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 = r"../../../../Examples/Image/MNIST/Data/Train-28x28_cntk_text.txt"
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),
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 ffnet():
input_dim = 2
num_output_classes = 2
num_hidden_layers = 2
hidden_layers_dim = 50
epoch_size = sys.maxsize
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
lr = learning_rates_per_sample(0.02)
input = variable((input_dim,), np.float32, needs_gradient=False, name="features")
label = variable((num_output_classes,), np.float32, needs_gradient=False, name="labels")
dev = -1
cntk_dev = cntk_device(dev)
netout = fully_connected_classifier_net(input, num_output_classes, hidden_layers_dim, num_hidden_layers, dev, sigmoid)
ce = cross_entropy_with_softmax(netout, label)
pe = classification_error(netout, label)
#TODO: add save and load module code
ffnet = combine([ce, pe, netout], "classifier_model")
rel_path = r"../../../../Examples/Other/Simple2d/Data/SimpleDataTrain_cntk_text.txt"
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), rel_path)
cm = create_text_mb_source(path, input_dim, num_output_classes, epoch_size)
stream_infos = cm.stream_infos()
for si in stream_infos:
if si.m_name == 'features':
features_si = si
elif si.m_name == 'labels':
labels_si = si
trainer = Trainer(netout, ce, pe, [sgdlearner(netout.owner.parameters(), lr)])
for i in range(0,int(num_minibatches_to_train)):
mb=cm.get_next_minibatch(minibatch_size, cntk_dev)
arguments = dict()
arguments[input] = mb[features_si].m_data
arguments[label] = mb[labels_si].m_data
trainer.train_minibatch(arguments, cntk_dev)
freq = 20
if i % freq == 0:
training_loss = get_train_loss(trainer)
eval_crit = get_train_eval_criterion(trainer)
print ("Minibatch: {}, Train Loss: {}, Train Evaluation Criterion: {}".format(i, training_loss, eval_crit))
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 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 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 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)
# Instantiate the trainer object to drive the model training
trainer = Trainer(netout, ce, pe, [sgd(netout.parameters, lr=0.005)])
# 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 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)
# 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
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 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/MNIST/Data/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.stream_info(feature_stream_name)
labels_si = mb_source.stream_info(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 = 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)
if debug_output:
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/MNIST/Data/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)
])
features_si = test_mb_source.stream_info(feature_stream_name)
labels_si = test_mb_source.stream_info(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.get_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].m_data,
label: mb[labels_si].m_data
}
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
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, 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(
*
"../../../../Examples/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 = {
input: reader_train.streams.features,
label: reader_train.streams.labels
}
# 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 = 64
num_samples_per_sweep = 60000
num_sweeps_to_train_with = 10
num_minibatches_to_train = (num_samples_per_sweep *
num_sweeps_to_train_with) / minibatch_size
training_progress_output_freq = 500
if debug_output:
training_progress_output_freq = training_progress_output_freq / 4
for i in range(0, int(num_minibatches_to_train)):
mb = reader_train.next_minibatch(minibatch_size, input_map=input_map)
trainer.train_minibatch(mb)
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)
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 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, 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(*"../../../../Examples/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 = {
input : reader_train.streams.features,
label : reader_train.streams.labels
}
# 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 = 64
num_samples_per_sweep = 60000
num_sweeps_to_train_with = 10
num_minibatches_to_train = (num_samples_per_sweep * num_sweeps_to_train_with) / minibatch_size
training_progress_output_freq = 500
if debug_output:
training_progress_output_freq = training_progress_output_freq/4
for i in range(0, int(num_minibatches_to_train)):
mb = reader_train.next_minibatch(minibatch_size, input_map=input_map)
trainer.train_minibatch(mb)
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)
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 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.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], 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.get_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
