{"id":564,"date":"2019-07-12T23:35:32","date_gmt":"2019-07-12T15:35:32","guid":{"rendered":"https:\/\/myoceane.fr\/?p=564"},"modified":"2020-04-12T16:02:13","modified_gmt":"2020-04-12T14:02:13","slug":"machine-learning","status":"publish","type":"post","link":"https:\/\/myoceane.fr\/index.php\/machine-learning\/","title":{"rendered":"[ML] \u985e\u795e\u7d93\u7db2\u8def\u5716\u50cf\u8b58\u5225 &#8211; \u5c07\u5716\u7247\u8f09\u5165 Numpy"},"content":{"rendered":"<div id=\"fb-root\"><\/div>\n\n<p style=\"text-align: justify;\">\u521d\u6b21\u5617\u8a66\u5229\u7528 Python, Tensorflow \u53bb\u5206\u6790\u00a0 PCB \u96fb\u8def\u677f\u4e0a\u9762\u7684\u8755\u523b\u54c1\u8cea\u3002\u5206\u6790\u5716\u7247\u5982\u540c\u9996\u9801\u7684\u793a\u7bc4\u5716\u7247\uff1a<\/p>\n\n\n\n<h5>1. \u5229\u7528 Jupyter Notebook \u5c07\u6a94\u6848\u5448\u73fe\u5728\u7db2\u9801\u4e0a<\/h5>\n<p>\u4ee5\u4e0b\u7684\u7a0b\u5f0f\u78bc\u9996\u5148\u5224\u65b7\u6a94\u6848\u8def\u5f91\uff1a<\/p>\n<pre class=\"lang:python\">import os\nfrom subprocess import check_output\nhome_path = os.getcwd()\nprint(home_path)\nprint(check_output([\"ls\", home_path]).decode(\"utf8\"))<\/pre>\n<p>\u63a5\u8457\u5c07\u6a94\u6848\u540d\u7a31\u8f09\u5165\u9032 onlyfiles \u88e1\u9762\uff0c\u4e26\u4e14\u5229\u7528 _Imgdis \u5c55\u793a\u4e00\u5f35\u5716\u7247\uff1a<\/p>\n<pre class=\"lang:python\">import os.path, sys\nfrom IPython.display import display\nfrom IPython.display import Image as _Imgdis\n\nfolder = os.path.join(home_path, \"png\")\nonlyfiles = []\nfor f in os.listdir(folder):\n    onlyfiles.append(os.path.join(folder,f))\n    \ndisplay(_Imgdis(filename = onlyfiles[40]))<\/pre>\n<h5>2. \u5229\u7528 Jupyter Notebook \u5c07\u5716\u7247\u6a94\u6848\u8f09\u5165\u9032 Numpy \u591a\u7dad\u5ea6\u7684\u5132\u5b58\u7a7a\u9593<\/h5>\n<p style=\"text-align: justify;\">\u4e0b\u4e00\u6b65\u6211\u5011\u5e0c\u671b\u80fd\u5920\u5c07 978 \u5f35\u5716\u7247\u90fd\u8f09\u5165\u9032 Numpy \u7684\u67b6\u69cb\u4e2d\uff0c\u5728\u8f09\u5165\u4e4b\u524d\u6211\u5011\u5fc5\u9808\u8981\u77e5\u9053\u5716\u7247\u7684\u5927\u5c0f\u4ee5\u6e96\u5099\u597d\u76f8\u5c0d\u61c9\u7684 Numpy \u653e\u5165 Pixel \u8cc7\u8a0a\u3002\u53ef\u4ee5\u5229\u7528 linux kernel \u4e2d\u7684 file \u6307\u4ee4\u5982\u4e0b\uff1a<\/p>\n<pre class=\"lang:bash\">file 00978.png\n00978.png: PNG image data, 1600 x 1200, 8-bit\/color RGB, non-interlaced<\/pre>\n<p>\u7531\u6b64\u6211\u5011\u53ef\u4ee5\u77e5\u9053 00978.png \u9019\u4e00\u500b\u6a94\u6848\u662f 1600 x 1200 \u7684\u50cf\u7d20\u5927\u5c0f\uff0c\u6240\u4ee5\u6211\u5011\u5fc5\u9808\u8981\u6e96\u5099\u76f8\u5c0d\u61c9\u7684 dataset \u53bb\u5132\u5b58\u9019\u4e9b\u50cf\u7d20\u7684\u8cc7\u8a0a\u3002<\/p>\n<pre class=\"lang:python\">import numpy as np\nfrom time import time\nfrom time import sleep\n\ntrain_files = []\nfor _file in onlyfiles:\n    train_files.append(_file)\n    \nimage_width = 1600\nimage_height = 1200\nchannels = 3\nnb_classes = 1\ndataset = np.ndarray(shape=(len(train_files), channels, image_height, image_width), dtype=np.float32)\ndataset.shape<\/pre>\n<pre class=\"lang:bash\">(978, 3, 1200, 1600)<\/pre>\n<p>\u63a5\u8457\u4f9d\u5e8f\u8f09\u5165\u5716\u7247\u5167\u5bb9\uff0c\u6700\u5f8c\u5370\u51fa\u7b2c\u4e00\u5f35\u5716\u7247\u7684\u50cf\u7d20\u6578\u503c\u3002<\/p>\n<pre class=\"lang:python\">i = 0\nfor _file in train_files:\n    img = load_img(_file)\n    img.thumbnail((image_width, image_height))\n    x = img_to_array(img)\n    x = x.reshape((3, 1200, 1600))\n    dataset[i] = x\n    \ndataset[0,:,:,:]<\/pre>\n<pre class=\"lang:bash\">array([[[178., 177., 174., ..., 192., 192., 188.],\n        [191., 191., 185., ..., 191., 186., 192.],\n        [191., 185., 191., ..., 155., 129., 159.],\n        ...,\n        [191., 189., 191., ...,  87.,  85., 103.],\n        [ 98.,  96., 104., ...,  84.,  78.,  76.],\n        [ 80.,  74.,  72., ..., 144., 127., 150.]],\n\n       [[191., 188., 190., ...,  97.,  98., 106.],\n        [ 99., 100., 106., ...,  85.,  75.,  74.],\n        [ 73.,  71.,  70., ..., 144., 129., 143.],\n        ...,\n        [194., 185., 187., ...,  36.,  32.,  40.],\n        [ 38.,  34.,  43., ..., 188., 185., 193.],\n        [189., 185., 193., ..., 152., 130., 151.]],\n\n       [[189., 185., 190., ...,  32.,  32.,  41.],\n        [ 38.,  38.,  46., ..., 190., 188., 189.],\n        [190., 189., 190., ..., 151., 129., 148.],\n        ...,\n        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n        [  0.,   0.,   0., ...,   0.,   0.,   0.]]], dtype=float32)<\/pre>\n<p>\u53c3\u8003\u8cc7\u6599\uff1ahttps:\/\/www.kaggle.com\/lgmoneda\/from-image-files-to-numpy-arrays<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u521d\u6b21\u5617\u8a66\u5229\u7528 Python, Tensorflow \u53bb\u5206\u6790\u00a0 PCB \u96fb\u8def\u677f\u4e0a\u9762\u7684\u8755\u523b\u54c1\u8cea\u3002<br \/>\n1. \u5229\u7528 Jupyter Notebook \u5c07\u6a94\u6848\u5448\u73fe\u5728\u7db2\u9801\u4e0a<br \/>\n2. \u5229\u7528 Jupyter Notebook \u5c07\u5716\u7247\u6a94\u6848\u8f09\u5165\u9032 Numpy \u591a\u7dad\u5ea6\u7684\u5132\u5b58\u7a7a\u9593<\/p>\n","protected":false},"author":1,"featured_media":1102,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9,176],"tags":[177,178],"class_list":["post-564","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-ml","category-python","tag-numpy","tag-pcb"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>[ML] \u985e\u795e\u7d93\u7db2\u8def\u5716\u50cf\u8b58\u5225 - \u5c07\u5716\u7247\u8f09\u5165 Numpy - \u60f3\u65b9\u6d89\u6cd5 - \u91cf\u74f6\u5916\u7684\u5929\u7a7a M-Y-Oceane<\/title>\n<meta name=\"description\" content=\"\u521d\u6b21\u5617\u8a66\u5229\u7528 Python, 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