color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
return ax
if __name__ == '__main__':
# Load dataset
x_train, y_train, x_test, y_test = load_dataset()
num_class = len(np.unique(y_train))
feature_dim = len(x_train[0])
num_value = 256
class_names = np.array(["T-shirt/top","Trouser","Pullover","Dress",
"Coat","Sandal","Shirt","Sneaker","Bag","Ankle boot"])
# Initialize perceptron model
perceptron = MultiClassPerceptron(num_class,feature_dim)
# Train model
perceptron.train(x_train,y_train)
# Visualize the learned perceptron weights
# plot_visualization(perceptron.w[:-1,:], class_names, None)
# Classify the test sets
accuracy, y_pred = perceptron.test(x_test,y_test)
"""
Uses image indices collected from running the test set on the model. These indicees are used here to get
the image pixel data of those images and stores them in mixed_imgs[] that will be used to print out images later.
"""
width = 28
mixed_imgs = np.zeros((num_class*2, width, width))
for i in range(num_class):
for y_feature_idx in range(width):
# # # # Visualize the feature likelihoods for high intensity pixels.
class_names = np.array([
"T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", "Sandal",
"Shirt", "Sneaker", "Bag", "Ankle boot"
])
# # plot_visualization(feature_likelihoods, class_names, "Greys")
# # Classify the test sets.
# accuracy, y_pred = NB.test(x_test,y_test)
# print(accuracy)
# # Plot confusion matrix.
# plot_confusion_matrix(y_test, y_pred, classes=class_names, normalize=True,
# title='Confusion matrix, with normalization')
# plt.show()
# Initialize perceptron model.
perceptron = MultiClassPerceptron(num_class, feature_dim)
# Train model.
perceptron.train(x_train, y_train)
# Visualize the learned perceptron weights.
plot_visualization(perceptron.w[:-1, :], class_names, None)
# Classify the test sets.
accuracy, y_pred = perceptron.test(x_test, y_test)
print("Accuracy: ", accuracy)
# Plot confusion matrix.
plot_confusion_matrix(y_test,
y_pred,
classes=class_names,
normalize=True,
title='Confusion matrix, with normalization')
plt.show()
perceptron.high_class_posterior_probs(x_test, y_test)