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Step1: Import Necessary Libraries
Numpy: Numpy arrays are very fast and can perform large computations in a very short time.
Matplotlib: Used for Visualizations.
Tensorflow: This is an open-source library that is used for Machine Learning and Artificial Intelligence and provides a range of functions to achieve complex functionalities with single lines of code.
import numpy as np
import pandas as pd
from tensorflow import keras
import matplotlib.pyplot as plt
Step2: Now Load the dataset using "Keras" from the imported version of tensor flow.
(X_train,y_train),(X_test,y_test)=keras.datasets.mnist.load_data()
Step3: Now display the shape and image of the single image in the dataset.The image size contains a 28*28 matrix and length of the training set is 60,000 and testing set is 10,000
print(f"Training set:{X_train.shape}")
print(f"Testing set:{X_test.shape}")
print(X_train[1].shape)
plt.matshow(X_train[1])
Step4: Now normalise the dataset in order to compute the calculations in a fast and accurate manner.
#Normalizing the dataset
x_train=X_train/255
x_test=X_test/255
#Flatting the dataset in order to compute for model building
x_train_flatten=x_train.reshape(len(x_train),28*28)
x_test_flatten=x_test.reshape(len(x_test),28*28)
x_train_flatten.shape
Step5: Building a neural network with single-layer perceptron.Here we can observe as the model is a single-layer perceptron that only contains one input layer and one output layer there is no presence of the hidden layers.
model=keras.Sequential([
keras.layers.Dense(10,input_shape=(784,),
activation='sigmoid')
])
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train_flatten,y_train,epochs=5)
Step6: Output the accuracy of the model on the testing data.
model.evaluate(x_test_flatten,y_test)