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)