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TensorFlow Model


Shuffle Data

Always shuffle data before training.

When a model is trained, the data is divided into small sets (batches). Each batch is then fed to the model. Shuffling is important to prevent the model getting the same data over again. If using the same data twice, the model will not be able to generalize the data and give the right output. Shuffling gives a better variety of data in each batch.

Example

tf.util.shuffle(data);

TensorFlow Tensors

To use TensorFlow, input data needs to be converted to tensor data.

The examples on the previous pages uses this code to convert to tensors:

// Map x values to Tensor inputs
const inputs = values.map(obj => obj.x);
// Map y values to Tensor labels
const labels = values.map(obj => obj.y);

// Convert inputs and labels to 2d tensors
const inputTensor = tf.tensor2d(inputs, [inputs.length, 1]);
const labelTensor = tf.tensor2d(labels, [labels.length, 1]);

Data Normalization

Data should be normalized before being used in a neural network.

A range of 0 - 1 using min-max are often best for numerical data:

const inputMin = inputTensor.min();
const inputMax = inputTensor.max();
const labelMin = labelTensor.min();
const labelMax = labelTensor.max();
const nmInputs = inputTensor.sub(inputMin).div(inputMax.sub(inputMin));
const nmLabels = labelTensor.sub(labelMin).div(labelMax.sub(labelMin));

Tensorflow Model

A Machine Learning Model is an algorithm that produces output from input.

The examples on the previous pages uses 3 lines to define a ML Model:

const model = tf.sequential();
model.add(tf.layers.dense({inputShape: [1], units: 1, useBias: true}));
model.add(tf.layers.dense({units: 1, useBias: true}));

Sequential ML Model

const model = tf.sequential(); creates a Sequential ML Model.

In a sequential model, the input flows directly to the output. Other models can have multiple inputs and multiple outputs. Sequential is the easiest ML model. It allows you to build a model layer by layer, with weights that correspond to the next layer.

TensorFlow Layers

model.add() is used to add two layers to the model.

tf.layer.dense is a layer type that works in most cases. It multiplies its inputs by a weight-matrix and adds a number (bias) to the result.

Shapes and Units

inputShape: [1] because we have 1 input (x = horsepower).

units: 1 defines the size of the weight matrix: 1 weight for each input (x value).


Compiling a Model

The examples on the previous pages uses 2 lines to compile the TensorFlow model:

model.compile({
  optimizer: tf.train.adam(),
  loss: tf.losses.meanSquaredError
});

The model is set to use the tf.train.adam() optimizer. The Adam optimizer is simple to use and quite effective.

tf.losses.meanSquaredError is the function we well use to compare model predictions and true values.