model = tf.sequential();
model.add(tf.layers.conv2d({
    inputShape: [48, 48, 1],
    kernelSize: FILTER_SIZE,
    filters: 64,
    dataFormat: "channelsLast",
    activation: ActFunc.RELU
}));
model.add(tf.layers.maxPooling2d(maxPoolConf));
model.add(tf.layers.conv2d({
    kernelSize: FILTER_SIZE,
    filters: 128,
    dataFormat: "channelsLast",
    activation: ActFunc.RELU
}));
model.add(tf.layers.maxPooling2d(maxPoolConf));
model.add(tf.layers.conv2d({
    kernelSize: FILTER_SIZE,
    filters: 256,
    dataFormat: "channelsLast",
    activation: ActFunc.RELU
}));
model.add(tf.layers.maxPooling2d(maxPoolConf));
model.add(tf.layers.conv2d({
    kernelSize: FILTER_SIZE,
    filters: 512,
    dataFormat: "channelsLast",
    activation: ActFunc.RELU
}));
model.add(tf.layers.maxPooling2d(maxPoolConf));
model.add(tf.layers.flatten());
model.add(tf.layers.dense({units: 128, activation: 'relu'}));
model.add(tf.layers.dense({units: 256, activation: 'relu'}));
model.add(tf.layers.dense({units: 512, activation: 'relu'}));
model.add(tf.layers.dense({units: 1024, activation: 'relu'}));
model.add(tf.layers.dense({
    units: 7,
    activation: 'softmax'
}));
model.compile({
    optimizer: 'adam',
    loss: 'categoricalCrossentropy',
    metrics: ['accuracy', 'categoricalCrossentropy']
});

let image_tensor = tf.tensor4d(training_data.getInputData(), [training_data.length, 48, 48, 1]);
let correct_prediction_tensor = tf.tensor2d(training_data.getLabels(), [training_data.length, 7]);

const history =  model.fit(image_tensor, correct_prediction_tensor,
    {
        batchSize: 128,
        epochs: 10,
        shuffle: true,
        callbacks: {
            onEpochEnd: (epoch, logs) => {
                // Plot the loss and accuracy values at the end of every training epoch.
                console.log(epoch, logs);
            },
            onTrainStart: console.log("Starting Training..."),
            onTrainEnd: console.log("Training Finished!"),
        }
    });