We first import the different libraries required by the code in our project.
As typical, we use numpy for array handling and matplotlib for plotting. These libraries are imported in our project using the following import statements
import numpy as np import matplotlib import matplotlib.pyplot as plot
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 lines 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
We use Keras libraries 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 some utilities that we need in our project. These imports are finished 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 step is appeared here −
Now, as we have all the imports required by our project, we will proceed to charecterize the architecture for our Deep Learning network.