用python能干什么有意思的事 用python可以做哪些有趣的事
python\u53ef\u4ee5\u505a\u54ea\u4e9b\u6709\u8da3\u7684\u4e8b\u753b\u753b
\u5728\u672c\u5730\u7528keras\u642d\u5efa\u98ce\u683c\u8f6c\u79fb\u5e73\u53f0
1.\u76f8\u5173\u4f9d\u8d56\u5e93\u7684\u5b89\u88c5
# \u547d\u4ee4\u884c\u5b89\u88c5keras\u3001h5py\u3001tensorflowpip3 install keraspip3 install h5pypip3 install tensorflow
\u5982\u679ctensorflowan\u547d\u4ee4\u884c\u5b89\u88c5\u5931\u8d25\uff0c\u53ef\u4ee5\u5728\u8fd9\u91cc\u4e0b\u8f7dwhl\u5305Python Extension Packages for Windows\uff08\u8fdb\u5165\u7f51\u5740\u540ectrl+F\u8f93\u5165tensorflow\u53ef\u4ee5\u5feb\u901f\u641c\u7d22\uff09
2.\u914d\u7f6e\u8fd0\u884c\u73af\u5883
\u4e0b\u8f7dVGG16\u6a21\u578bz \u653e\u5165\u5982\u4e0b\u76ee\u5f55\u5f53\u4e2d
3.\u4ee3\u7801\u7f16\u5199
from __future__ import print_functionfrom keras.preprocessing.image import load_img, img_to_arrayfrom scipy.misc import imsaveimport numpy as npfrom scipy.optimize import fmin_l_bfgs_bimport timeimport argparsefrom keras.applications import vgg16from keras import backend as Kparser = argparse.ArgumentParser(description='Neural style transfer with Keras.')parser.add_argument('base_image_path', metavar='base', type=str,help='Path to the image to transform.')parser.add_argument('style_reference_image_path', metavar='ref', type=str,help='Path to the style reference image.')parser.add_argument('result_prefix', metavar='res_prefix', type=str,help='Prefix for the saved results.')parser.add_argument('--iter', type=int, default=10, required=False,help='Number of iterations to run.')parser.add_argument('--content_weight', type=float, default=0.025, required=False,help='Content weight.')parser.add_argument('--style_weight', type=float, default=1.0, required=False,help='Style weight.')parser.add_argument('--tv_weight', type=float, default=1.0, required=False,help='Total Variation weight.')args = parser.parse_args()base_image_path = args.base_image_pathstyle_reference_image_path = args.style_reference_image_pathresult_prefix = args.result_prefixiterations = args.iter# these are the weights of the different loss componentstotal_variation_weight = args.tv_weightstyle_weight = args.style_weightcontent_weight = args.content_weight# dimensions of the generated picture.width, height = load_img(base_image_path).sizeimg_nrows = 400img_ncols = int(width * img_nrows / height)# util function to open, resize and format pictures into appropriate tensorsdef preprocess_image(image_path):img = load_img(image_path, target_size=(img_nrows, img_ncols))img = img_to_array(img)img = np.expand_dims(img, axis=0)img = vgg16.preprocess_input(img)return img# util function to convert a tensor into a valid imagedef deprocess_image(x):if K.image_data_format() == 'channels_first':x = x.reshape((3, img_nrows, img_ncols))x = x.transpose((1, 2, 0))else:x = x.reshape((img_nrows, img_ncols, 3))# Remove zero-center by mean pixelx[:, :, 0] += 103.939x[:, :, 1] += 116.779x[:, :, 2] += 123.68# 'BGR'->'RGB'x = x[:, :, ::-1]x = np.clip(x, 0, 255).astype('uint8')return x# get tensor representations of our imagesbase_image = K.variable(preprocess_image(base_image_path))style_reference_image = K.variable(preprocess_image(style_reference_image_path))# this will contain our generated imageif K.image_data_format() == 'channels_first':combination_image = K.placeholder((1, 3, img_nrows, img_ncols))else:combination_image = K.placeholder((1, img_nrows, img_ncols, 3))# combine the 3 images into a single Keras tensorinput_tensor = K.concatenate([base_image,style_reference_image,combination_image], axis=0)# build the VGG16 network with our 3 images as input# the model will be loaded with pre-trained ImageNet weightsmodel = vgg16.VGG16(input_tensor=input_tensor,weights='imagenet', include_top=False)print('Model loaded.')# get the symbolic outputs of each "key" layer (we gave them unique names).outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])# compute the neural style loss# first we need to define 4 util functions# the gram matrix of an image tensor (feature-wise outer product)def gram_matrix(x):assert K.ndim(x) == 3if K.image_data_format() == 'channels_first':features = K.batch_flatten(x)else:features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))gram = K.dot(features, K.transpose(features))return gram# the "style loss" is designed to maintain# the style of the reference image in the generated image.# It is based on the gram matrices (which capture style) of# feature maps from the style reference image# and from the generated imagedef style_loss(style, combination):assert K.ndim(style) == 3assert K.ndim(combination) == 3S = gram_matrix(style)C = gram_matrix(combination)channels = 3size = img_nrows * img_ncolsreturn K.sum(K.square(S - C)) / (4. * (channels ** 2) * (size ** 2))# an auxiliary loss function# designed to maintain the "content" of the# base image in the generated imagedef content_loss(base, combination):return K.sum(K.square(combination - base))# the 3rd loss function, total variation loss,# designed to keep the generated image locally coherentdef total_variation_loss(x):assert K.ndim(x) == 4if K.image_data_format() == 'channels_first':a = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, 1:, :img_ncols - 1])b = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, :img_nrows - 1, 1:])else:a = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, 1:, :img_ncols - 1, :])b = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, :img_nrows - 1, 1:, :])return K.sum(K.pow(a + b, 1.25))# combine these loss functions into a single scalarloss = K.variable(0.)layer_features = outputs_dict['block4_conv2']base_image_features = layer_features[0, :, :, :]combination_features = layer_features[2, :, :, :]loss += content_weight * content_loss(base_image_features,combination_features)feature_layers = ['block1_conv1', 'block2_conv1','block3_conv1', 'block4_conv1','block5_conv1']for layer_name in feature_layers:layer_features = outputs_dict[layer_name]style_reference_features = layer_features[1, :, :, :]combination_features = layer_features[2, :, :, :]sl = style_loss(style_reference_features, combination_features)loss += (style_weight / len(feature_layers)) * slloss += total_variation_weight * total_variation_loss(combination_image)# get the gradients of the generated image wrt the lossgrads = K.gradients(loss, combination_image)outputs = [loss]if isinstance(grads, (list, tuple)):outputs += gradselse:outputs.append(grads)f_outputs = K.function([combination_image], outputs)def eval_loss_and_grads(x):if K.image_data_format() == 'channels_first':x = x.reshape((1, 3, img_nrows, img_ncols))else:x = x.reshape((1, img_nrows, img_ncols, 3))outs = f_outputs([x])loss_value = outs[0]if len(outs[1:]) == 1:grad_values = outs[1].flatten().astype('float64')else:grad_values = np.array(outs[1:]).flatten().astype('float64')return loss_value, grad_values# this Evaluator class makes it possible# to compute loss and gradients in one pass# while retrieving them via two separate functions,# "loss" and "grads". This is done because scipy.optimize# requires separate functions for loss and gradients,# but computing them separately would be inefficient.class Evaluator(object):def __init__(self):self.loss_value = Noneself.grads_values = Nonedef loss(self, x):assert self.loss_value is Noneloss_value, grad_values = eval_loss_and_grads(x)self.loss_value = loss_valueself.grad_values = grad_valuesreturn self.loss_valuedef grads(self, x):assert self.loss_value is not Nonegrad_values = np.copy(self.grad_values)self.loss_value = Noneself.grad_values = Nonereturn grad_valuesevaluator = Evaluator()# run scipy-based optimization (L-BFGS) over the pixels of the generated image# so as to minimize the neural style lossif K.image_data_format() == 'channels_first':x = np.random.uniform(0, 255, (1, 3, img_nrows, img_ncols)) - 128.else:x = np.random.uniform(0, 255, (1, img_nrows, img_ncols, 3)) - 128.for i in range(iterations):print('Start of iteration', i)start_time = time.time()x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),fprime=evaluator.grads, maxfun=20)print('Current loss value:', min_val)# save current generated imageimg = deprocess_image(x.copy())fname = result_prefix + '_at_iteration_%d.png' % iimsave(fname, img)end_time = time.time()print('Image saved as', fname)print('Iteration %d completed in %ds' % (i, end_time - start_time))
\u590d\u5236\u4e0a\u8ff0\u4ee3\u7801\u4fdd\u5b58\u4e3aneural_style_transfer.py(\u968f\u4fbf\u547d\u540d)
4.\u8fd0\u884c
\u65b0\u5efa\u4e00\u4e2a\u7a7a\u6587\u4ef6\u5939\uff0c\u628a\u4e0a\u4e00\u6b65\u9aa4\u7684\u6587\u4ef6neural_style_transfer.py\u653e\u5165\u8fd9\u4e2a\u7a7a\u6587\u4ef6\u5939\u4e2d\u3002\u7136\u540e\u628a\u76f8\u5e94\u7684\u6a21\u677f\u56fe\u7247\uff0c\u5f85\u8f6c\u5316\u56fe\u7247\u653e\u5165\u8be5\u6587\u4ef6\u5f53\u4e2d\u3002
python neural_style_transfer.py \u4f60\u7684\u5f85\u8f6c\u5316\u56fe\u7247\u8def\u5f84 \u6a21\u677f\u56fe\u7247\u8def\u5f84 \u4fdd\u5b58\u7684\u751f\u4ea7\u56fe\u7247\u8def\u5f84\u52a0\u540d\u79f0\uff08\u6ce8\u610f\u4e0d\u9700\u8981\u6709.jpg\u7b49\u540e\u7f00\uff09python neural_style_transfer.py './me.jpg' './starry_night.jpg' './me_t'
\u8fed\u4ee3\u7ed3\u679c\u622a\u56fe\uff1a
\u8fed\u4ee3\u8fc7\u7a0b\u5bf9\u6bd4
\u53ef\u4ee5\u7528Python\u722c\u866b\u6293\u53d6\u7f51\u7edc\u4e0a\u7684\u56fe\u7247\u3001\u7535\u5f71\u94fe\u63a5\uff1b\u8fd8\u53ef\u4ee5\u7528Python\u7f16\u5199\u81ea\u52a8\u5316\u767b\u5f55\u811a\u672c\uff0c\u7528\u4e8e\u4e00\u4e9b\u8bba\u575b\u7684\u81ea\u52a8\u7b7e\u5230\uff1b\u8fd8\u6709\u4e00\u4e9b\u5e94\u7528\u7684\u7b2c\u4e09\u65b9\u5ba2\u6237\u7aef\u4e5f\u662f\u7528Python\u7f16\u5199\u7684\uff1b\u8fd8\u53ef\u4ee5\u7f16\u5199\u4e00\u4e9b\u5c0f\u6e38\u620f\u3002
躺着赚钱
一位匿名知乎网友爆料用Python写了自动化交易程序，2年躺着赚了200万！相当于普通程序员10年的工资，此刻的心情...你懂的！
不过，这位大侠的真实身份也被网友找出了，真是人红了想低调都不行。
程序员式浪漫
程序员不轻易展示浪漫，一旦浪漫起来也是非常帅的。他们不屑于送情书，也无意送玫瑰花，他们用自己的语言表达对自己另一半的爱，这种语言叫作“代码”。
如果上面这段“代码”不过瘾的话，我们接着欣赏。
代码：
(⊙o⊙)…是不是很高深？这句话的汉语解释是“你的一句明天见，偷走了我整夜的睡眠”。
当然，具备了Money和浪漫，也未必能迎娶白富美，毕竟男女比例失衡的现实摆在这里，但不必担心，Python也为宅男准备了锦囊。
宅男必备
“当硬盘没有空间的时候，当身体无力不能下手；我还是不能和你分手，不能和你分手，你的存在是我治愈空虚的粮酒”，这首《至Python》，扎心了，歌词的原意下图正解！
上述好玩的事情，远远不是Python的全部，接下来给大家介绍几个高大上的。
魔镜
每篇清晨，当我们对着镜子梳妆打扮时，镜子上显示现在的时间、今天的天气，或者一句奋斗的名言警句，会不会有种温馨而又不失斗志的生活感呢？
这个魔镜是由树莓派打造的，树莓派是一款主要基于Linux的单机电脑，可以连接电视、显示器、键盘鼠标等设备，还可以玩游戏和播放视频。Python是树莓派的主要编程语言。
买买买
11月份的前几天，最悲伤的是快递小哥，因为快递量逐天下降，直到双十一下午开始迅速反弹。剁手党们决定将积攒了半个月甚至1个月的物品，在11.11当天全部买入，那么怎样才能买到最实惠的商品呢？毫无疑问，用Python呀！
确定商品类别后，用Python爬出各大购物网站的商品销量、购买数以及折扣信息，就可以及时发现性价比高的了。
人工智能世界名画
2015年，德国科学家用深度学习算法让人工智能系统学习梵高、莫奈等世界著名画家的画风绘制新的“人工智能世界名画”，先让我们来欣赏名画风采。这效果是不是让你很动心？
除了建筑自然风景外，我们也可以将自己的照片，转成世界名画风格，也是很酷的吆。
这个程序代码是可以下载的，有基于Python深度学习库DeepPy的实现版本，有基于Python深度学习库TensorFlow的实现版本，有基于Python深度学习库Caffe的实现版本，还有基于Python深度学习库Keras的实现版本。
python学习网，大量的免费python视频教程，欢迎在线学习！
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