TensorFlow

TensorFlow is the famous machine learning library by Google

Usage (TF2)

Here we'll cover usage using TensorFlow 2 which has eager execution.
This is using the Keras API in tensorflow.keras.

Training Loop

Reference
While you can train using model.compile and model.fit, using your own custom training loop is much more flexable and easier to understand. You can write your own training loop by doing the following:

my_model= keras.Sequential([
    keras.layers.Dense(400, input_shape=400, activation='relu'),
    keras.layers.Dense(400, activation='relu'),
    keras.layers.Dense(400, activation='relu'),
    keras.layers.Dense(400, activation='relu'),
    keras.layers.Dense(400, activation='relu'),
    keras.layers.Dense(2)
])

training_loss = []
validation_loss = []
for epoch in range(100):
    print('Start of epoch %d' % (epoch,))
    for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
        with tf.GradientTape() as tape:
            guess = my_model(x_batch_train)
            loss_value = my_custom_loss(y_batch_train, guess)

        # Use the gradient tape to automatically retrieve
        # the gradients of the trainable variables with respect to the loss.
        grads = tape.gradient(loss_value, my_model.trainable_weights)

        # Run one step of gradient descent by updating
        # the value of the variables to minimize the loss.
        optimizer.apply_gradients(zip(grads, my_model.trainable_weights))

        # Log every 200 batches.
        if step % 200 == 0:
            print('Training loss at step %s: %s' % (step, float(loss_value)))
        training_loss.append(loss_value)
        guess_validation = model(x_validation)
        validation_loss.append(my_custom_loss(y_validation, guess_validation))

Save and Load Models

Reference

Estimators

First Contact w/ TF Estimator (TDS)