public void Run()
{
// Prepare data
var baseDataDirectoryPath = @"E:\DataSets\CatsAndDogs";
var mapFiles = PrepareMapFiles(baseDataDirectoryPath);
// Define the input and output shape.
var inputShape = new int[] { 150, 150, 3 };
var numberOfClasses = 2;
var outputShape = new int[] { numberOfClasses };
// Define data type and device for the model.
var dataType = DataType.Float;
var device = DeviceDescriptor.UseDefaultDevice();
// Setup initializers
var random = new Random(232);
Func <CNTKDictionary> weightInit = () => Initializers.GlorotNormal(random.Next());
var biasInit = Initializers.Zero();
// Ensure reproducible results with CNTK.
CNTKLib.SetFixedRandomSeed((uint)random.Next());
CNTKLib.ForceDeterministicAlgorithms();
// Create the architecture.
var network = Layers.Input(inputShape, dataType)
.Conv2D((3, 3), 32, (1, 1), Padding.None, weightInit(), biasInit, device, dataType)
.ReLU()
.MaxPool2D((2, 2), (2, 2), Padding.None)
.Conv2D((3, 3), 64, (1, 1), Padding.None, weightInit(), biasInit, device, dataType)
.ReLU()
.MaxPool2D((2, 2), (2, 2), Padding.None)
.Conv2D((3, 3), 128, (1, 1), Padding.None, weightInit(), biasInit, device, dataType)
.ReLU()
.MaxPool2D((2, 2), (2, 2), Padding.None)
.Conv2D((3, 3), 128, (1, 1), Padding.None, weightInit(), biasInit, device, dataType)
.ReLU()
.MaxPool2D((2, 2), (2, 2), Padding.None)
.Dense(512, weightInit(), biasInit, device, dataType)
.ReLU()
.Dense(numberOfClasses, weightInit(), biasInit, device, dataType)
.Softmax();
// Get input and target variables from network.
var inputVariable = network.Arguments[0];
var targetVariable = Variable.InputVariable(outputShape, dataType);
// setup loss and learner.
var lossFunc = Losses.CategoricalCrossEntropy(network.Output, targetVariable);
var metricFunc = Metrics.Accuracy(network.Output, targetVariable);
// setup trainer.
var learner = Learners.RMSProp(network.Parameters());
var trainer = Trainer.CreateTrainer(network, lossFunc, metricFunc, new List <Learner> {
learner
});
// Create the network.
var model = new Model(trainer, network, dataType, device);
// Write model summary.
Trace.WriteLine(model.Summary());
// Setup minibatch sources.
var featuresName = "features";
var targetsName = "targets";
// setup name to variable map.
var nameToVariable = new Dictionary <string, Variable>
{
{ featuresName, inputVariable },
{ targetsName, targetVariable },
};
var train = CreateMinibatchSource(mapFiles.trainFilePath, featuresName, targetsName,
numberOfClasses, inputShape, augmentation: true);
var trainingSource = new CntkMinibatchSource(train, nameToVariable);
// Notice augmentation is switched off for validation data.
var valid = CreateMinibatchSource(mapFiles.validFilePath, featuresName, targetsName,
numberOfClasses, inputShape, augmentation: false);
var validationSource = new CntkMinibatchSource(valid, nameToVariable);
// Notice augmentation is switched off for test data.
var test = CreateMinibatchSource(mapFiles.testFilePath, featuresName, targetsName,
numberOfClasses, inputShape, augmentation: false);
var testSource = new CntkMinibatchSource(test, nameToVariable);
// Train the model using the training set.
model.Fit(trainMinibatchSource: trainingSource,
epochs: 100, batchSize: 32,
validationMinibatchSource: validationSource);
// Evaluate the model using the test set.
var(loss, metric) = model.Evaluate(testSource);
// Write the test set loss and metric to debug output.
Trace.WriteLine($"Test set - Loss: {loss}, Metric: {metric}");
// Save model.
model.Network.Save("cats_and_dogs_small_2.cntk");
}
public void Run()
{
// Prepare data
var baseDataDirectoryPath = @"E:\DataSets\Imdb";
var trainFilePath = Path.Combine(baseDataDirectoryPath, "imdb_sparse_train_50w.txt");
var testFilePath = Path.Combine(baseDataDirectoryPath, "imdb_sparse_test_50w.txt");
// Define the input and output shape.
var inputShape = new int[] { 129888 + 4 }; // Number distinct input words + offset for one-hot, sparse
var numberOfClasses = 2;
var outputShape = new int[] { numberOfClasses };
// Define data type and device for the model.
var dataType = DataType.Float;
var device = DeviceDescriptor.UseDefaultDevice();
// Setup initializers
var random = new Random(232);
Func <CNTKDictionary> weightInit = () => Initializers.GlorotNormal(random.Next());
var biasInit = Initializers.Zero();
// Ensure reproducible results with CNTK.
CNTKLib.SetFixedRandomSeed((uint)random.Next());
CNTKLib.ForceDeterministicAlgorithms();
// Create the architecture.
var network = Layers.Input(inputShape, dataType, isSparse: true)
.Embedding(32, weightInit(), dataType, device)
.LSTMStack(32, 1, weightInit(), false, device, dataType)
.Dense(numberOfClasses, weightInit(), biasInit, device, dataType)
.Softmax();
// Since we are processing sequence data,
// wrap network in sequenceLast.
network = CNTKLib.SequenceLast(network);
// Get input and target variables from network.
var inputVariable = network.Arguments[0];
var targetVariable = Variable.InputVariable(outputShape, dataType,
dynamicAxes: new List <Axis>()
{
Axis.DefaultBatchAxis()
},
isSparse: false);
// setup loss and learner.
var lossFunc = Losses.CategoricalCrossEntropy(network.Output, targetVariable);
var metricFunc = Metrics.Accuracy(network.Output, targetVariable);
// setup trainer.
var learner = Learners.Adam(network.Parameters());
var trainer = Trainer.CreateTrainer(network, lossFunc, metricFunc, new List <Learner> {
learner
});
// Create the network.
var model = new Model(trainer, network, dataType, device);
// Write model summary.
Trace.WriteLine(model.Summary());
// Setup minibatch sources.
// Network will be trained using the training set,
// and tested using the test set.
var featuresName = "x";
var targetsName = "y";
// setup name to variable map.
var nameToVariable = new Dictionary <string, Variable>
{
{ featuresName, inputVariable },
{ targetsName, targetVariable },
};
// The order of the training data is randomize.
var train = CreateMinibatchSource(trainFilePath, featuresName, targetsName,
numberOfClasses, inputShape, randomize: true);
var trainingSource = new CntkMinibatchSource(train, nameToVariable);
// Notice randomization is switched off for test data.
var test = CreateMinibatchSource(testFilePath, featuresName, targetsName,
numberOfClasses, inputShape, randomize: false);
var testSource = new CntkMinibatchSource(test, nameToVariable);
// Train the model using the training set.
var history = model.Fit(trainingSource, epochs: 100, batchSize: 512,
validationMinibatchSource: testSource);
// Trace loss and validation history
TraceLossValidationHistory(history);
// Evaluate the model using the test set.
var(loss, metric) = model.Evaluate(testSource);
// Write the test set loss and metric to debug output.
Trace.WriteLine($"Test set - Loss: {loss}, Metric: {metric}");
// Write first ten predictions
var predictions = model.Predict(testSource)
.Take(10);
// Use tensor data directly, since only 1 element pr. sample.
Trace.WriteLine($"Predictions: [{string.Join(", ", predictions.Select(p => p.First()))}]");
}
public void Run()
{
// Prepare data
var baseDataDirectoryPath = @"E:\DataSets\Mnist";
var trainFilePath = Path.Combine(baseDataDirectoryPath, "Train-28x28_cntk_text.txt");
var testFilePath = Path.Combine(baseDataDirectoryPath, "Test-28x28_cntk_text.txt");
// Define the input and output shape.
var inputShape = new int[] { 28, 28, 1 };
var numberOfClasses = 10;
var outputShape = new int[] { numberOfClasses };
// Define data type and device for the model.
var dataType = DataType.Float;
var device = DeviceDescriptor.UseDefaultDevice();
// Setup initializers
var random = new Random(232);
Func <CNTKDictionary> weightInit = () => Initializers.GlorotNormal(random.Next());
var biasInit = Initializers.Zero();
// Ensure reproducible results with CNTK.
CNTKLib.SetFixedRandomSeed((uint)random.Next());
CNTKLib.ForceDeterministicAlgorithms();
// Create the architecture.
var input = Layers.Input(inputShape, dataType);
// scale input between 0 and 1.
var scaledInput = CNTKLib.ElementTimes(Constant.Scalar(0.00390625f, device), input);
var network = scaledInput
.Conv2D((3, 3), 32, (1, 1), Padding.None, weightInit(), biasInit, device, dataType)
.ReLU()
.MaxPool2D((2, 2), (2, 2), Padding.None)
.Conv2D((3, 3), 32, (1, 1), Padding.None, weightInit(), biasInit, device, dataType)
.ReLU()
.MaxPool2D((2, 2), (2, 2), Padding.None)
.Conv2D((3, 3), 32, (1, 1), Padding.None, weightInit(), biasInit, device, dataType)
.ReLU()
.Dense(64, weightInit(), biasInit, device, dataType)
.ReLU()
.Dense(numberOfClasses, weightInit(), biasInit, device, dataType)
.Softmax();
// Get input and target variables from network.
var inputVariable = network.Arguments[0];
var targetVariable = Variable.InputVariable(outputShape, dataType);
// setup loss and learner.
var lossFunc = Losses.CategoricalCrossEntropy(network.Output, targetVariable);
var metricFunc = Metrics.Accuracy(network.Output, targetVariable);
// setup trainer.
var learner = Learners.RMSProp(network.Parameters());
var trainer = Trainer.CreateTrainer(network, lossFunc, metricFunc, new List <Learner> {
learner
});
// Create the network.
var model = new Model(trainer, network, dataType, device);
// Write model summary.
Trace.WriteLine(model.Summary());
// Setup minibatch sources.
// Network will be trained using the training set,
// and tested using the test set.
// setup name to variable map.
var nameToVariable = new Dictionary <string, Variable>
{
{ "features", inputVariable },
{ "labels", targetVariable },
};
// The order of the training data is randomize.
var train = CreateMinibatchSource(trainFilePath, nameToVariable, randomize: true);
var trainingSource = new CntkMinibatchSource(train, nameToVariable);
// Notice randomization is switched off for test data.
var test = CreateMinibatchSource(testFilePath, nameToVariable, randomize: false);
var testSource = new CntkMinibatchSource(test, nameToVariable);
// Train the model using the training set.
model.Fit(trainingSource, epochs: 5, batchSize: 64);
// Evaluate the model using the test set.
var(loss, metric) = model.Evaluate(testSource);
// Write the test set loss and metric to debug output.
Trace.WriteLine($"Test set - Loss: {loss}, Metric: {metric}");
}
