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Lab 6

The document describes a Python program that uses a neural network to recognize the numbers 0, 1, 2, and 39 represented in a 5x3 matrix format. It includes the setup of training data, the initialization and training of an MLPClassifier, and the testing of the model with new data. The predictions for the test data are printed, confirming the model's ability to recognize the numbers accurately.

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Riya
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19 views2 pages

Lab 6

The document describes a Python program that uses a neural network to recognize the numbers 0, 1, 2, and 39 represented in a 5x3 matrix format. It includes the setup of training data, the initialization and training of an MLPClassifier, and the testing of the model with new data. The predictions for the test data are printed, confirming the model's ability to recognize the numbers accurately.

Uploaded by

Riya
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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Write a python program to recognize the number 0, 1, 2, 39.

A 5 * 3 matrix
forms the numbers. For any valid point it is taken as 1 and invalid point it is
taken as 0. The net has to be trained to recognize all the numbers and
when the test data is given, the network has to recognize the particular
numbers

In [ ]: import numpy as np
from sklearn.neural_network import MLPClassifier

In [ ]: # Define training data


X_train = np.array([
[[1, 1, 1],
[1, 0, 1],
[1, 0, 1],
[1, 0, 1],
[1, 1, 1]], # Number 0

[[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0]], # Number 1

[[1, 1, 1],
[0, 0, 1],
[1, 1, 1],
[1, 0, 0],
[1, 1, 1]], # Number 2

[[1, 1, 1],
[1, 0, 1],
[1, 1, 1],
[0, 0, 1],
[1, 1, 1]] # Number 39
])

In [ ]: # Flatten and reshape training data


X_train_flat = X_train.reshape(len(X_train), -1)

In [ ]: # Define labels
y_train = [0, 1, 2, 39]

In [ ]: # Initialize and train MLPClassifier


clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=100
clf.fit(X_train_flat, y_train)

Out[ ]: ▾ MLPClassifier i ?

MLPClassifier(hidden_layer_sizes=(10,), max_iter=1000)

In [ ]: # Test data
X_test = np.array([

[[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0],
[0, 1, 0]], # Number 1

[[1, 1, 1],
[0, 0, 1],
[1, 1, 1],
[1, 0, 0],
[1, 1, 1]], # Number 2

[[1, 1, 1],
[1, 0, 1],
[1, 1, 1],
[0, 0, 1],
[1, 1, 1]], # Number 39

[[1, 1, 1],
[1, 0, 1],
[1, 0, 1],
[1, 0, 1],
[1, 1, 1]] # Number 0

])

In [ ]: # Flatten and reshape test data


X_test_flat = X_test.reshape(len(X_test), -1)

In [ ]: # Predict using trained model


predictions = clf.predict(X_test_flat)

In [ ]: # Print predictions
for i, pred in enumerate(predictions):
print(f"Test {i+1}: Predicted number is {pred}")

Test 1: Predicted number is 1


Test 2: Predicted number is 2
Test 3: Predicted number is 39
Test 4: Predicted number is 0

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