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# cifar10数据集共有60000张32*32的彩色图像，分为10个类，每类6000张图.50000张用于训练,10000张用于测试# TODO: Build a modelfrom keras.models import Sequentialfrom keras.utils.vis_utils import plot_modelfrom keras.layers.core impor...

注：数据以及tools.py在无人驾驶汽车对两侧道路线图片识别文件中P1_video.py# 视频剪辑所需要包from moviepy.editor import VideoFileClipfrom IPython.display import HTMLfrom tools import *from P1_image import lane_detector# 输出图像是一个...

环境：python3.6数据集:一：tools.pyimport mathimport cv2import numpy as np# 返回图片的灰度单一通道图片def grayscale(img): """Applies the Grayscale transform This will return an image with only one color channel ...

from tensorflow.examples.tutorials.mnist import input_dataimport numpy as npimport tensorflow as tffrom tensorflow.contrib.layers import flattenfrom sklearn.utils import shufflemnist = input_data...

import picklefrom sklearn.utils import shufflefrom sklearn.preprocessing import LabelBinarizerfrom keras.models import Sequentialfrom keras.layers import Convolution2D, MaxPooling2D, Activation, F...

import picklefrom sklearn.preprocessing import LabelBinarizerfrom sklearn.utils import shufflefrom keras.models import Sequentialfrom keras.layers import Dense, Flatten, Activationwith open('trai...

import picklefrom collections import Counterfrom sklearn.utils import shufflefrom sklearn.model_selection import train_test_splitimport tensorflow as tffrom tensorflow.contrib.layers import flatt...