def seqcla():
# LSTM params
input_dim = 50
output_dim = 128
cell_dim = 128
# model
num_labels = 5
vocab = 2000
embed_dim = 50
t = C.dynamic_axis(name='t')
# temporarily using cntk1 SparseInput because cntk2's Input() will simply allow sparse as a parameter
features = cntk1.SparseInput(vocab, dynamicAxis=t, name='features')
labels = C.input(num_labels, name='labels')
train_reader = C.CNTKTextFormatReader(train_file)
# setup embedding matrix
embedding = C.parameter((embed_dim, vocab), learning_rate_multiplier=0.0,
init_from_file_path=embedding_file)
# get the vector representing the word
sequence = C.times(embedding, features, name='sequence')
# add an LSTM layer
L = lstm_layer(output_dim, cell_dim, sequence, input_dim)
# add a softmax layer on top
w = C.parameter((num_labels, output_dim), name='w')
b = C.parameter((num_labels), name='b')
z = C.plus(C.times(w, L), b, name='z')
z.tag = "output"
# and reconcile the shared dynamic axis
pred = C.reconcile_dynamic_axis(z, labels, name='pred')
ce = C.cross_entropy_with_softmax(labels, pred)
ce.tag = "criterion"
my_sgd = C.SGDParams(epoch_size=0, minibatch_size=10, learning_rates_per_mb=0.1, max_epochs=3)
with C.LocalExecutionContext('seqcla') as ctx:
# train the model
ctx.train(root_nodes=[ce], training_params=my_sgd, input_map=train_reader.map(
features, alias='x', dim=vocab, format='Sparse').map(
labels, alias='y', dim=num_labels, format='Dense'))
# write out the predictions
ctx.write(input_map=train_reader.map(
features, alias='x', dim=vocab, format='Sparse').map(
labels, alias='y', dim=num_labels, format='Dense'))
# do some manual accuracy testing
acc = calc_accuracy(train_file, ctx.output_filename_base)
# and test for the same number...
TOLERANCE_ABSOLUTE = 1E-02
assert np.allclose(acc, 0.6006415396952687, atol=TOLERANCE_ABSOLUTE)
def train_eval_logistic_regression_from_file(criterion_name=None,
eval_name=None,
device_id=-1):
cur_dir = os.path.dirname(__file__)
# Using data from https://github.com/Microsoft/CNTK/wiki/Tutorial
train_file = os.path.join(cur_dir, "Train-3Classes.txt")
test_file = os.path.join(cur_dir, "Test-3Classes.txt")
X = C.input(2)
y = C.input(3)
W = C.parameter(value=np.zeros(shape=(3, 2)))
b = C.parameter(value=np.zeros(shape=(3, 1)))
out = C.times(W, X) + b
out.tag = 'output'
ce = C.cross_entropy_with_softmax(y, out)
ce.name = criterion_name
ce.tag = 'criterion'
eval = C.ops.square_error(y, out)
eval.tag = 'eval'
eval.name = eval_name
# training data readers
train_reader = C.CNTKTextFormatReader(train_file, randomize=None)
# testing data readers
test_reader = C.CNTKTextFormatReader(test_file, randomize=None)
my_sgd = C.SGDParams(epoch_size=0,
minibatch_size=25,
learning_rates_per_mb=0.1,
max_epochs=3)
with C.LocalExecutionContext('logreg') as ctx:
ctx.device_id = device_id
ctx.train(root_nodes=[ce, eval],
training_params=my_sgd,
input_map=train_reader.map(X, alias='I',
dim=2).map(y, alias='L', dim=3))
result = ctx.test(root_nodes=[ce, eval],
input_map=test_reader.map(X, alias='I',
dim=2).map(y,
alias='L',
dim=3))
return result
def train_eval_logistic_regression_with_numpy(criterion_name=None,
eval_name=None,
device_id=-1):
# for repro and tests :-)
np.random.seed(1)
train_X, train_y = synthetic_data(train_N, feature_dim, num_classes)
test_X, test_y = synthetic_data(test_N, feature_dim, num_classes)
# Set up the training data for CNTK. Before writing the CNTK configuration,
# the data will be attached to X.reader.batch and y.reader.batch and then
# serialized.
X = C.input_numpy(train_X)
y = C.input_numpy(train_y)
# define our network -- one weight tensor and a bias
W = C.parameter(value=np.zeros(shape=(num_classes, feature_dim)))
b = C.parameter(value=np.zeros(shape=(num_classes, 1)))
out = C.times(W, X) + b
ce = C.cross_entropy_with_softmax(y, out)
ce.tag = 'criterion'
ce.name = criterion_name
eval = C.ops.cntk1.SquareError(y, out)
eval.tag = 'eval'
eval.name = eval_name
my_sgd = C.SGDParams(epoch_size=0,
minibatch_size=25,
learning_rates_per_mb=0.1,
max_epochs=3)
with C.LocalExecutionContext('logreg_numpy',
device_id=device_id,
clean_up=True) as ctx:
ctx.train(root_nodes=[ce, eval], training_params=my_sgd)
# For testing, we attach the test data to the input nodes.
X.reader.batch, y.reader.batch = test_X, test_y
result = ctx.test(root_nodes=[ce, eval])
return result
def train_eval_mnist_onelayer_from_file(criterion_name=None, eval_name=None):
# Network definition
feat_dim = 784
label_dim = 10
hidden_dim = 200
cur_dir = os.path.dirname(__file__)
training_filename = os.path.join(cur_dir, "Data", "Train-28x28_text.txt")
test_filename = os.path.join(cur_dir, "Data", "Test-28x28_text.txt")
features = C.input(feat_dim)
features.name = 'features'
feat_scale = C.constant(0.00390625)
feats_scaled = C.element_times(features, feat_scale)
labels = C.input(label_dim)
labels.tag = 'label'
labels.name = 'labels'
traning_reader = C.CNTKTextFormatReader(training_filename)
test_reader = C.CNTKTextFormatReader(test_filename)
h1 = add_dnn_sigmoid_layer(feat_dim, hidden_dim, feats_scaled, 1)
out = add_dnn_layer(hidden_dim, label_dim, h1, 1)
out.tag = 'output'
ec = C.cross_entropy_with_softmax(labels, out)
ec.name = criterion_name
ec.tag = 'criterion'
eval = C.ops.square_error(labels, out)
eval.name = eval_name
eval.tag = 'eval'
# Specify the training parameters (settings are scaled down)
my_sgd = C.SGDParams(epoch_size=600,
minibatch_size=32,
learning_rates_per_mb=0.1,
max_epochs=5,
momentum_per_mb=0)
# Create a context or re-use if already there
with C.LocalExecutionContext('mnist_one_layer', clean_up=True) as ctx:
# CNTK actions
ctx.train(root_nodes=[ec, eval],
training_params=my_sgd,
input_map=traning_reader.map(labels,
alias='labels',
dim=label_dim).map(
features,
alias='features',
dim=feat_dim))
result = ctx.test(root_nodes=[ec, eval],
input_map=test_reader.map(labels,
alias='labels',
dim=label_dim).map(
features,
alias='features',
dim=feat_dim))
return result
