# -*- coding: utf-8 -*-
import argparse
import os
import six.moves.cPickle as pickle
from alexnet import forward
from util import empty_label, load_image, walk_dir
def predict_image(model, file_path):
_, pred = forward(model, load_image(file_path), empty_label(), train=False)
print '多分これかな？: %s' % (pred.data)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--model', type=str, help='pickle file', default='AlexNet_epoch_100.pickle')
parser.add_argument('-d', '--data_dir', type=str, default='data')
args = parser.parse_args()
model = pickle.load(open(args.model, 'rb'))
walk_dir(args.data_dir, lambda _, f: predict_image(model, f))
from alexnet import forward, model
from util import load_image, num_to_label, walk_dir
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--data_dir', type=str, default='data')
parser.add_argument('-e', '--epoch', type=int, default=100)
args = parser.parse_args()
# init optimizer
optimizer = optimizers.Adam()
optimizer.setup(model)
# load data
data = []
walk_dir(args.data_dir, lambda i, f: data.extend([(num_to_label(i), load_image(f))]))
# learn
for i in range(args.epoch):
random.shuffle(data)
t = 0
pbar = ProgressBar(len(data))
for (label, img) in data:
optimizer.zero_grads()
loss, acc = forward(model, img, label, train=True)
loss.backward()
optimizer.update()
t += 1
