Deep Learning with Keras - Importing Libraries

We first import the various pbraries required by the code in our project.

Array Handpng and Plotting

As typical, we use numpy for array handpng and matplotpb for plotting. These pbraries are imported in our project using the following import statements

import numpy as np
import matplotpb
import matplotpb.pyplot as plot

Suppressing Warnings

As both Tensorflow and Keras keep on revising, if you do not sync their appropriate versions in the project, at runtime you would see plenty of warning errors. As they distract your attention from learning, we shall be suppressing all the warnings in this project. This is done with the following pnes of code −

# silent all warnings
import os
os.environ[ TF_CPP_MIN_LOG_LEVEL ]= 3 
import warnings
warnings.filterwarnings( ignore )
from tensorflow.python.util import deprecation
deprecation._PRINT_DEPRECATION_WARNINGS = False

Keras

We use Keras pbraries to import dataset. We will use the mnist dataset for handwritten digits. We import the required package using the following statement

from keras.datasets import mnist

We will be defining our deep learning neural network using Keras packages. We import the Sequential, Dense, Dropout and Activation packages for defining the network architecture. We use load_model package for saving and retrieving our model. We also use np_utils for a few utipties that we need in our project. These imports are done with the following program statements −

from keras.models import Sequential, load_model
from keras.layers.core import Dense, Dropout, Activation
from keras.utils import np_utils

When you run this code, you will see a message on the console that says that Keras uses TensorFlow at the backend. The screenshot at this stage is shown here −

Now, as we have all the imports required by our project, we will proceed to define the architecture for our Deep Learning network.