def forwardANN(xList, struct, activ_list, cost_type):
net = Net_structure(struct, [activation_dict[n] for n in activ_list], cost_dict[cost_type])
w_list = []
b_list = []
for l in range(len(struct) - 1):
w_list += [
(array(range(struct[l] * struct[l + 1])).reshape(struct[l], struct[l + 1]) + float(l)) / 100.0
]
b_list += [(array(range(struct[l + 1])) - float(l)) / 100.0]
net.set_w_b(w_list, b_list)
return xList + list(net.net_act_forward(array(xList)))
"""
This script is to manually test if a trained model feels good.
"""
from net_structure import Net_structure
import argparse
import numpy as np
import util.convert_ndarr_img as img_cvt
from logf.printf import printf
def parse_args():
parser = argparse.ArgumentParser('evaluate the quality of trained model')
parser.add_argument('checkpoint', type=str, help='path to the checkpoint file of trained net')
parser.add_argument('test_img', type=str, help='path to the image to be tested')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
net = Net_structure(None)
net.import_(args.checkpoint)
ip_arr = img_cvt.img_to_array(args.test_img)
op = net.net_act_forward(ip_arr)
printf('predicted category: {}', op.argmax(axis=1))