public void Main()
{
// Step-1. Prepare Data
(x_train, y_train, x_test, y_test) = keras.datasets.mnist.load_data();
x_train = x_train.reshape(60000, 28, 28, 1) / 255f;
x_test = x_test.reshape(10000, 28, 28, 1) / 255f;
// Step-2. Build CNN Model with Keras Functional
// input layer
var inputs = keras.Input(shape: (28, 28, 1));
// 1st convolution layer
var outputs = layers.Conv2D(32, kernel_size: 5, activation: keras.activations.Relu).Apply(inputs);
// 2nd maxpooling layer
outputs = layers.MaxPooling2D(2, strides: 2).Apply(outputs);
// 3nd convolution layer
outputs = layers.Conv2D(64, kernel_size: 3, activation: keras.activations.Relu).Apply(outputs);
// 4nd maxpooling layer
outputs = layers.MaxPooling2D(2, strides: 2).Apply(outputs);
// 5nd flatten layer
outputs = layers.Flatten().Apply(outputs);
// 6nd dense layer
outputs = layers.Dense(1024).Apply(outputs);
// 7nd dropout layer
outputs = layers.Dropout(rate: 0.5f).Apply(outputs);
// output layer
outputs = layers.Dense(10).Apply(outputs);
// build keras model
model = keras.Model(inputs, outputs, name: "mnist_model");
// show model summary
model.summary();
// compile keras model into tensorflow's static graph
model.compile(loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true),
optimizer: keras.optimizers.Adam(learning_rate: 0.001f),
metrics: new[] { "accuracy" });
// Step-3. Train Model
// train model by feeding data and labels.
model.fit(x_train, y_train, batch_size: 64, epochs: 2, validation_split: 0.2f);
// evluate the model
model.evaluate(x_test, y_test, verbose: 2);
}