Making Predictions
Making predictions on the trained model.
We'll cover the following...
We'll cover the following...
The predict
function
Make predictions on the trained model.
Python 3.5
import numpy as npimport matplotlib.pyplot as pltA = x[0] # pixel values for letter AB = x[1] # pixel values for letter BC = x[2] # pixel values for letter Cdef 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 arrayprint("Highest value of index:", prediction[1][0])# plot the predicted labelplt.xlabel("Predicted label")plt.imshow(x[prediction[1][0]].reshape(5, 6))plt.show()plt.savefig('output/predicted.png')letter = A# printing the target labelplt.imshow(letter.reshape(5, 6))plt.xlabel("Target label")plt.show()plt.savefig('output/target.png')predict(letter, x)
Explanation
Recall that x[0] has the letter A pixel values, x[1] has letter B pixel values, x[2] has letter C pixel values. ...