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Chapter 1
Stochastic Gradient Descent (SGD) (see Chapter 15, The Math Behind Deep Learning)
is a particular kind of optimization algorithm used to reduce the mistakes made
by neural networks after each training epoch. We will review SGD and other
optimization algorithms in the next chapters. Once the model is compiled, it
can then be trained with the fit() method, which specifies a few parameters:
• epochs is the number of times the model is exposed to the training set. At
each iteration the optimizer tries to adjust the weights so that the objective
function is minimized.
• batch_size is the number of training instances observed before the
optimizer performs a weight update; there are usually many batches
per epoch.
Training a model in TensorFlow 2.0 is very simple:
# Training the model.
model.fit(X_train, Y_train,
batch_size=BATCH_SIZE, epochs=EPOCHS,
verbose=VERBOSE, validation_split=VALIDATION_SPLIT)
Note that we've reserved part of the training set for validation. The key idea is
that we reserve a part of the training data for measuring the performance on the
validation while training. This is a good practice to follow for any machine learning
task, and one that we will adopt in all of our examples. Please note that we will
return to validation later in this chapter when we talk about overfitting.
Once the model is trained, we can evaluate it on the test set that contains new
examples never seen by the model during the training phase.
Note that, of course, the training set and the test set are rigorously separated. There
is no point evaluating a model on an example that was already used for training. In
TensorFlow 2.0 we can use the method evaluate(X_test, Y_test) to compute the
test_loss and the test_acc:
#evaluate the model
test_loss, test_acc = model.evaluate(X_test, Y_test)
print('Test accuracy:', test_acc)
So, congratulations! You have just defined your first neural network in TensorFlow
2.0. A few lines of code and your computer should be able to recognize handwritten
numbers. Let's run the code and see what the performance is.
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