import cv2
import matplotlib.pyplot as plt
from sklearn.neighbors import NearestNeighbors
from sklearn.decomposition import PCA
import chainer
from chainer import cuda, Variable
from chainer.dataset import convert
from chainer import serializers
from chainer.datasets import get_cifar10
from mllogger import MLLogger
from models import small
from models import medium
logger = MLLogger()
def get_color_map_nipy(gradation_num):
colors = []
for idx in [int(x * 255 / gradation_num) for x in xrange(gradation_num)]:
colors.append(plt.cm.nipy_spectral(idx)[0:3])
return (np.array(colors)[::-1, (2, 1, 0)] * 255).astype(np.int)
def plot_nn(data, label, nn_result, nn_result2, k):
colors = get_color_map_nipy(10)
size = 32
canvas = np.zeros((size * 100, size * k * 2, 3), dtype=np.uint8)
# (32x32を縦に配置)
import numpy as np
import chainer
from chainer import Variable, optimizers, serializers, iterators, cuda
from chainer.dataset import convert
from utils.generic import get_args, get_model, write_prediction
from utils.dataset import SceneDatasetCV
from utils.plot import plot_trajectory_eval
from utils.summary_logger import SummaryLogger
from utils.scheduler import AdamScheduler
from utils.evaluation import Evaluator_Direct
from mllogger import MLLogger
logger = MLLogger(init=False)
if __name__ == "__main__":
"""
Evaluation with Cross-Validation
"""
args = get_args()
np.random.seed(args.seed)
start = time.time()
logger.initialize(args.root_dir)
logger.info(vars(args))
save_dir = logger.get_savedir()
logger.info("Written to {}".format(save_dir))
summary = SummaryLogger(args, logger,
os.path.join(args.root_dir, "summary.csv"))
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2017 Takuma Yagi <*****@*****.**>
#
# Distributed under terms of the MIT license.
import argparse
from mllogger import MLLogger
from arghelper import LoadFromJson
from logging import DEBUG
logger = MLLogger("outputs_test", level=DEBUG,
init=False) # Create outputs/yymmdd_HHMMSS/
parser = argparse.ArgumentParser(conflict_handler='resolve')
parser.add_argument('--lr', type=float, default=0.1)
parser.add_argument('--momentum', type=float, default=0.9)
parser.add_argument('--dataset', type=str, default='MNIST')
parser.add_argument('--decay_step', type=int, nargs='+', default=[100, 200])
parser.add_argument('--option', type=str, default=None)
parser.add_argument('--cond', type=str, action=LoadFromJson)
args = parser.parse_args()
logger.initialize()
logger.info('Logger test')
logger.info(vars(args))
save_dir = logger.get_savedir()
logger.save_args(args)