In the labs, when the images from random training examples are drawn the vector X that contains the pixel intensities is converted into a matrix. However, I don’t understand why we have to take the transpose of the matrix. Here is an example of the code that appear in the labs:
import warnings
warnings.simplefilter(action=‘ignore’, category=FutureWarning)
You do not need to modify anything in this cell
m, n = X.shape
fig, axes = plt.subplots(8,8, figsize=(5,5))
fig.tight_layout(pad=0.13,rect=[0, 0.03, 1, 0.91]) #[left, bottom, right, top]
widgvis(fig)
for i,ax in enumerate(axes.flat):
# Select random indices
random_index = np.random.randint(m)
# Select rows corresponding to the random indices and
# reshape the image
X_random_reshaped = X[random_index].reshape((20,20)).T
# Display the image
ax.imshow(X_random_reshaped, cmap='gray')
# Predict using the Neural Network
prediction = model.predict(X[random_index].reshape(1,400))
prediction_p = tf.nn.softmax(prediction)
yhat = np.argmax(prediction_p)
# Display the label above the image
ax.set_title(f"{y[random_index,0]},{yhat}",fontsize=10)
ax.set_axis_off()
fig.suptitle("Label, yhat", fontsize=14)
plt.show()
What I’m asking is about the transpose in the line ’ X_random_reshaped = X[random_index].reshape((20,20)).T’.
