from sklearn import model_selection from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score, confusion_matrix from MNIST_Dataset_Loader.mnist_loader import MNIST import matplotlib.pyplot as plt from matplotlib import style style.use('ggplot') old_stdout = sys.stdout log_file = open("summary.log", "w") sys.stdout = log_file print('\nLoading MNIST Data...') # data = MNIST('./python-mnist/data/') data = MNIST('./MNIST_Dataset_Loader/python-mnist/data/') print('\nLoading Training Data...') img_train, labels_train = data.load_training() train_img = np.array(img_train) train_labels = np.array(labels_train) print('\nLoading Testing Data...') img_test, labels_test = data.load_testing() test_img = np.array(img_test) test_labels = np.array(labels_test) #Features X = train_img #Labels
from sklearn import model_selection from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score, confusion_matrix from MNIST_Dataset_Loader.mnist_loader import MNIST import matplotlib.pyplot as plt from matplotlib import style style.use('ggplot') old_stdout = sys.stdout log_file = open("summary.log", "w") sys.stdout = log_file print('\nLoading MNIST Data...') # data = MNIST('./python-mnist/data/') data = MNIST('./MNIST_Dataset_Loader/dataset/') print('\nLoading Training Data...') img_train, labels_train = data.load_training() train_img = np.array(img_train) train_labels = np.array(labels_train) print('\nLoading Testing Data...') img_test, labels_test = data.load_testing() test_img = np.array(img_test) test_labels = np.array(labels_test) #Features X = train_img #Labels
from matplotlib import style import os from PIL import Image import numpy as np import PIL np.set_printoptions(threshold=np.nan) style.use('ggplot') # Save all the Print Statements in a Log file. old_stdout = sys.stdout log_file = open("summary.log", "w") #sys.stdout = log_file # Load MNIST Data print('\nLoading MNIST Data...') data = MNIST('./MNIST_Dataset_Loader/') features = [] labels = [] count = 1000 for file in os.listdir("A"): try: img = np.asarray( Image.open("A/" + file).convert('L').resize( (45, 45), Image.ANTIALIAS)).flatten() features.append(img) labels.append('A') count = count - 1 if count == 0: break except Exception as e: