def create_trainer(network, minibatch_size, epoch_size, num_quantization_bits, block_size, warm_up, progress_printer):
if network['name'] == 'resnet20':
lr_per_mb = [1.0]*80 + [0.1]*40 + [0.01]
elif network['name'] == 'resnet110':
lr_per_mb = [0.1]*1 + [1.0]*80 + [0.1]*40 + [0.01]
else:
return RuntimeError("Unknown model name!")
l2_reg_weight = 0.0001
# Set learning parameters
minibatch_size = 128
lr_per_sample = [lr/minibatch_size for lr in lr_per_mb]
lr_schedule = learning_parameter_schedule(lr_per_mb, minibatch_size = minibatch_size, epoch_size=epoch_size)
mm_schedule = momentum_schedule(0.9, minibatch_size = minibatch_size)
# learner object
if block_size != None and num_quantization_bits != 32:
raise RuntimeError("Block momentum cannot be used with quantization, please remove quantized_bits option.")
local_learner = momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule,
l2_regularization_weight=l2_reg_weight)
if block_size != None:
learner = block_momentum_distributed_learner(local_learner, block_size=block_size)
else:
learner = data_parallel_distributed_learner(local_learner, num_quantization_bits=num_quantization_bits, distributed_after=warm_up)
return Trainer(network['output'], (network['ce'], network['pe']), learner, progress_printer)
def train_model(reader, model, criterion, epoch_size=50000, max_epochs=80):
minibatch_size = 64
# learning parameters
learner = momentum_sgd(model.parameters,
lr = learning_parameter_schedule([0.0015625]*20+[0.00046875]*20+[0.00015625]*20+[0.000046875]*10+[0.000015625], minibatch_size=1, 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 create_trainer(network, epoch_size, num_epochs, minibatch_size, num_quantization_bits, progress_printer):
# CNTK weights new gradient by (1-momentum) for unit gain,
# thus we divide Caffe's learning rate by (1-momentum)
initial_learning_rate = 2.0 # equal to 0.2 in caffe
initial_learning_rate *= minibatch_size / 128
learn_rate_adjust_interval = 2
learn_rate_decrease_factor = 0.94
# Set learning parameters
lr_per_mb = []
learning_rate = initial_learning_rate
for i in range(0, num_epochs, learn_rate_adjust_interval):
lr_per_mb.extend([learning_rate] * learn_rate_adjust_interval)
learning_rate *= learn_rate_decrease_factor
lr_schedule = learning_parameter_schedule(lr_per_mb, epoch_size=epoch_size)
mm_schedule = momentum_schedule(0.9)
l2_reg_weight = 0.0001 # CNTK L2 regularization is per sample, thus same as Caffe
# Create learner
local_learner = momentum_sgd(network['output'].parameters, lr_schedule, mm_schedule,
l2_regularization_weight=l2_reg_weight)
parameter_learner = data_parallel_distributed_learner(
local_learner,
num_quantization_bits=num_quantization_bits,
distributed_after=0)
# Create trainer
return Trainer(network['output'], (network['ce'], network['pe']), parameter_learner, progress_printer)
def train_model(reader, model, criterion, epoch_size=50000, max_epochs=80):
minibatch_size = 64
# learning parameters
learner = momentum_sgd(model.parameters,
lr=learning_parameter_schedule(
[0.0015625] * 20 + [0.00046875] * 20 + [0.00015625] * 20 + [0.000046875] * 10 + [
0.000015625], minibatch_size=1, 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 create_trainer(network, epoch_size, num_epochs, minibatch_size,
num_quantization_bits, progress_printer):
# CNTK weights new gradient by (1-momentum) for unit gain,
# thus we divide Caffe's learning rate by (1-momentum)
initial_learning_rate = 2.0 # equal to 0.2 in caffe
initial_learning_rate *= minibatch_size / 128
learn_rate_adjust_interval = 2
learn_rate_decrease_factor = 0.94
# Set learning parameters
lr_per_mb = []
learning_rate = initial_learning_rate
for i in range(0, num_epochs, learn_rate_adjust_interval):
lr_per_mb.extend([learning_rate] * learn_rate_adjust_interval)
learning_rate *= learn_rate_decrease_factor
lr_schedule = learning_parameter_schedule(lr_per_mb, epoch_size=epoch_size)
mm_schedule = momentum_schedule(0.9)
l2_reg_weight = 0.0001 # CNTK L2 regularization is per sample, thus same as Caffe
# Create learner
local_learner = momentum_sgd(network['output'].parameters,
lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight)
parameter_learner = data_parallel_distributed_learner(
local_learner,
num_quantization_bits=num_quantization_bits,
distributed_after=0)
# Create trainer
return Trainer(network['output'], (network['ce'], network['pe']),
parameter_learner, progress_printer)
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_parameter_schedule(1),
momentum=momentum_schedule(0))
return Trainer(None, (loss, metric), dummy_learner)
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, 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 test_learner_update():
i = C.input_variable(shape=(1,), needs_gradient=True, name='a')
w_init = 1
w = parameter(shape=(1,), init=w_init)
res = i * w
learner = sgd(res.parameters, lr=C.learning_parameter_schedule([0.1]*50 + [0.2]*50, minibatch_size = 1, epoch_size=1))
assert learner.learning_rate() == 0.1
x = learner.update({w: np.asarray([[2.]], dtype=np.float32)}, 100)
assert learner.learning_rate() == 0.2
assert w.value < w_init
learner.reset_learning_rate(learning_parameter_schedule([0.3]*50 + [0.4]*50, minibatch_size = 1, epoch_size=1));
assert learner.learning_rate() == 0.3
x = learner.update({w: np.asarray([[2.]], dtype=np.float32)}, 100)
assert learner.learning_rate() == 0.4
def test_learner_update():
i = C.input_variable(shape=(1,), needs_gradient=True, name='a')
w_init = 1
w = parameter(shape=(1,), init=w_init)
res = i * w
learner = sgd(res.parameters, lr=C.learning_parameter_schedule([0.1]*50 + [0.2]*50, minibatch_size = 1, epoch_size=1))
assert learner.learning_rate() == 0.1
x = learner.update({w: np.asarray([[2.]], dtype=np.float32)}, 100)
assert learner.learning_rate() == 0.2
assert w.value < w_init
learner.reset_learning_rate(learning_parameter_schedule([0.3]*50 + [0.4]*50, minibatch_size = 1, epoch_size=1));
assert learner.learning_rate() == 0.3
x = learner.update({w: np.asarray([[2.]], dtype=np.float32)}, 100)
assert learner.learning_rate() == 0.4
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
feature = input_variable((input_dim), np.float32)
label = input_variable((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_parameter_schedule(0.5)
# 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
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 create_trainer(network, minibatch_size, epoch_size, num_quantization_bits,
block_size, warm_up, progress_printer):
if network['name'] == 'resnet20':
lr_per_mb = [1.0] * 80 + [0.1] * 40 + [0.01]
elif network['name'] == 'resnet110':
lr_per_mb = [0.1] * 1 + [1.0] * 80 + [0.1] * 40 + [0.01]
else:
return RuntimeError("Unknown model name!")
l2_reg_weight = 0.0001
# Set learning parameters
minibatch_size = 128
lr_per_sample = [lr / minibatch_size for lr in lr_per_mb]
lr_schedule = learning_parameter_schedule(lr_per_mb,
minibatch_size=minibatch_size,
epoch_size=epoch_size)
mm_schedule = momentum_schedule(0.9, minibatch_size=minibatch_size)
# learner object
if block_size != None and num_quantization_bits != 32:
raise RuntimeError(
"Block momentum cannot be used with quantization, please remove quantized_bits option."
)
local_learner = momentum_sgd(network['output'].parameters,
lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight)
if block_size != None:
learner = block_momentum_distributed_learner(local_learner,
block_size=block_size)
else:
learner = data_parallel_distributed_learner(
local_learner,
num_quantization_bits=num_quantization_bits,
distributed_after=warm_up)
return Trainer(network['output'], (network['ce'], network['pe']), learner,
progress_printer)
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
feature = input_variable((input_dim), np.float32)
label = input_variable((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_parameter_schedule(0.5)
# 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
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 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_parameter_schedule(lr_per_mb)
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', log_to_file=log_file_name, num_epochs=num_epochs)
#progress_printer = ProgressPrinter(tag='Training', log_to_file=log_file_name, 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))
batch_index = 0
plot_data = {'batchindex': list(), 'loss': list(), 'error': list()}
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))
# For visualization...
#print("type of plot_data:", type(plot_data), type(plot_data['batchindex']), type(plot_data['loss']),type(plot_data['error']))
plot_data['batchindex'].append(batch_index)
plot_data['loss'].append(trainer.previous_minibatch_loss_average)
plot_data['error'].append(trainer.previous_minibatch_evaluation_average)
batch_index += 1
trainer.summarize_training_progress()
# Visualize training result:
window_width = 32
loss_cumsum = np.cumsum(np.insert(plot_data['loss'], 0, 0))
error_cumsum = np.cumsum(np.insert(plot_data['error'], 0, 0))
# Moving average.
plot_data['batchindex'] = np.insert(plot_data['batchindex'], 0, 0)[window_width:]
plot_data['avg_loss'] = (loss_cumsum[window_width:] - loss_cumsum[:-window_width]) / window_width
plot_data['avg_error'] = (error_cumsum[window_width:] - error_cumsum[:-window_width]) / window_width
plt.figure(1)
#plt.subplot(211)
plt.plot(plot_data["batchindex"], plot_data["avg_loss"], 'b--')
plt.xlabel('Minibatch number')
plt.ylabel('Loss')
plt.title('Minibatch run vs. Training loss ')
#plt.show()
plt.savefig(output_figure_loss, bbox_inches='tight' )
plt.figure(2)
#plt.subplot(212)
plt.plot(plot_data["batchindex"], plot_data["avg_error"], 'r--')
plt.xlabel('Minibatch number')
plt.ylabel('Label Prediction Error')
plt.title('Minibatch run vs. Label Prediction Error ')
#plt.show()
plt.savefig(output_figure_error, bbox_inches='tight')
return tl_model
def train(self,
path_to_folder: str,
model_definition,
epochs: int = 10,
output_model_path: str = "driver_model.onnx"):
path_to_folder = path_to_folder.rstrip('/')
map_file_train = path_to_folder + "/train_map.txt"
map_file_test = path_to_folder + "/test_map.txt"
mean_file = path_to_folder + "/meanfile.xml"
classes_set = set()
num_train = 0
num_test = 0
num_channels = 3
try:
with open(map_file_train) as f:
csv_reader = csv.reader(f, delimiter='\t')
for row in csv_reader:
cmd = row[1]
classes_set.add(cmd)
num_train = num_train + 1
except Exception as e:
raise Exception(
"No train_map.txt file found in path " + path_to_folder +
". Did you create a dataset using create_balanced_dataset()?")
num_classes = len(classes_set)
with open(map_file_test) as f:
for num_test, l in enumerate(f):
pass
transforms = [
xforms.scale(width=self.__image_width,
height=self.__image_height,
channels=num_channels,
interpolations='linear'),
xforms.mean(mean_file),
]
# ImageDeserializer loads images in the BGR format, not RGB
reader_train = MinibatchSource(
ImageDeserializer(
map_file_train,
StreamDefs(features=StreamDef(field='image',
transforms=transforms),
labels=StreamDef(field='label',
shape=num_classes))))
reader_test = MinibatchSource(
ImageDeserializer(
map_file_test,
StreamDefs(features=StreamDef(field='image',
transforms=transforms),
labels=StreamDef(field='label',
shape=num_classes))))
input_var = input_variable(
(num_channels, self.__image_height, self.__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)
model = model_definition(input_var)
ce = cross_entropy_with_softmax(model, label_var)
pe = classification_error(model, label_var)
epoch_size = num_train
minibatch_size = 64
lr_per_minibatch = learning_parameter_schedule([0.01] * 10 +
[0.003] * 10 + [0.001],
epoch_size=epoch_size)
momentums = momentum_schedule(0.9, minibatch_size=minibatch_size)
l2_reg_weight = 0.001
learner = momentum_sgd(model.parameters,
lr=lr_per_minibatch,
momentum=momentums,
l2_regularization_weight=l2_reg_weight)
progress_printer = ProgressPrinter(tag='Training', num_epochs=epochs)
trainer = cntk.train.Trainer(model, (ce, pe), [learner],
[progress_printer])
input_map = {
input_var: reader_train.streams.features,
label_var: reader_train.streams.labels
}
batch_index = 0
plot_data = {'batchindex': [], 'loss': [], 'error': []}
for epoch in range(epochs):
sample_count = 0
while sample_count < epoch_size:
data = reader_train.next_minibatch(min(
minibatch_size, epoch_size - sample_count),
input_map=input_map)
trainer.train_minibatch(data)
sample_count += data[label_var].num_samples
plot_data['batchindex'].append(batch_index)
plot_data['loss'].append(
trainer.previous_minibatch_loss_average)
plot_data['error'].append(
trainer.previous_minibatch_evaluation_average)
batch_index += 1
trainer.summarize_training_progress()
epoch_size = num_test
minibatch_size = 16
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)
data = reader_test.next_minibatch(current_minibatch,
input_map=input_map)
metric_numer += trainer.test_minibatch(data) * current_minibatch
metric_denom += current_minibatch
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("")
model.save(output_model_path, format=ModelFormat.ONNX)
def __train_cntk(self, path_to_folder: str, model_definition, epochs: int,
output_model_path: str, classes, minibatch_size: int):
import cntk
from cntk.learners import learning_parameter_schedule
from cntk.ops import input_variable
from cntk.io import MinibatchSource, ImageDeserializer, StreamDefs, StreamDef, MinibatchData, UserDeserializer
import cntk.io.transforms as xforms
from cntk.layers import default_options, Dense, Sequential, Activation, Embedding, Convolution2D, MaxPooling, Stabilizer, Convolution, Dropout, BatchNormalization
from cntk.ops.functions import CloneMethod
from cntk.logging import ProgressPrinter
from cntk.losses import cross_entropy_with_softmax
from cntk import classification_error, softmax, relu, ModelFormat, element_times, momentum_schedule, momentum_sgd
import pandas as pd
path_to_folder = path_to_folder.rstrip('/')
map_file_train = path_to_folder + "/train_map.txt"
map_file_test = path_to_folder + "/test_map.txt"
classes_set = set()
num_train = 0
num_test = 0
num_channels = 3
class TrackDataset(UserDeserializer):
def __init__(self, map_file, streams, chunksize=100):
super(TrackDataset, self).__init__()
self._batch_size = chunksize
self.dataframes = pd.read_csv(map_file,
sep='\t',
dtype=str,
header=None,
names=["features", "labels"])
self._streams = [
cntk.io.StreamInformation(s['name'], i, 'dense',
np.float32, s['shape'])
for i, s in enumerate(streams)
]
self._num_chunks = int(
math.ceil(len(self.dataframes) / chunksize))
def _scale_image(self, image, width=224, height=168):
try:
return image.resize((width, height), Image.LINEAR)
except:
raise Exception('scale_image error')
def stream_infos(self):
return self._streams
def num_chunks(self):
return self._num_chunks
def get_chunk(self, chunk_id):
images = []
labels = []
maximum = (chunk_id + 1) * self._batch_size
if (maximum > len(self.dataframes)):
maximum = len(self.dataframes)
for i in range(chunk_id * self._batch_size, maximum):
img_name = self.dataframes.iloc[i, 0]
image = Image.open(img_name)
cl = self.dataframes.iloc[i, 1:].values[0]
image = self._scale_image(image)
image = np.moveaxis((np.array(image).astype('float32')),
-1, 0)
image -= np.mean(image, keepdims=True)
image /= (np.std(image, keepdims=True) + 1e-6)
images.append(image)
yv = np.zeros(num_classes)
yv[classes.index(cl)] = 1
labels.append(yv)
result = {}
features = np.array(images)
lab = np.array(labels).astype('float32')
result[self._streams[0].m_name] = features
result[self._streams[1].m_name] = lab
return result
try:
with open(map_file_train) as f:
csv_reader = csv.reader(f, delimiter='\t')
for row in csv_reader:
cmd = row[1]
classes_set.add(cmd)
num_train = num_train + 1
except Exception as e:
raise Exception(
"No train_map.txt file found in path " + path_to_folder +
". Did you create a dataset using create_balanced_dataset()?")
num_classes = len(classes)
with open(map_file_test) as f:
for num_test, l in enumerate(f):
pass
# transforms = [
# xforms.scale(width=self.__image_width, height=self.__image_height, channels=num_channels, interpolations='linear'),
# xforms.mean(mean_file)
# ]
dataset_train = TrackDataset(map_file=map_file_train,
streams=[
dict(name='features',
shape=(num_channels,
self.__image_height,
self.__image_width)),
dict(name='labels',
shape=(num_classes, ))
])
reader_train = MinibatchSource([dataset_train], randomize=True)
# a = dataset_train.num_chunks()
dataset_test = TrackDataset(map_file=map_file_test,
streams=[
dict(name='features',
shape=(num_channels,
self.__image_height,
self.__image_width)),
dict(name='labels',
shape=(num_classes, ))
])
reader_test = MinibatchSource([dataset_test], randomize=True)
# ImageDeserializer loads images in the BGR format, not RGB
# reader_train = MinibatchSource(ImageDeserializer(map_file_train, StreamDefs(
# features = StreamDef(field='image', transforms=transforms),
# labels = StreamDef(field='label', shape=num_classes)
# )))
# reader_test = MinibatchSource(ImageDeserializer(map_file_test, StreamDefs(
# features = StreamDef(field='image', transforms=transforms),
# labels = StreamDef(field='label', shape=num_classes)
# )))
# mb = reader_train.next_minibatch(10)
input_var = input_variable(
(num_channels, self.__image_height, self.__image_width))
label_var = input_variable((num_classes))
model = model_definition(input_var)
ce = cross_entropy_with_softmax(model, label_var)
pe = classification_error(model, label_var)
epoch_size = num_train
lr_per_minibatch = learning_parameter_schedule([0.01] * 10 +
[0.003] * 10 + [0.001],
epoch_size=epoch_size)
momentums = momentum_schedule(0.9, minibatch_size=minibatch_size)
l2_reg_weight = 0.001
learner = momentum_sgd(model.parameters,
lr=lr_per_minibatch,
momentum=momentums,
l2_regularization_weight=l2_reg_weight)
progress_printer = ProgressPrinter(tag='Training', num_epochs=epochs)
trainer = cntk.train.Trainer(model, (ce, pe), [learner],
[progress_printer])
input_map = {
input_var: reader_train.streams.features,
label_var: reader_train.streams.labels
}
print("Training started")
batch_index = 0
plot_data = {'batchindex': [], 'loss': [], 'error': []}
for epoch in range(epochs):
sample_count = 0
while sample_count < epoch_size:
data: MinibatchSource = reader_train.next_minibatch(
min(minibatch_size, epoch_size - sample_count),
input_map=input_map)
trainer.train_minibatch(data)
sample_count += data[label_var].num_samples
batch_index += 1
plot_data['batchindex'].append(batch_index)
plot_data['loss'].append(
trainer.previous_minibatch_loss_average)
plot_data['error'].append(
trainer.previous_minibatch_evaluation_average)
trainer.summarize_training_progress()
metric_numer = 0
metric_denom = 0
sample_count = 0
minibatch_index = 0
epoch_size = num_test
while sample_count < epoch_size:
current_minibatch = min(minibatch_size, epoch_size - sample_count)
data = reader_test.next_minibatch(current_minibatch,
input_map=input_map)
metric_numer += trainer.test_minibatch(data) * current_minibatch
metric_denom += current_minibatch
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("")
model.save(output_model_path, format=ModelFormat.ONNX)
def test_learning_parameter_schedule(params, expectation, minibatch_size):
l = learning_parameter_schedule(*params)
assert l.minibatch_size == minibatch_size
assert [l[i] for i in range(len(expectation))] == expectation
def test_learner_init_legacy():
i = C.input_variable(shape=(1,), needs_gradient=True, name='a')
w = parameter(shape=(1,))
res = i * w
# for backcompatibility test
# this will be deprecated in future version
learner = sgd(res.parameters, lr=learning_rate_schedule(0.1, UnitType.sample))
assert learner._learning_rate_schedule.minibatch_size == 1 # the deprecated per sample schedule should not use compatible mode
assert learner.learning_rate() == 0.1
# for backcompatibility test
# this will be deprecated in future version
# The UnitType will provide per minibatch instruction for the learner
# this will be deprecated in future version
learner = sgd(res.parameters, lr=learning_rate_schedule(0.1, UnitType.minibatch))
assert learner.is_compatible_mode() == False
assert learner.learning_rate() == 0.1
assert learner.minibatch_size == C.learners.IGNORE
assert learner._learning_rate_schedule.minibatch_size == 0
# for backcompatibility test, in reset learning rate, the learner won't receive the reference minibatch size from the schedule
# user will need to specify the reference minibatch size explicitly
# this will be deprecated in future version
learner = sgd(res.parameters, lr=0.1)
learner.reset_learning_rate(learning_rate_schedule([1, 2, 3], UnitType.minibatch))
assert learner.learning_rate() == 1.0
learner.minibatch_size = C.learners.IGNORE # reset to be per minibatch
assert learner.minibatch_size == C.learners.IGNORE
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.is_compatible_mode() == True
# for backcompatibility test
# this will be deprecated in future version
learner = sgd(res.parameters, lr=learning_rate_schedule(0.1, UnitType.sample), minibatch_size=C.learners.IGNORE)
assert learner.is_compatible_mode() == True
assert learner.learning_rate() == 0.1
assert learner.minibatch_size == C.learners.IGNORE # the learner's reference minibatch size is still 0
# this will be deprecated in future version: This is logical invalid combination but it was the only way to use mean gradient and set learning rate in the past.
learner = sgd(res.parameters, lr=learning_rate_schedule(0.1, UnitType.sample), use_mean_gradient=True)
assert learner.is_compatible_mode() == True
assert learner.learning_rate() == 0.1
#test the override in the new version
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.minibatch_size == C.learners.IGNORE # the learner's reference minibatch size is still 0
# for backcompatibility test
# this will be deprecated in future version
# The UnitType will provide per minibatch instruction for the learner
# this will be deprecated in future version
learner = sgd(res.parameters, lr=learning_rate_schedule(0.1, UnitType.minibatch), minibatch_size=C.learners.IGNORE)
assert learner.is_compatible_mode() == True
assert learner.learning_rate() == 0.1
assert learner.minibatch_size == C.learners.IGNORE
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
# for backcompatibility test, in reset learning rate, the learner won't receive the reference minibatch size from the schedule
# user will need to specify the reference minibatch size explicitly
# this will be deprecated in future version
learner = sgd(res.parameters, lr=0.1)
learner.reset_learning_rate(learning_rate_schedule([1, 2, 3], UnitType.minibatch))
assert learner.learning_rate() == 1.0
learner.minibatch_size = C.learners.IGNORE # reset to be per minibatch
assert learner.minibatch_size == C.learners.IGNORE
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.is_compatible_mode() == True
learner_parameter = learner.parameters
from cntk.variables import Parameter
param = learner_parameter[0]
assert isinstance(param, Parameter)
unit_gain_value = C.default_unit_gain_value()
assert unit_gain_value
# back compatible API test
momentum_time_constant = C.momentum_as_time_constant_schedule(1100)
lr_per_sample = learning_parameter_schedule(0.1, minibatch_size=1)
C.momentum_sgd(res.parameters, lr_per_sample, momentum_time_constant)
C.momentum_sgd(res.parameters, lr_per_sample, momentum_time_constant, unit_gain_value)
C.momentum_sgd(res.parameters, lr_per_sample, momentum_time_constant, unit_gain=unit_gain_value)
C.set_default_unit_gain_value(False)
unit_gain_value = C.default_unit_gain_value()
assert not unit_gain_value
C.set_default_unit_gain_value(True)
unit_gain_value = C.default_unit_gain_value()
assert unit_gain_value
lr_per_sample = learning_rate_schedule([(3, 0.1), (2, 0.2), (1, 0.3)], unit=UnitType.sample)
C.fsadagrad(res.parameters, lr=lr_per_sample, momentum=momentum_time_constant)
C.fsadagrad(res.parameters, lr_per_sample, momentum_time_constant, unit_gain_value)
C.fsadagrad(res.parameters, lr=lr_per_sample, momentum=momentum_time_constant, unit_gain=unit_gain_value)
gamma, inc, dec, max, min = [0.5, 1.2, 0.7, 10, 1e-8]
lr_per_sample = learning_rate_schedule([0.1, 0.2], unit=UnitType.sample, epoch_size=100)
C.rmsprop(res.parameters, lr_per_sample, gamma, inc, dec, max, min, True)
C.adadelta(res.parameters, lr_per_sample, use_mean_gradient=True)
def test_learner_init():
i = C.input_variable(shape=(1,), needs_gradient=True, name='a')
w = parameter(shape=(1,))
res = i * w
#test new API: learning_parameter_schedule
#explicitly specify reference minibatch size and learning rate is in number:
learner = sgd(res.parameters, lr=0.1, minibatch_size = 25)
assert learner.is_compatible_mode() == False
assert learner.minibatch_size == 25 #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == 25
assert learner.learning_rate() == 0.1
#no explicitly specification of reference minibatch size and learning rate is in number:
learner = sgd(res.parameters, lr=learning_parameter_schedule(0.1))
assert learner.is_compatible_mode() == False
assert learner.minibatch_size == C.learners.IGNORE #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.learning_rate() == 0.1
learner = sgd(res.parameters, lr=learning_parameter_schedule(0.1, 20), minibatch_size = 25)
assert learner.is_compatible_mode() == False
assert learner.minibatch_size == 25 #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == 20
assert learner.learning_rate() == 0.1
learner = sgd(res.parameters, lr=learning_parameter_schedule(0.1, 20))
assert learner.is_compatible_mode() == False
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == 20
assert learner.learning_rate() == 0.1
#no explicitly specification of reference minibatch size and learning rate is in number:
learner = sgd(res.parameters, lr=learning_parameter_schedule(0.1))
assert learner.is_compatible_mode() == False
assert learner.minibatch_size == C.learners.IGNORE #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.learning_rate() == 0.1
#no explicitly specification of reference minibatch size and learning rate is in number:
learner = sgd(res.parameters, lr=learning_parameter_schedule(0.1), minibatch_size=C.learners.IGNORE)
assert learner.is_compatible_mode() == True
assert learner.minibatch_size == C.learners.IGNORE #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.learning_rate() == 0.1
learner = sgd(res.parameters, lr=learning_parameter_schedule(0.1, 20), minibatch_size=C.learners.IGNORE)
assert learner.is_compatible_mode() == True
assert learner.minibatch_size == C.learners.IGNORE #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == 20
assert learner.learning_rate() == 0.1
#no explicitly specification of reference minibatch size and learning rate is in number:
learner = sgd(res.parameters, lr=learning_parameter_schedule(0.1), minibatch_size=C.learners.IGNORE)
assert learner.is_compatible_mode() == True
assert learner.minibatch_size == C.learners.IGNORE #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.learning_rate() == 0.1
mysgd = C.sgd(parameters=res.parameters, lr=0.4, minibatch_size=32)
assert mysgd.minibatch_size == 32
assert mysgd._learning_rate_schedule.minibatch_size == 32
assert mysgd.learning_rate() == 0.4
mymomentum = C.momentum_sgd(parameters=res.parameters, lr=0.4, momentum=0.9, minibatch_size=32)
assert mymomentum.minibatch_size == 32
assert mymomentum._learning_rate_schedule.minibatch_size == 32
assert mymomentum.learning_rate() == 0.4
myadadelta = C.adadelta(parameters=res.parameters, lr=0.4, minibatch_size=32)
assert myadadelta.minibatch_size == 32
assert myadadelta._learning_rate_schedule.minibatch_size == 32
assert myadadelta.learning_rate() == 0.4
myadam = C.adam(parameters=res.parameters, lr=0.4, momentum=0.9, variance_momentum=0.9, minibatch_size=32)
assert myadam.minibatch_size == 32
assert myadam._learning_rate_schedule.minibatch_size == 32
assert myadam.learning_rate() == 0.4
myadagrad = C.adagrad(parameters=res.parameters, lr=0.4, minibatch_size=32)
assert myadagrad.minibatch_size == 32
assert myadagrad._learning_rate_schedule.minibatch_size == 32
assert myadagrad.learning_rate() == 0.4
myfsadagrad = C.fsadagrad(parameters=res.parameters, lr=0.4, momentum=0.9, variance_momentum=0.9,
minibatch_size=32)
assert myfsadagrad.minibatch_size == 32
assert myfsadagrad._learning_rate_schedule.minibatch_size == 32
assert myfsadagrad.learning_rate() == 0.4
mynesterov = C.nesterov(parameters=res.parameters, lr=0.4, momentum=0.9, minibatch_size=32)
assert mynesterov.minibatch_size == 32
assert mynesterov._learning_rate_schedule.minibatch_size == 32
assert mynesterov.learning_rate() == 0.4
myrmsrop = C.rmsprop(parameters=res.parameters, lr=0.4, gamma=0.5, inc=1.2, dec=0.7, max=10, min=1e-8,
minibatch_size=32)
assert myrmsrop.minibatch_size == 32
assert myrmsrop._learning_rate_schedule.minibatch_size == 32
assert myrmsrop.learning_rate() == 0.4
mysgd = C.sgd(parameters=res.parameters, lr=[0.4, 0.1, 0.001], minibatch_size=32, epoch_size=512)
assert mysgd.minibatch_size == 32
assert mysgd._learning_rate_schedule.minibatch_size == 32
assert mysgd._learning_rate_schedule[0] == 0.4
assert mysgd._learning_rate_schedule[512] == 0.1
assert mysgd._learning_rate_schedule[512 * 2] == 0.001
mymomentum = C.momentum_sgd(parameters=res.parameters, lr=[0.4, 0.1, 0.001], momentum=[0.9],
minibatch_size=32, epoch_size=512)
assert mymomentum.minibatch_size == 32
assert mymomentum._learning_rate_schedule.minibatch_size == 32
assert mymomentum._learning_rate_schedule[0] == 0.4
assert mymomentum._learning_rate_schedule[512] == 0.1
assert mymomentum._learning_rate_schedule[512 * 2] == 0.001
myadadelta = C.adadelta(parameters=res.parameters, lr=[0.4, 0.1, 0.001],
minibatch_size=32, epoch_size=512)
assert myadadelta.minibatch_size == 32
assert myadadelta._learning_rate_schedule.minibatch_size == 32
assert myadadelta._learning_rate_schedule[0] == 0.4
assert myadadelta._learning_rate_schedule[512] == 0.1
assert myadadelta._learning_rate_schedule[512 * 2] == 0.001
myadam = C.adam(parameters=res.parameters, lr=[0.4, 0.1, 0.001], momentum=[0.9, 0.1, 0.001], variance_momentum=[0.9],
minibatch_size=32, epoch_size=512)
assert myadam.minibatch_size == 32
assert myadam._learning_rate_schedule.minibatch_size == 32
assert myadam._learning_rate_schedule[0] == 0.4
assert myadam._learning_rate_schedule[512] == 0.1
assert myadam._learning_rate_schedule[512 * 2] == 0.001
myadagrad = C.adagrad(parameters=res.parameters, lr=[0.4, 0.1, 0.001], minibatch_size=32, epoch_size=512)
assert myadagrad.minibatch_size == 32
assert myadagrad._learning_rate_schedule.minibatch_size == 32
assert myadagrad._learning_rate_schedule[0] == 0.4
assert myadagrad._learning_rate_schedule[512] == 0.1
assert myadagrad._learning_rate_schedule[512 * 2] == 0.001
myfsadagrad = C.fsadagrad(parameters=res.parameters, lr=[0.4, 0.1, 0.001], momentum=[0.9],
variance_momentum=[0.9],
minibatch_size=32, epoch_size=512)
assert myadagrad.minibatch_size == 32
assert myadagrad._learning_rate_schedule.minibatch_size == 32
assert myadagrad._learning_rate_schedule[0] == 0.4
assert myadagrad._learning_rate_schedule[512] == 0.1
assert myadagrad._learning_rate_schedule[512 * 2] == 0.001
mynesterov = C.nesterov(parameters=res.parameters, lr=[0.4, 0.1, 0.001], momentum=[0.9],
minibatch_size=32, epoch_size=512)
assert mynesterov.minibatch_size == 32
assert mynesterov._learning_rate_schedule.minibatch_size == 32
assert mynesterov._learning_rate_schedule[0] == 0.4
assert mynesterov._learning_rate_schedule[512] == 0.1
assert mynesterov._learning_rate_schedule[512 * 2] == 0.001
myrmsrop = C.rmsprop(parameters=res.parameters, lr=[0.4, 0.1, 0.001], gamma=0.5, inc=1.2, dec=0.7, max=10,
min=1e-8,
minibatch_size=32, epoch_size=512)
assert myrmsrop.minibatch_size == 32
assert myrmsrop._learning_rate_schedule.minibatch_size == 32
assert myrmsrop._learning_rate_schedule[0] == 0.4
assert myrmsrop._learning_rate_schedule[512] == 0.1
assert myrmsrop._learning_rate_schedule[512 * 2] == 0.001
learner_parameter = learner.parameters
from cntk.variables import Parameter
param = learner_parameter[0]
assert isinstance(param, Parameter)
unit_gain_value = C.default_unit_gain_value()
assert unit_gain_value
momentum = C.momentum_schedule(0.999, minibatch_size=1)
lr_per_sample = learning_parameter_schedule(0.1, minibatch_size = 1)
C.momentum_sgd(res.parameters, lr_per_sample, momentum)
C.momentum_sgd(res.parameters, lr_per_sample, momentum, unit_gain_value)
C.momentum_sgd(res.parameters, lr_per_sample, momentum, unit_gain=unit_gain_value)
C.set_default_unit_gain_value(False)
unit_gain_value = C.default_unit_gain_value()
assert not unit_gain_value
lr_per_sample = learning_parameter_schedule([0.1, 0.2], minibatch_size = 1)
C.nesterov(res.parameters, lr=lr_per_sample, momentum=momentum)
C.nesterov(res.parameters, lr_per_sample, momentum, unit_gain_value)
C.nesterov(res.parameters, lr=lr_per_sample, momentum=momentum, unit_gain=unit_gain_value)
lr_per_sample = learning_parameter_schedule([0.1]*3 +[0.2]*2 +[0.3], minibatch_size=1)
C.adagrad(res.parameters, lr=lr_per_sample, need_ave_multiplier=True)
C.set_default_unit_gain_value(True)
unit_gain_value = C.default_unit_gain_value()
assert unit_gain_value
lr_per_sample = learning_parameter_schedule([(3,0.1), (2, 0.2), (1, 0.3)], minibatch_size=1)
C.fsadagrad(res.parameters, lr=lr_per_sample, momentum=momentum)
C.fsadagrad(res.parameters, lr_per_sample, momentum, unit_gain_value)
C.fsadagrad(res.parameters, lr=lr_per_sample, momentum=momentum, unit_gain=unit_gain_value)
gamma, inc, dec, max, min = [0.5, 1.2, 0.7, 10, 1e-8]
lr_per_sample = learning_parameter_schedule([0.1, 0.2], minibatch_size = 1, epoch_size = 100)
C.rmsprop(res.parameters, lr_per_sample, gamma, inc, dec, max, min, True)
C.adadelta(res.parameters, lr_per_sample)
def test_learner_init():
i = C.input_variable(shape=(1, ), needs_gradient=True, name='a')
w = parameter(shape=(1, ))
res = i * w
#test new API: learning_parameter_schedule
#explictly specify reference minibatch size and learning rate is in number:
learner = sgd(res.parameters, lr=0.1, minibatch_size=25)
assert learner.is_compatible_mode() == False
assert learner.minibatch_size == 25 #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == 25
assert learner.learning_rate() == 0.1
#no explictly specification of reference minibatch size and learning rate is in number:
learner = sgd(res.parameters, lr=learning_parameter_schedule(0.1))
assert learner.is_compatible_mode() == False
assert learner.minibatch_size == C.learners.IGNORE #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.learning_rate() == 0.1
learner = sgd(res.parameters,
lr=learning_parameter_schedule(0.1, 20),
minibatch_size=25)
assert learner.is_compatible_mode() == False
assert learner.minibatch_size == 25 #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == 20
assert learner.learning_rate() == 0.1
learner = sgd(res.parameters, lr=learning_parameter_schedule(0.1, 20))
assert learner.is_compatible_mode() == False
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == 20
assert learner.learning_rate() == 0.1
#no explictly specification of reference minibatch size and learning rate is in number:
learner = sgd(res.parameters, lr=learning_parameter_schedule(0.1))
assert learner.is_compatible_mode() == False
assert learner.minibatch_size == C.learners.IGNORE #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.learning_rate() == 0.1
#no explictly specification of reference minibatch size and learning rate is in number:
learner = sgd(res.parameters,
lr=learning_parameter_schedule(0.1),
minibatch_size=C.learners.IGNORE)
assert learner.is_compatible_mode() == True
assert learner.minibatch_size == C.learners.IGNORE #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.learning_rate() == 0.1
learner = sgd(res.parameters,
lr=learning_parameter_schedule(0.1, 20),
minibatch_size=C.learners.IGNORE)
assert learner.is_compatible_mode() == True
assert learner.minibatch_size == C.learners.IGNORE #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == 20
assert learner.learning_rate() == 0.1
#no explictly specification of reference minibatch size and learning rate is in number:
learner = sgd(res.parameters,
lr=learning_parameter_schedule(0.1),
minibatch_size=C.learners.IGNORE)
assert learner.is_compatible_mode() == True
assert learner.minibatch_size == C.learners.IGNORE #the learner's reference minibatch
#with direct learner learning rate number specification, the learning rate schedule get the reference minibatch size from the learner parameters:
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.learning_rate() == 0.1
mysgd = C.sgd(parameters=res.parameters, lr=0.4, minibatch_size=32)
assert mysgd.minibatch_size == 32
assert mysgd._learning_rate_schedule.minibatch_size == 32
assert mysgd.learning_rate() == 0.4
mymomentum = C.momentum_sgd(parameters=res.parameters,
lr=0.4,
momentum=0.9,
minibatch_size=32)
assert mymomentum.minibatch_size == 32
assert mymomentum._learning_rate_schedule.minibatch_size == 32
assert mymomentum.learning_rate() == 0.4
myadadelta = C.adadelta(parameters=res.parameters,
lr=0.4,
minibatch_size=32)
assert myadadelta.minibatch_size == 32
assert myadadelta._learning_rate_schedule.minibatch_size == 32
assert myadadelta.learning_rate() == 0.4
myadam = C.adam(parameters=res.parameters,
lr=0.4,
momentum=0.9,
variance_momentum=0.9,
minibatch_size=32)
assert myadam.minibatch_size == 32
assert myadam._learning_rate_schedule.minibatch_size == 32
assert myadam.learning_rate() == 0.4
myadagrad = C.adagrad(parameters=res.parameters, lr=0.4, minibatch_size=32)
assert myadagrad.minibatch_size == 32
assert myadagrad._learning_rate_schedule.minibatch_size == 32
assert myadagrad.learning_rate() == 0.4
myfsadagrad = C.fsadagrad(parameters=res.parameters,
lr=0.4,
momentum=0.9,
variance_momentum=0.9,
minibatch_size=32)
assert myfsadagrad.minibatch_size == 32
assert myfsadagrad._learning_rate_schedule.minibatch_size == 32
assert myfsadagrad.learning_rate() == 0.4
mynesterov = C.nesterov(parameters=res.parameters,
lr=0.4,
momentum=0.9,
minibatch_size=32)
assert mynesterov.minibatch_size == 32
assert mynesterov._learning_rate_schedule.minibatch_size == 32
assert mynesterov.learning_rate() == 0.4
myrmsrop = C.rmsprop(parameters=res.parameters,
lr=0.4,
gamma=0.5,
inc=1.2,
dec=0.7,
max=10,
min=1e-8,
minibatch_size=32)
assert myrmsrop.minibatch_size == 32
assert myrmsrop._learning_rate_schedule.minibatch_size == 32
assert myrmsrop.learning_rate() == 0.4
mysgd = C.sgd(parameters=res.parameters,
lr=[0.4, 0.1, 0.001],
minibatch_size=32,
epoch_size=512)
assert mysgd.minibatch_size == 32
assert mysgd._learning_rate_schedule.minibatch_size == 32
assert mysgd._learning_rate_schedule[0] == 0.4
assert mysgd._learning_rate_schedule[512] == 0.1
assert mysgd._learning_rate_schedule[512 * 2] == 0.001
mymomentum = C.momentum_sgd(parameters=res.parameters,
lr=[0.4, 0.1, 0.001],
momentum=[0.9],
minibatch_size=32,
epoch_size=512)
assert mymomentum.minibatch_size == 32
assert mymomentum._learning_rate_schedule.minibatch_size == 32
assert mymomentum._learning_rate_schedule[0] == 0.4
assert mymomentum._learning_rate_schedule[512] == 0.1
assert mymomentum._learning_rate_schedule[512 * 2] == 0.001
myadadelta = C.adadelta(parameters=res.parameters,
lr=[0.4, 0.1, 0.001],
minibatch_size=32,
epoch_size=512)
assert myadadelta.minibatch_size == 32
assert myadadelta._learning_rate_schedule.minibatch_size == 32
assert myadadelta._learning_rate_schedule[0] == 0.4
assert myadadelta._learning_rate_schedule[512] == 0.1
assert myadadelta._learning_rate_schedule[512 * 2] == 0.001
myadam = C.adam(parameters=res.parameters,
lr=[0.4, 0.1, 0.001],
momentum=[0.9, 0.1, 0.001],
variance_momentum=[0.9],
minibatch_size=32,
epoch_size=512)
assert myadam.minibatch_size == 32
assert myadam._learning_rate_schedule.minibatch_size == 32
assert myadam._learning_rate_schedule[0] == 0.4
assert myadam._learning_rate_schedule[512] == 0.1
assert myadam._learning_rate_schedule[512 * 2] == 0.001
myadagrad = C.adagrad(parameters=res.parameters,
lr=[0.4, 0.1, 0.001],
minibatch_size=32,
epoch_size=512)
assert myadagrad.minibatch_size == 32
assert myadagrad._learning_rate_schedule.minibatch_size == 32
assert myadagrad._learning_rate_schedule[0] == 0.4
assert myadagrad._learning_rate_schedule[512] == 0.1
assert myadagrad._learning_rate_schedule[512 * 2] == 0.001
myfsadagrad = C.fsadagrad(parameters=res.parameters,
lr=[0.4, 0.1, 0.001],
momentum=[0.9],
variance_momentum=[0.9],
minibatch_size=32,
epoch_size=512)
assert myadagrad.minibatch_size == 32
assert myadagrad._learning_rate_schedule.minibatch_size == 32
assert myadagrad._learning_rate_schedule[0] == 0.4
assert myadagrad._learning_rate_schedule[512] == 0.1
assert myadagrad._learning_rate_schedule[512 * 2] == 0.001
mynesterov = C.nesterov(parameters=res.parameters,
lr=[0.4, 0.1, 0.001],
momentum=[0.9],
minibatch_size=32,
epoch_size=512)
assert mynesterov.minibatch_size == 32
assert mynesterov._learning_rate_schedule.minibatch_size == 32
assert mynesterov._learning_rate_schedule[0] == 0.4
assert mynesterov._learning_rate_schedule[512] == 0.1
assert mynesterov._learning_rate_schedule[512 * 2] == 0.001
myrmsrop = C.rmsprop(parameters=res.parameters,
lr=[0.4, 0.1, 0.001],
gamma=0.5,
inc=1.2,
dec=0.7,
max=10,
min=1e-8,
minibatch_size=32,
epoch_size=512)
assert myrmsrop.minibatch_size == 32
assert myrmsrop._learning_rate_schedule.minibatch_size == 32
assert myrmsrop._learning_rate_schedule[0] == 0.4
assert myrmsrop._learning_rate_schedule[512] == 0.1
assert myrmsrop._learning_rate_schedule[512 * 2] == 0.001
learner_parameter = learner.parameters
from cntk.variables import Parameter
param = learner_parameter[0]
assert isinstance(param, Parameter)
unit_gain_value = C.default_unit_gain_value()
assert unit_gain_value
momentum = C.momentum_schedule(0.999, minibatch_size=1)
lr_per_sample = learning_parameter_schedule(0.1, minibatch_size=1)
C.momentum_sgd(res.parameters, lr_per_sample, momentum)
C.momentum_sgd(res.parameters, lr_per_sample, momentum, unit_gain_value)
C.momentum_sgd(res.parameters,
lr_per_sample,
momentum,
unit_gain=unit_gain_value)
C.set_default_unit_gain_value(False)
unit_gain_value = C.default_unit_gain_value()
assert not unit_gain_value
lr_per_sample = learning_parameter_schedule([0.1, 0.2], minibatch_size=1)
C.nesterov(res.parameters, lr=lr_per_sample, momentum=momentum)
C.nesterov(res.parameters, lr_per_sample, momentum, unit_gain_value)
C.nesterov(res.parameters,
lr=lr_per_sample,
momentum=momentum,
unit_gain=unit_gain_value)
lr_per_sample = learning_parameter_schedule([0.1] * 3 + [0.2] * 2 + [0.3],
minibatch_size=1)
C.adagrad(res.parameters, lr=lr_per_sample, need_ave_multiplier=True)
C.set_default_unit_gain_value(True)
unit_gain_value = C.default_unit_gain_value()
assert unit_gain_value
lr_per_sample = learning_parameter_schedule([(3, 0.1), (2, 0.2), (1, 0.3)],
minibatch_size=1)
C.fsadagrad(res.parameters, lr=lr_per_sample, momentum=momentum)
C.fsadagrad(res.parameters, lr_per_sample, momentum, unit_gain_value)
C.fsadagrad(res.parameters,
lr=lr_per_sample,
momentum=momentum,
unit_gain=unit_gain_value)
gamma, inc, dec, max, min = [0.5, 1.2, 0.7, 10, 1e-8]
lr_per_sample = learning_parameter_schedule([0.1, 0.2],
minibatch_size=1,
epoch_size=100)
C.rmsprop(res.parameters, lr_per_sample, gamma, inc, dec, max, min, True)
C.adadelta(res.parameters, lr_per_sample)
def train_model(cntkModel, params, input_map):
log = logging.getLogger("neuralnets1.utils.train_model")
mb_size = params['mb_size']
num_epochs = params['num_epochs']
epoch_size_train = params['epoch_size_train']
epoch_size_test = params['epoch_size_test']
minibatch_source_train = params['train_mbs']
minibatch_source_valid = params['valid_mbs']
#minibatch_source_test = params['test_mbs'] ;
# Instantiate the trainer object
#lr_schedule = learning_rate_schedule(params['learn_rate'], unit=UnitType.minibatch)
lr_per_minibatch = learning_parameter_schedule(params['learn_rate'],
minibatch_size=mb_size,
epoch_size=epoch_size_train)
mm_schedule = momentum_schedule(params['beta_momentum_gd'])
learner = momentum_sgd(cntkModel.parameters,
lr_per_minibatch,
mm_schedule,
l2_regularization_weight=params['l2_reg_weight'])
progress_writers = [ProgressPrinter(tag='Training', num_epochs=num_epochs)]
trainer = Trainer(cntkModel, (params['ce'], params['pe']), learner,
progress_writers)
# Run training epochs
log.info(
'Training transfer learning model for %s epochs (epoch_size_train = %s ) .'
% (num_epochs, epoch_size_train))
# print("Training transfer learning model for {0} epochs (epoch_size_train = {1}).".format(num_epochs, epoch_size_train))
errsVal = []
errsTrain = []
log_number_of_parameters(cntkModel)
for epoch in range(num_epochs):
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:
log.info("Training: processed %s samples" % sample_counts)
# Compute accuracy on training and test sets
errsTrain.append(err_numer / float(sample_counts))
trainer.summarize_training_progress()
errsVal.append(
cntkComputeTestError(trainer, minibatch_source_valid, mb_size,
epoch_size_test, input_map))
trainer.summarize_test_progress()
# Plot training progress
plt.plot(errsTrain, 'b-', errsVal, 'g-')
plt.xlabel('Epoch number')
plt.ylabel('Error')
plt.title('Training error (blue), validation error (green)')
plt.draw()
return cntkModel
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_parameter_schedule(lr_per_mb)
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
x = C.input_variable(shape=(2, ), needs_gradient=False)
t = C.input_variable(shape=(3, ), needs_gradient=False)
init = C.initializer.normal(0.01)
with C.layers.default_options(init=init):
z = C.layers.Sequential(
[C.layers.Dense(12, activation=C.relu),
C.layers.Dense(3)])
y = C.cross_entropy_with_softmax(z(x), t)
acc = C.classification_error(z(x), t)
batch_size = 20
from cntk.learners import sgd, learning_parameter_schedule
lr = learning_parameter_schedule([.5 * (.1**i) for i in range(10000)],
minibatch_size=batch_size,
epoch_size=1000 * batch_size)
learner = sgd(z.parameters, lr)
trainer = C.Trainer(z(x), (y, acc), [learner])
for i in range(min(dataset_size, 100000) // batch_size):
sample = X[batch_size * i:batch_size * (i + 1)]
target = labels[batch_size * i:batch_size * (i + 1)]
trainer.train_minibatch({x: sample, t: target})
loss = trainer.previous_minibatch_loss_average
acc = trainer.previous_minibatch_evaluation_average
print("cost {} - classification error {} - learning rate {}".format(
loss, acc, learner.learning_rate()))
y = C.argmax(z(x))
accuracy = 0
def __init__(self, input_shape, nb_actions,
gamma=0.99, explorer=LinearEpsilonAnnealingExplorer(1, 0.1, 1000000),
learning_rate=0.00025, momentum=0.95, minibatch_size=32,
memory_size=500000, train_after=200000, train_interval=4, target_update_interval=10000,
monitor=True):
self.input_shape = input_shape
self.nb_actions = nb_actions
self.gamma = gamma
self._train_after = train_after
self._train_interval = train_interval
self._target_update_interval = target_update_interval
self._explorer = explorer
self._minibatch_size = minibatch_size
self._history = History(input_shape)
self._memory = ReplayMemory(memory_size, input_shape[1:], 4)
self._num_actions_taken = 0
# Metrics accumulator
self._episode_rewards, self._episode_q_means, self._episode_q_stddev = [], [], []
# Action Value model (used by agent to interact with the environment)
with default_options(activation=relu, init=he_uniform()):
self._action_value_net = Sequential([
Convolution2D((8, 8), 16, strides=4),
Convolution2D((4, 4), 32, strides=2),
Convolution2D((3, 3), 32, strides=1),
Dense(256, init=he_uniform(scale=0.01)),
Dense(nb_actions, activation=None, init=he_uniform(scale=0.01))
])
self._action_value_net.update_signature(Tensor[input_shape])
# Target model used to compute the target Q-values in training, updated
# less frequently for increased stability.
self._target_net = self._action_value_net.clone(CloneMethod.freeze)
# Function computing Q-values targets as part of the computation graph
@Function
@Signature(post_states=Tensor[input_shape], rewards=Tensor[()], terminals=Tensor[()])
def compute_q_targets(post_states, rewards, terminals):
return element_select(
terminals,
rewards,
gamma * reduce_max(self._target_net(post_states), axis=0) + rewards,
)
# Define the loss, using Huber Loss (more robust to outliers)
@Function
@Signature(pre_states=Tensor[input_shape], actions=Tensor[nb_actions],
post_states=Tensor[input_shape], rewards=Tensor[()], terminals=Tensor[()])
def criterion(pre_states, actions, post_states, rewards, terminals):
# Compute the q_targets
q_targets = compute_q_targets(post_states, rewards, terminals)
# actions is a 1-hot encoding of the action done by the agent
q_acted = reduce_sum(self._action_value_net(pre_states) * actions, axis=0)
# Define training criterion as the Huber Loss function
return huber_loss(q_targets, q_acted, 1.0)
# Adam based SGD
lr_schedule = learning_parameter_schedule(learning_rate)
m_schedule = momentum_schedule(momentum)
vm_schedule = momentum_schedule(0.999)
l_sgd = adam(self._action_value_net.parameters, lr_schedule,
momentum=m_schedule, variance_momentum=vm_schedule)
self._metrics_writer = TensorBoardProgressWriter(freq=1, log_dir='metrics', model=criterion) if monitor else None
self._learner = l_sgd
self._trainer = Trainer(criterion, (criterion, None), l_sgd, self._metrics_writer)
def test_learning_parameter_schedule(params, expectation, minibatch_size):
l = learning_parameter_schedule(*params)
assert l.minibatch_size == minibatch_size
assert [l[i] for i in range(len(expectation))] == expectation
def __init__(self,
input_shape,
nb_actions,
gamma=0.99,
explorer=ExpEpsilonAnnealingExplorer(1, 0.1, 1000000),
learning_rate=0.0005,
momentum=0.95,
minibatch_size=128,
memory_size=500000,
train_after=256,
train_interval=2,
target_update_interval=10000,
monitor=True):
self.input_shape = input_shape
self.nb_actions = nb_actions
self.gamma = gamma
self._train_after = train_after
self._train_interval = train_interval
self._target_update_interval = target_update_interval
self._explorer = explorer
self._minibatch_size = minibatch_size
self._memory = ReplayMemory(memory_size, input_shape)
self._num_actions_taken = 0
# Metrics accumulator
self._episode_rewards, self._episode_q_means, self._episode_q_stddev = [], [], []
# Action Value model (used by agent to interact with the environment)
with default_options(activation=relu, init=he_uniform()):
self._action_value_net = Sequential([
# Convolution2D((8, 8), 16, strides=4),
# Convolution2D((4, 4), 32, strides=2),
# Convolution2D((3, 3), 32, strides=1),
Dense(128, init=he_uniform()),
Dense(128, init=he_uniform()),
Dense(nb_actions, activation=None, init=he_uniform())
])
self._action_value_net.update_signature(Tensor[input_shape])
# Target model used to compute the target Q-values in training, updated
# less frequently for increased stability.
self._target_net = self._action_value_net.clone(CloneMethod.freeze)
# Function computing Q-values targets as part of the computation graph
@Function
@Signature(post_states=Tensor[input_shape],
rewards=Tensor[()],
terminals=Tensor[()])
def compute_q_targets(post_states, rewards, terminals):
return element_select(
terminals,
rewards,
gamma * reduce_max(self._target_net(post_states), axis=0) +
rewards,
)
# Define the loss, using Huber Loss (more robust to outliers)
@Function
@Signature(pre_states=Tensor[input_shape],
actions=Tensor[nb_actions],
post_states=Tensor[input_shape],
rewards=Tensor[()],
terminals=Tensor[()])
def criterion(pre_states, actions, post_states, rewards, terminals):
# Compute the q_targets
q_targets = compute_q_targets(post_states, rewards, terminals)
# actions is a 1-hot encoding of the action done by the agent
q_acted = reduce_sum(self._action_value_net(pre_states) * actions,
axis=0)
# Define training criterion as the Huber Loss function
return huber_loss(q_targets, q_acted, 1.0)
# Adam based SGD
lr_schedule = learning_parameter_schedule(learning_rate)
m_schedule = momentum_schedule(momentum)
vm_schedule = momentum_schedule(0.999)
l_sgd = adam(self._action_value_net.parameters,
lr_schedule,
momentum=m_schedule,
variance_momentum=vm_schedule)
log_dir = 'metrics/' + datetime.now().strftime('%Y%m%d%H%M%S')
self._metrics_writer = TensorBoardProgressWriter(
freq=1, log_dir=log_dir, model=criterion) if monitor else None
self._learner = l_sgd
self._trainer = Trainer(criterion, (criterion, None), l_sgd,
self._metrics_writer)
def test_learner_init_legacy():
i = C.input_variable(shape=(1, ), needs_gradient=True, name='a')
w = parameter(shape=(1, ))
res = i * w
# for backcompatibility test
# this will be deprecated in future version
learner = sgd(res.parameters,
lr=learning_rate_schedule(0.1, UnitType.sample))
assert learner._learning_rate_schedule.minibatch_size == 1 # the deprecated per sample schedule should not use compatible mode
assert learner.learning_rate() == 0.1
# for backcompatibility test
# this will be deprecated in future version
# The UnitType will provide per minibatch instruction for the learner
# this will be deprecated in future version
learner = sgd(res.parameters,
lr=learning_rate_schedule(0.1, UnitType.minibatch))
assert learner.is_compatible_mode() == False
assert learner.learning_rate() == 0.1
assert learner.minibatch_size == C.learners.IGNORE
assert learner._learning_rate_schedule.minibatch_size == 0
# for backcompatibility test, in reset learning rate, the learner won't receive the reference minibatch size from the schedule
# user will need to specify the reference minibatch size explicitly
# this will be deprecated in future version
learner = sgd(res.parameters, lr=0.1)
learner.reset_learning_rate(
learning_rate_schedule([1, 2, 3], UnitType.minibatch))
assert learner.learning_rate() == 1.0
learner.minibatch_size = C.learners.IGNORE # reset to be per minibatch
assert learner.minibatch_size == C.learners.IGNORE
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.is_compatible_mode() == True
# for backcompatibility test
# this will be deprecated in future version
learner = sgd(res.parameters,
lr=learning_rate_schedule(0.1, UnitType.sample),
minibatch_size=C.learners.IGNORE)
assert learner.is_compatible_mode() == True
assert learner.learning_rate() == 0.1
assert learner.minibatch_size == C.learners.IGNORE # the learner's reference minibatch size is still 0
# this will be deprecated in future version: This is logical invalid combination but it was the only way to use mean gradient and set learning rate in the past.
learner = sgd(res.parameters,
lr=learning_rate_schedule(0.1, UnitType.sample),
use_mean_gradient=True)
assert learner.is_compatible_mode() == True
assert learner.learning_rate() == 0.1
#test the override in the new version
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.minibatch_size == C.learners.IGNORE # the learner's reference minibatch size is still 0
# for backcompatibility test
# this will be deprecated in future version
# The UnitType will provide per minibatch instruction for the learner
# this will be deprecated in future version
learner = sgd(res.parameters,
lr=learning_rate_schedule(0.1, UnitType.minibatch),
minibatch_size=C.learners.IGNORE)
assert learner.is_compatible_mode() == True
assert learner.learning_rate() == 0.1
assert learner.minibatch_size == C.learners.IGNORE
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
# for backcompatibility test, in reset learning rate, the learner won't receive the reference minibatch size from the schedule
# user will need to specify the reference minibatch size explicitly
# this will be deprecated in future version
learner = sgd(res.parameters, lr=0.1)
learner.reset_learning_rate(
learning_rate_schedule([1, 2, 3], UnitType.minibatch))
assert learner.learning_rate() == 1.0
learner.minibatch_size = C.learners.IGNORE # reset to be per minibatch
assert learner.minibatch_size == C.learners.IGNORE
assert learner._learning_rate_schedule.minibatch_size == C.learners.IGNORE
assert learner.is_compatible_mode() == True
learner_parameter = learner.parameters
from cntk.variables import Parameter
param = learner_parameter[0]
assert isinstance(param, Parameter)
unit_gain_value = C.default_unit_gain_value()
assert unit_gain_value
# back compatible API test
momentum_time_constant = C.momentum_as_time_constant_schedule(1100)
lr_per_sample = learning_parameter_schedule(0.1, minibatch_size=1)
C.momentum_sgd(res.parameters, lr_per_sample, momentum_time_constant)
C.momentum_sgd(res.parameters, lr_per_sample, momentum_time_constant,
unit_gain_value)
C.momentum_sgd(res.parameters,
lr_per_sample,
momentum_time_constant,
unit_gain=unit_gain_value)
C.set_default_unit_gain_value(False)
unit_gain_value = C.default_unit_gain_value()
assert not unit_gain_value
C.set_default_unit_gain_value(True)
unit_gain_value = C.default_unit_gain_value()
assert unit_gain_value
lr_per_sample = learning_rate_schedule([(3, 0.1), (2, 0.2), (1, 0.3)],
unit=UnitType.sample)
C.fsadagrad(res.parameters,
lr=lr_per_sample,
momentum=momentum_time_constant)
C.fsadagrad(res.parameters, lr_per_sample, momentum_time_constant,
unit_gain_value)
C.fsadagrad(res.parameters,
lr=lr_per_sample,
momentum=momentum_time_constant,
unit_gain=unit_gain_value)
gamma, inc, dec, max, min = [0.5, 1.2, 0.7, 10, 1e-8]
lr_per_sample = learning_rate_schedule([0.1, 0.2],
unit=UnitType.sample,
epoch_size=100)
C.rmsprop(res.parameters, lr_per_sample, gamma, inc, dec, max, min, True)
C.adadelta(res.parameters, lr_per_sample, use_mean_gradient=True)
((0.2, 0), [0.2], 0),
((0.2, 0), [0.2, 0.2, 0.2, 0.2], 0),
(([0.2,0.4], 0, 5), [0.2]*5+[0.4]*20, 0),
(([(3,0.2),(2,0.4),(1,0.8)], 0, 5), [0.2]*15+[0.4]*10+[0.8]*20, 0),
]
MOMENTUM_SCHEDULE_PARAMS = [
((0.2,), [0.2]),
((0.2,), [0.2, 0.2, 0.2, 0.2]),
(([0.2,0.4], 5), [0.2]*5+[0.4]*20),
(([(3,0.2),(2,0.4),(1,0.8)], 5), [0.2]*15+[0.4]*10+[0.8]*20),
]
LEARNER_LAMBDAS = [
lambda params: C.adadelta(params),
lambda params: C.adagrad(params, lr=learning_parameter_schedule(1)),
lambda params: C.adam(params, lr=learning_parameter_schedule(1), momentum=C.momentum_schedule(0.9)),
lambda params: C.fsadagrad(params, lr=learning_parameter_schedule(1), momentum=C.momentum_schedule(0.9)),
lambda params: C.nesterov(params, lr=learning_parameter_schedule(1), momentum=C.momentum_schedule(0.9)),
lambda params: C.rmsprop(params, lr=learning_parameter_schedule(1), gamma=0.1, inc=3.0, dec=0.1, max=np.inf, min=1e-8),
lambda params: C.sgd(params, lr=learning_parameter_schedule(1)),
lambda params: C.momentum_sgd(params, lr=learning_parameter_schedule(1), momentum=C.momentum_schedule(0.9))]
@pytest.mark.parametrize("params, expectation, minibatch_size", LR_SCHEDULE_PARAMS_LEGACY)
def test_learning_rate_schedule(params, expectation, minibatch_size):
l = learning_rate_schedule(*params)
assert l.minibatch_size == minibatch_size
assert [l[i] for i in range(len(expectation))] == expectation
@pytest.mark.parametrize("params, expectation, minibatch_size", LR_SCHEDULE_PARAMS)
def test_learning_parameter_schedule(params, expectation, minibatch_size):
def train_and_evaluate(reader_train,
reader_test,
network_name,
epoch_size,
max_epochs,
minibatch_size,
model_dir=None,
log_dir=None,
tensorboard_logdir=None,
gen_heartbeat=False,
fp16=False):
"""
:param reader_train:
:param reader_test:
:param network_name:
:param epoch_size: 一个epoch有多少样本
:param max_epochs: 训练多少个epoch
:param model_dir:
:param log_dir:
:param tensorboard_logdir:
:param gen_heartbeat:
:param fp16:
:return:准确率,用时
"""
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))
with C.default_options(dtype=np.float32):
# create model, and configure learning parameters
model = create_cifar10_model(input_var, 3, num_classes)
# loss and metric
loss = cross_entropy_with_softmax(model, label_var)
error_rate = classification_error(model, label_var)
# shared training parameters
# Set learning parameters
lr_per_sample = []
check_point = [80, 120, 160, 180]
lrs = [3e-2, 3e-3, 3e-4, 3e-4, 5e-5]
for i in range(max_epochs + 1):
if i in range(0, check_point[0]):
lr_per_sample.append(lrs[0])
if i in range(check_point[0], check_point[1]):
lr_per_sample.append(lrs[1])
if i in range(check_point[1], check_point[2]):
lr_per_sample.append(lrs[2])
if i in range(check_point[2], check_point[3]):
lr_per_sample.append(lrs[3])
if i > check_point[3]:
lr_per_sample.append(lrs[4])
lr_schedule = learning_parameter_schedule(lr_per_sample,
minibatch_size=minibatch_size,
epoch_size=epoch_size)
mm_schedule = momentum_schedule(0.9, minibatch_size) #动量
# progress writers
progress_writers = [
ProgressPrinter(tag='Training',
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=model)
progress_writers.append(tensorboard_writer)
# trainer object
l2_reg_weight = 0.0001
learner = adam(model.parameters,
lr_schedule,
mm_schedule,
l2_regularization_weight=l2_reg_weight)
trainer = Trainer(model, (loss, error_rate), 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(model)
print("*********Training Start*********")
start = time.clock()
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 model.parameters:
tensorboard_writer.write_value(parameter.uid + "/mean",
reduce_mean(parameter).eval(),
epoch)
if model_dir:
model.save(
os.path.join(model_dir,
network_name + "_{}.dnn".format(epoch)))
enable_profiler() # begin to collect profiler data after first epoch
# Evaluation parameters
test_epoch_size = 10000
minibatch_size = 32
# 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("")
elapsed = (time.clock() - start)
return 1 - metric_numer / metric_denom, elapsed
