Advanced Deep Learning with Keras

Introducing Advanced Deep Learning with Keras
Train labels: {0: 5923, 1: 6742, 2: 5958, 3: 6131, 4: 5842, 5: 5421, 6:
5918, 7: 6265, 8: 5851, 9: 5949}
Test labels: {0: 980, 1: 1135, 2: 1032, 3: 1010, 4: 982, 5: 892, 6: 958,
7: 1028, 8: 974, 9: 1009}
Afterward, the code will plot 25 random digits as shown in the preceding figure,
Figure 1.3.1.
Before discussing the multilayer perceptron classifier model, it is essential to keep in
mind that while MNIST data are 2D tensors, they should be reshaped accordingly
depending on the type of input layer. The following figure shows how a 3 × 3
grayscale image is reshaped for MLPs, CNNs, and RNNs input layers:
Figure 1.3.2: An input image similar to the MNIST data is reshaped depending on the type of input layer.
For simplicity, reshaping of a 3 × 3 grayscale image is shown.
MNIST digits classifier model
The proposed MLP model shown in Figure 1.3.3 can be used for MNIST digit
classification. When the units or perceptrons are exposed, the MLP model is a fully
connected network as shown in Figure 1.3.4. It will also be shown how the output of
the perceptron is computed from inputs as a function of weights, w i
and bias, b n
for
the n th unit. The corresponding Keras implementation is illustrated in Listing 1.3.2.
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