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A Beginner's Guide to Deep Learning

Making Predictions

Explore how to use the predict function to classify letters A, B, and C with a trained model. Understand how forward propagation produces output probabilities, and learn to interpret prediction results in a letter classification task using NumPy deep learning.

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The predict function

Make predictions on the trained model.

Python 3.5
import numpy as np 
import matplotlib.pyplot as plt
A = x[0] # pixel values for letter A
B = x[1] # pixel values for letter B
C = x[2] # pixel values for letter C
def predict(letter, x):
    """Computes predictions on the trained weights and bias"""
    out_h1, out_h2, out_y = forward_propagation(letter, w1, w2, w3, b1, b2, b3)
    print("softmax output:", out_y)
    prediction = np.where(out_y == np.amax(out_y)) # returns the maximum value of array
    print("Highest value of index:", prediction[1][0]) 
    # plot the predicted label
    plt.xlabel("Predicted label") 
    plt.imshow(x[prediction[1][0]].reshape(5, 6)) 
    plt.show()
    plt.savefig('output/predicted.png')
letter = A
# printing the target label
plt.imshow(letter.reshape(5, 6)) 
plt.xlabel("Target label") 
plt.show() 
plt.savefig('output/target.png')
predict(letter, x)
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