input_height = args.input_height input_width = args.input_width resize_op = args.resize_op n_class = args.classes iou = args.mIOU image_init = args.image_init # color random.seed(0) colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) for _ in range(5000)] # model model = build_model(model_name, n_class, input_height=input_height, input_width=input_width) model.load_weights(save_weights_path) output_height = model.outputHeight output_width = model.outputWidth print(output_height) print(output_width) # look up test images images = glob.glob(images_path + "*.jpg") + glob.glob( images_path + "*.png") + glob.glob(images_path + "*.jpeg") images.sort() cnt = 0
import numpy as np from sklearn.datasets import fetch_openml from Models import build_model from NeuralNetwork import NetworkLayers, MomentumNetwork, RMSNetwork, AdamNetwork def scale_labels(Y, targtes): temp = np.zeros((targtes, Y.shape[1])) for i in range(Y.shape[1]): temp[int(Y[0, i]), i] = 1 return temp mnist = fetch_openml('mnist_784', version=1) X, y = mnist['data'], mnist['target'] X_train, y_train = X[:10000,].T, y[:10000,].reshape(1, 10000) y_train = scale_labels(y_train, len(np.unique(y))) X_test, y_test = X[10000:12000,].T, y[10000:12000,].reshape(1, 2000) y_test = scale_labels(y_test, len(np.unique(y))) networks = [NetworkLayers(layer=1, neurons=200), NetworkLayers(layer=2, neurons=50), NetworkLayers(layer=3, neurons=10, final=True)] build_model(X_train, y_train, X_test, y_test, 200, networks, 0.01, 128, model='gd', log_loss=True)