{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A Date with Deep Learning\n", "\n", "Learn how to build a small model that can classify topwear vs bottomwear from the Myntra catalog.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create Dataset\n", "\n", "Fetch a random set of styleids, and download the corresponding product images.\n", "A dataset of just 2000 images is good enough for this usecase." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import json\n", "import requests\n", "import urllib.request\n", "\n", "# read data file\n", "with open('data.json') as data_file:\n", " data = json.load(data_file)\n", "\n", "# iterate over a range of style ids\n", "for styleid in range(1322000, 1323000):\n", " try:\n", " print('Downloading ' + str(styleid) + '...')\n", " \n", " # hit style service\n", " r = requests.get(url='http://developer.myntra.com/style/' + str(styleid)).json()\n", " \n", " # generarate url for product image\n", " imageURL = r['data']['styleImages']['default']['imageURL'].replace('assets.myntassets.com/', 'assets.myntassets.com/w_60/')\n", "\n", " # download the image and save\n", " urllib.request.urlretrieve(imageURL, 'data/' + str(styleid) + '.jpg')\n", "\n", " # update data object with meta\n", " data[styleid] = {\n", " \"masterCategory\": r['data']['masterCategory']['typeName'],\n", " \"subCategory\": r['data']['subCategory']['typeName'],\n", " \"articleType\": r['data']['articleType']['typeName']\n", " }\n", "\n", " except:\n", " # ignore images that result in an error\n", " pass\n", "\n", "# save back the meta data\n", "with open('data.json', 'w') as outfile:\n", " json.dump(data, outfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Investigate dataset" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(80, 60, 3)\n" ] } ], "source": [ "import matplotlib.image as mpimg\n", "import matplotlib.pyplot as plt\n", "\n", "image = mpimg.imread('data/' + '1320032' + '.jpg')\n", "plt.imshow(image)\n", "plt.show()\n", "print(image.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train Model" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/5\n", "4s - loss: 0.4623 - acc: 0.8580\n", "Epoch 2/5\n", "3s - loss: 0.1746 - acc: 0.9440\n", "Epoch 3/5\n", "3s - loss: 0.1268 - acc: 0.9570\n", "Epoch 4/5\n", "3s - loss: 0.0729 - acc: 0.9790\n", "Epoch 5/5\n", "4s - loss: 0.0567 - acc: 0.9810\n" ] } ], "source": [ "import json\n", "import matplotlib\n", "import pickle\n", "import numpy as np\n", "import matplotlib.image as mpimg\n", "\n", "from numpy import newaxis\n", "from keras.models import Sequential, load_model\n", "from keras.layers import Convolution2D, MaxPooling2D, Flatten, Dropout, Dense\n", " \n", "def read_data(batch_size):\n", " \"\"\"\n", " Generator function to load driving logs and input images.\n", " \"\"\"\n", " while 1:\n", " with open('data.json') as data_file:\n", " data = json.load(data_file)\n", " count = 0\n", " inputs = []\n", " targets = []\n", " try:\n", " for styleid, category in data.items():\n", "\n", " # ignore all other types and add class label to each image\n", " label = 0\n", " if category['subCategory'] == 'Topwear':\n", " label = 0\n", " elif category['subCategory'] == 'Bottomwear':\n", " label = 1\n", " else:\n", " continue\n", " \n", " # read image and create a flipped version\n", " image = mpimg.imread('data/' + styleid + '.jpg')\n", " flip_image = np.fliplr(image)\n", " \n", " # ignore greyscale images in dataset\n", " if len(image.shape) == 2:\n", " continue\n", " \n", " # loop\n", " if count == 0:\n", " inputs = np.empty([0, 80, 60, 3], dtype=int)\n", " targets = np.empty([0, ], dtype=int)\n", " \n", " # add images to array till we create batch\n", " if count < int(batch_size/2):\n", " inputs = np.append(inputs, np.array([image, flip_image]), axis=0)\n", " targets = np.append(targets, np.array([label, label]), axis=0)\n", " count += 1\n", " \n", " # yield the batch of images when done\n", " else:\n", " yield inputs, targets\n", " count = 0\n", " except StopIteration:\n", " pass\n", "\n", "sample_count = 1000\n", "batch_size = 10\n", "use_transfer_learning = False\n", "\n", "# define model\n", "if use_transfer_learning:\n", " model = load_model('model.h5')\n", "else:\n", " model = Sequential()\n", " model.add(Convolution2D(2, 5, 5, border_mode='valid', activation='relu', input_shape=(80, 60, 3)))\n", " model.add(MaxPooling2D((2, 2)))\n", " model.add(Convolution2D(4, 5, 5, border_mode='valid', activation='relu'))\n", " model.add(MaxPooling2D((2, 2)))\n", " model.add(Convolution2D(8, 5, 5, border_mode='valid', activation='relu'))\n", " model.add(MaxPooling2D((2, 2)))\n", " model.add(Flatten())\n", " model.add(Dense(32, activation='relu'))\n", " model.add(Dense(16, activation='relu'))\n", " model.add(Dense(2, activation='softmax'))\n", " model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", "\n", "# train model\n", "model.fit_generator(read_data(batch_size), samples_per_epoch=sample_count, nb_epoch=5, verbose=2)\n", "model.save('model.h5')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Test Model" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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m0YoK+agpD434Y/09+vr6Qj+vp2JFqYxhfbItQ03qOQqXMlSCKBQxWFISpJEw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\nw7Y24aohtlzJ5KnpUT7vonM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WY/FqaJDPnztyiu5NrGrO91SiSOmFBbIsWlrUSCE7xQqqNaHSo9wOCVHhzzmk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\nar4gVmJ0T0j/OIBXLetsWge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0C/MaTV10lC9HWsWzR05G7UxRv9h/i438bVD4D1vliuSTClOpTU5//6stlrLs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\ngF81xjwgIn+EFZQ6aJMePXD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mdpLNZleddXigdiYkn9dbxJjucZQxswBPddgiM06K6+xjKJszBdxIFD5KZx0/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\nOWelSNfLM4SfAco0E2ezGg6zYYsVmZi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OHz8e1EnJ9eKLL9ZcE5Uq1fVNLgIMDw8H5ZUrVwTlYtHYXZIyusfVh5wZlDok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\noigx6ABRlBj+P4grDWRNi8I8AAAAAElFTkSuQmCC\n", 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SkeMA/iOATwL4gojcCeA5AD/T/y2tOtQrfGQghpMEK1YU1QsAXE/GULJR9HqoskpySJnP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\n", 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MTfI6S/eQlRntPYtpUZQX15B5nVOI56bt/R555BG/7uJrOdxlcKOlWxs28gph\nNsfpvXFB3chFXjeiVfVAZ3DHQ7UgCkUAtIMoFAFoC4qFItMEL2ZNdLHE4RRRscIzmeFVmZBb8dq4\nhVXHxw4d9sv9SXutcIIpTXaWqc6GbZxSmyu41TMR4kLEK14f+w8cqnb4VZu6OnWIN5kpzHAE77TY\nNzDRZx2Tt93CaupHDnIbuwcs5auWOWSEqux09CpMP0Mle614gmlipZudjiNTfN0TBfvTWL+RHaMJ\nYlo01MvOxlSnpaVXXsPhLl2b+by8Syc2BZHYZvjdAD2iXJ33HwCQSL6at5f0MkT8qgVRKALQFhbE\nEym1xgUQVsUkryBSX+NCA8tzYgyTIzw53SRGxoTLLZkpco5IprzPL49PcThFR9paoXye71Wo8ES2\nd4BzMM4/x0bqRBLcllyayz955SW/vOGYHdU3dPGoH47xn61wwG6bFrqMQ+GmPR69R8bYDxJL2HcS\nmWMrVxQ+oIEB3v+j4Eb7Y0KWtSvKz7ahl9955tgB+/0GFpOYywvfhLHjsKkITTLp6fDEJLvehHxe\nMKqoNifXLTXUgigUAdAOolAEoC0olhEb6JCL4o2Ivj+XYz9IJMT2OB22k/eSx76CRIW/r7i03XCE\nr7VpeItfnpzlCX8mY2mYry4PoG89+0EQFnKfhy1VMRGeqA71sZNheIDp0sS09W/kRMTy+k3cBuy0\ntCibYcX28Rz7UQyvT2Bzv1t0YFcPQsLJkPC4fMTlyFSjHH4SFjpg6Sr7Wioup+StYxwOU4kxZazp\ncKVEfkwh28B3UXv9TcR91yjWmZyrqwVRKAKgHUShCEBbUKyC2Hcw5hTEjVj/70jwWv/cbEacZ210\nXNCIE0INPdFtaUI1zPRnbISPTcc4dGLaCcYNDLJvBCToSYLlRMcHLR0yUeYG3VUu94b5vNgJu/Iz\nGOaI1+mjx/2ycSttyXV831KMOdT6bq6PRJy/SLybWIh9PNMZpkh9LhGrKleNpnk8nZnjFa0+5/Po\nWc/vY3aW/TqlkqVjxYTYyCgmuJ/gU7VQHenu8KjuofPrzxDUgigUAdAOolAEoC0oVkwod+TdilVN\nQA4ASiVeAUqnmC5NT9gc6hdfftGvu2g3b188O2ejXzv7eTUqEWeHXWGOiUDaRQ8bIVhXEOEfBaG+\nnt5sV54SQkgt9wqvQnUmOEd+y6A99uibR/y6/ne90y9n3QpepcBjXbqLnZJdHfzs1YKlZvEeTgrL\nMBPC6/sd/comAAATpklEQVS5DcN7nOxAWCSI9XBk7/FZ5jdzJ+w9zlrH9xKsFuGqfU4TY5qYjMjM\nJ267Y70wjZamjORY9Za6lnbMVwuiUASgLSxIpSoU1Wv5BIb7fkjkgBTFBDXday3Dzos5dyGS4glu\nn1NGnzOca+FF2afS3cVr/a+NW79BSWzhtnHzsF+ODwqNrKo9b2KO/RWU5ZE63sUT50kn6zkywoGN\nY6+wZdq23U6MIwkecZPdfH5lirWspk5YNZUN6zmYca7A724iw1Zqd7dVHfGIfURmTmhoGbak6zZY\n+dKs0NBKhTmsJOTZRYdynK13lIRumVDXN256LncuXhBGhK0s5rwm0PTVnDbWM0T0gPus0qOKtsdi\nutvHYdVMalDpUUXboymKRUSbAPwcgD8F8LtkpbdXjfRoSJhV4xbKjVhJD4XqT/hqSu/JTp4UF+TW\nbW5Cb0pMpSohDiWZrrLE5+ExW96xgwXRDglatHuAI2ynC9YX0xFjynE0zgY4LuRRMy4C9miaZ9OP\nP/9Vv3zRETtxvvq6j/p14Qgf++p+jlQ+e89l9vsCLw4Ue/l5Ln7/z/rlOX+xg9uVBfs+YtvEZjgp\ne79Ikp+nAJ6l18T8ouLXZkj+9Pidh12sSUMXxzznhysvMa2ad7smj/sLAH8A9t/0QaVHFWsAzQjH\nvR/AqDHmKVld59CG0qPGmL3GmL0DAwP1DlEoVi2aoVhXAriFiG4CEAfQCWtRuoko7KzIikqPLrTZ\nivze804eE+T3UpWkUjPngg4k0iJ8JMpU6NIr7EY3sSRTi65BXqkxIb5vpGJXrEyF6c2WDexrOfAk\na9h2OR/MnAjt+OAv/oZfHnCKK8cnOPwkZbiN/WIfxEgtjEOEgQz2C9E2kewVdUlM5X5Oh00LNXtJ\na2IubCQkE9dO0YOwWjbOqWHBpzDGfNoYs8kYMwzggwAeNsb8ClR6VLEGcDp+kE9ilUiPStQbgepZ\njUbnzNtfvfZ6xPkVefkYj5i9Q9Y7nc+zVejs4EDB2Rn2cxz4zsMAgKTIM/GEWyCc4w9jh23Kbcc0\nj97hUT7vye8+DgDICG0vk2afS7dIMa502Ul0eoA94mf/wo3chj62fumUnYRH+XGQTAo9LfFOqk6X\nTFqNkNiJt/Z+Zd6OZOmrzWpILKqDGGMehVV3V+lRxZqAhpooFAFoi1CTemhktiU1YNN/cj4CAHgu\n4DFMMhiSqVI4wvW+cgpJhQ5BoSL8qlNJS3sKs7zhTNjw98kET7JnCnZCHe9hH0PfxmG/fKzPUqvI\n2Ft+XS4ilEpE2MqsU0OJCV/CgNiVtyzaEHIhJhUjNMfEz6VBikZd1HvPq5lWSagFUSgCoB1EoQhA\n21KsRqhn2huuYrndW2V4QzTMvg+5D2KpYKlIRPhGCiWmJzGRn+JV7THFEke8dogcDioxTYt3OKpT\nEPQmztQuPWBXm46waiiqMT4/3MV0rTNuyyQ2HAoXmY7FBJU0Tkq1ItJkG9GikLseLUC2FlpNXI1o\nvRYrFMsI7SAKRQDagmJJWlQPjSjUQnSLnFewXOQQjLAIpygJJXc/UUueLxxjJaGB2xG1SVnhNK8w\nSUchSlJKxHrq1vVzkpNsdSppKdhgip2S+ZBQmC9LtXnrCKyI6Oas0MhNJPjZanllUSmsfvI+Rbbt\n/uXqU7DaOz9TK1dncnVMLYhCEYC2sCD10GgkWWiEmWeN3Fw27IlkSXm+J8MlvJPO98RkmCo8YT+S\ntWIQcRHH0Z1iZfX8pAgK7LBWJhfmsawowkfyMVtfFpatGhbBmUICNFxTkw/zxDsilNXlj6FaqpkQ\nac0amJA6W6AtZNWXAstxD7UgCkUAtIMoFAFoC4q1lBMzea1q1VIduX5fFjPVsEiNncuz2ol/LFXq\nlrtcbkgyJnwqJb5u3GNllTm3eU9IRsrOCV9NxvpSjOF2x2JCHjXNISoV58OZFYsLRRk6E+Zo3qgf\nqbxw6jIfc3IYj8RSTKaXg1ZJqAVRKAKgHUShCEBbUKwzBc87+fXIDWeqYlObWihJRdRVZIaqKJeN\n9at4hvdDLKWFTGmUd7ktTrl0VrAqfSzFFCo7a6N4I0KGUwp/5CIi3MX9uYuCpaTF5kEVKdfqjoks\noWLIUlDh5Y4CVguiUARAO4hCEYBmheMOApgBUAFQNsbsJaJeAF8BMAzgIIBfNMZMNrrGWsS8sBO5\nOub+n0fhRA63zIGvraDJ1ZuyWIWqxYTIHPCQiDKW9604R18oIhyfdRLI7P3qPtKaw2IsyHuMMRcY\nY/a6z58C8JCTHn3IfVYo2gqnM0m/FcA1rnwXrJjDJ0+zPW0LOTqXXVCf3AMjHJLhLOxTqVmGcpWt\nRrnAISqmeLKvZn7eBZdr26SExL4kVWlBRLChVzNzDTajXSto1oIYAP9KRE8R0cdc3TpjzDEAcP8P\nNjxboWhRNGtBrjTGHCWiQQAPEtHLzd7AdaiPAcCWLVsWOFqhWF1oqoMYY466/0eJ6D5YPawRIhoy\nxhwjoiEAow3O/RyAzwHA3r17W37qtxiZ00aSp1VXrApKE0H9Y2tlEqEkFZHK65UtFyIRBiIn7FJZ\npez2N0t0cu6ICdX/CTSMKlljaEa8OkVEHbUygJ8F8AKA+2ElRwGVHlW0KZqxIOsA3OdGwzCAfzDG\nfJuIngBwLxHdAeAQgA+cuWYqFCuDBTuIkxg9v079BIDrzkSjWgWN6NRC35PzQ5RFWErUyGOZIhkX\no0Kh+sa+FmHSuC2S2tn6jh4WizPh+mJwCgv1pCsUAdBgxTOIRpP0iNtEvFwon3TO2+GryYuJt0zl\nraEsdvr1hGUKVaVavQ1MTHbzJL3SKBix5ZdTlgZqQRSKAGgHUSgCoBRrmSDpVqLT6WGNTYkjxFjl\nnRxgKNXhPSFvWq3tEmdkngpfqiLFrBxNS3XxtmsF4fCYN0mvUbM17hBRC6JQBEA7iEIRAKVYZxCN\nwlJiTvit0IS4XdWNYZ7wg9TziUjFEaneHgoxHSOnVBJ2+w8CQAni+/mNqNu2tQa1IApFALSDKBQB\nUIp1GqgX3tFMNG8yvREAUCof9OsqMSE8d0I4+hx18sKsgILqHH8fscJw1Sor0MfKfC0hKo+ure+0\nxwqBuEhRbC8dYWpWce1d4/lSakEUiiCoBVliNKPbFE1aXSvpozAVIfEpxm0PNtU2mmCVdk8EGHo1\npfc53s5NTrcrImQkkbKWg+b5Nmih4pqGWhCFIgDaQRSKACjFWiIsJh8k3mn9EEVxbKpUX/nccxyp\nRssAzusAgHDKUqzKrLyvmGyLlNtUV6/9Xtx3vmK7oHxLKDnaytC3oFAEQDuIQhEApVhLjGb2Rgwn\n3W6zgsZQWSw3Ef9Zqq7eS7HvQmTnwovXBOfk+cJ7EWKRuHjK+UxEuzySCVWyrACatCBE1E1EXyWi\nl4noJSK6goh6iehBInrV/d9zphurUCw3mrUgfwng28aY24koCiAJ4A9htXnvJKJPwWrzrinp0VPV\nyDJuKzQSItJeVQQohuWfxY5h0TRbEBJ+koiTLK2IlFwjvPbRNKfXhtyOt3J/kKiwIHIj9KqSbwDN\n6WJ1ArgawOcBwBhTNMZMwWr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zKZLURVpNpQac0+BDL2pEI+q0CYdpz5+Ib7w1TENcFVoBR5byGNvPEaOXHR5D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\np7h028S4+ldKLjQjTf4Iljf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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import requests\n", "import urllib.request\n", "import matplotlib.pyplot as plt\n", "\n", "styleids = [1456039, 1116823, 1024476, 1595888, 1062662, 1392558, 1377086, 1468462]\n", "\n", "for styleid in styleids:\n", " # download image and read\n", " r = requests.get(url='http://developer.myntra.com/style/' + str(styleid)).json()\n", " imageURL = r['data']['styleImages']['default']['imageURL'].replace('assets.myntassets.com/', 'assets.myntassets.com/w_60/')\n", " urllib.request.urlretrieve(imageURL, 'data/' + str(styleid) + '.jpg')\n", " image = mpimg.imread('data/' + str(styleid) + '.jpg')\n", " \n", " # predict what type is it\n", " prediction = model.predict_classes(np.array([image]), verbose=0)[0]\n", "\n", " # visualize prediction\n", " plt.title('Topwear' if prediction==0 else 'Bottomwear')\n", " plt.imshow(image)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" }, "widgets": { "state": {}, "version": "1.1.2" } }, "nbformat": 4, "nbformat_minor": 2 }