and vocab
data, char_to_ix, ix_to_char, data_size, vocab_dim = load_data_and_vocab(training_file)
# Model the source and target inputs to the model
input_sequence, label_sequence = create_inputs(vocab_dim)
# create the model
model = create_model(vocab_dim)
# and apply it to the input sequence
z = model(input_sequence)
# setup the criterions (loss and metric)
ce = cross_entropy_with_softmax(z, label_sequence)
errs = classification_error(z, label_sequence)
# Instantiate the trainer object to drive the model training
lr_per_sample = learning_rate_schedule(0.001, UnitType.sample)
momentum_time_constant = momentum_as_time_constant_schedule(1100)
clipping_threshold_per_sample = 5.0
gradient_clipping_with_truncation = True
learner = momentum_sgd(z.parameters, lr_per_sample, momentum_time_constant,
gradient_clipping_threshold_per_sample=clipping_threshold_per_sample,
gradient_clipping_with_truncation=gradient_clipping_with_truncation)
trainer = Trainer(z, (ce, errs), learner)
sample_freq = 1000
epochs = 50
minibatches_per_epoch = int((data_size / minibatch_size))
minibatches = min(epochs * minibatches_per_epoch, max_num_minibatches)
# print out some useful training information
log_number_of_parameters(z) ; print()
progress_printer = ProgressPrinter(freq=100, tag='Training')
e = 0
p = 0
for i in range(0, minibatches):
if p + minibatch_size+1 >= data_size:
p = 0
e += 1
model_filename = "models/shakespeare_epoch%d.dnn" % e
z.save(model_filename)
print("Saved model to '%s'" % model_filename)
# get the data
features, labels = get_data(p, minibatch_size, data, char_to_ix, vocab_dim)
# Specify the mapping of input variables in the model to actual minibatch data to be trained with
# If it's the start of the data, we specify that we are looking at a new sequence (True)
mask = [False]
if p == 0:
mask = [True]
arguments = ({input_sequence : features, label_sequence : labels}, mask)
trainer.train_minibatch(arguments)
progress_printer.update_with_trainer(trainer, with_metric=True) # log progress
if i % sample_freq == 0:
print(sample(z, ix_to_char, vocab_dim, char_to_ix))
p += minibatch_size
# Do a final save of the model
model_filename = "models/shakespeare_epoch%d.dnn" % e
z.save(model_filename)
def train_and_evaluate(reader_train, reader_test, max_epochs):
# Input variables denoting the features and label data
input_var = input_variable((num_channels, image_height, image_width))
label_var = input_variable((num_classes))
# apply model to input
z = create_resnet_model(input_var, 10)
#
# Training action
#
# loss and metric
ce = cross_entropy_with_softmax(z, label_var)
pe = classification_error(z, label_var)
# training config
epoch_size = 50000
minibatch_size = 128
# Set learning parameters
lr_per_sample = [1 / minibatch_size] * 80 + [0.1 / minibatch_size] * 40 + [
0.01 / minibatch_size
]
lr_schedule = learning_rate_schedule(lr_per_sample, units=epoch_size)
momentum_per_sample = 0.9**(1.0 / minibatch_size)
l2_reg_weight = 0.0001
# trainer object
lr_schedule = learning_rate_schedule(lr_per_sample, units=epoch_size)
learner = momentum_sgd(z.parameters,
lr_schedule,
momentum_per_sample,
l2_regularization_weight=l2_reg_weight)
trainer = Trainer(z, ce, pe, learner)
# define mapping from reader streams to network inputs
input_map = {
input_var: reader_train.streams.features,
label_var: reader_train.streams.labels
}
log_number_of_parameters(z)
print()
progress_printer = ProgressPrinter(tag='Training')
# perform model training
for epoch in range(max_epochs): # loop over epochs
sample_count = 0
while sample_count < epoch_size: # loop over minibatches in the epoch
data = reader_train.next_minibatch(
min(minibatch_size, epoch_size - sample_count),
input_map=input_map) # fetch minibatch.
trainer.train_minibatch(data) # update model with it
sample_count += data[
label_var].num_samples # count samples processed so far
progress_printer.update_with_trainer(
trainer, with_metric=True) # log progress
progress_printer.epoch_summary(with_metric=True)
#
# Evaluation action
#
epoch_size = 10000
minibatch_size = 16
# process minibatches and evaluate the model
metric_numer = 0
metric_denom = 0
sample_count = 0
minibatch_index = 0
#progress_printer = ProgressPrinter(freq=100, first=10, tag='Eval')
while sample_count < epoch_size:
current_minibatch = min(minibatch_size, epoch_size - sample_count)
# Fetch next test min batch.
data = reader_test.next_minibatch(current_minibatch,
input_map=input_map)
# minibatch data to be trained with
metric_numer += trainer.test_minibatch(data) * current_minibatch
metric_denom += current_minibatch
# Keep track of the number of samples processed so far.
sample_count += data[label_var].num_samples
minibatch_index += 1
print("")
print("Final Results: Minibatch[1-{}]: errs = {:0.1f}% * {}".format(
minibatch_index + 1, (metric_numer * 100.0) / metric_denom,
metric_denom))
print("")
return metric_numer / metric_denom