Advanced Deep Learning with Keras

Chapter 2
The Functional API is guided by the following two concepts:
• A layer is an instance that accepts a tensor as an argument. The output of
a layer is another tensor. To build a model, the layer instances are objects that
are chained to one another through both input and output tensors. This will
have similar end-result as would stacking multiple layers in the sequential
model have. However, using layer instances makes it easier for models to
have either auxiliary or multiple inputs and outputs since the input/output
of each layer will be readily accessible.
• A model is a function between one or more input tensors and output tensors.
In between the model input and output, tensors are the layer instances that
are chained to one another by layer input and output tensors. A model is,
therefore, a function of one or more input layers and one or more output
layers. The model instance formalizes the computational graph on how
the data flows from input(s) to output(s).
After you've completed building the functional API model, the training and
evaluation are then performed by the same functions used in the sequential
model. To illustrate, in a functional API, a 2D convolutional layer, Conv2D, with
32 filters and with x as the layer input tensor and y as the layer output tensor can
be written as:
y = Conv2D(32)(x)
We're also able to stack multiple layers to build our models. For example, we can
rewrite the CNN on MNIST code, the same code we created in the last chapter,
as shown in following listing:
You'll find Listing 2.1.1, cnn-functional-2.1.1.py, as follows. This shows us
how we can convert the cnn-mnist-1.4.1.py code using the functional API:
import numpy as np
from keras.layers import Dense, Dropout, Input
from keras.layers import Conv2D, MaxPooling2D, Flatten
from keras.models import Model
from keras.datasets import mnist
from keras.utils import to_categorical
# compute the number of labels
num_labels = len(np.unique(y_train))
# convert to one-hot vector
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
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