For training the model, we will use the architecture from the CIFAR-10 experiment:
arch = @mx.chain mx.Variable(:data) => mx.Convolution(kernel=(3, 3), num_filter=32) => mx.Activation(act_type=:relu) => mx.Dropout(p = 0.25) => mx.Pooling( kernel=(2, 2), pool_type=:max) => mx.Flatten() => mx.FullyConnected(num_hidden=256) => mx.Activation(act_type=:relu) => mx.FullyConnected(num_hidden=10) => mx.SoftmaxOutput(mx.Variable(:label))nnet = mx.FeedForward(arch, context = mx.cpu())mx.fit(nnet, mx.ADAM(), train_data_provider, eval_data = validation_data_provider, n_epoch = 50, initializer = mx.XavierInitializer());
Running the model on CPU can take some time, so please be patient. If you have configured MXNet with GPU support, ...
