{"cells":[{"metadata":{"_uuid":"b9cd02d161210a286d8c597a662cb595d4611955"},"cell_type":"markdown","source":"# Dogs vs Cats Competition\n\nIn this competition, we'll write an algorithm to classify whether images contain either a dog or a cat.  This is easy for humans, dogs, and cats. Your computer will find it a bit more difficult.\n\n## State of the art\n\nIn 2013, the literature suggested that machine classifiers can score above 82% accuracy on this task. Source: [Machine Learning Attacks Against the Asirra CAPTCHA](http://xenon.stanford.edu/~pgolle/papers/dogcat.pdf)\n\n\nFive years later, using transfer learning we can achieve a much better score with little data and in just a few epochs.\nLet's find out how!\n\nFirst, let's **define** some constants for the folders we'll be working with. Different folders for training images and test images. Another folder for temporary files such as the weights of the model or the numpy vectors with the data. Finally, an output folder for the submission's file.\n\nAnother important define is the **IMG_SHAPE**. Later, I'll explain why these values."},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"4011111eae3578de63c8a1f4d3ce5a373afbd982"},"cell_type":"code","source":"# defines\n\nDIR_IMG_TRAIN = '../input/train'\nDIR_IMG_TEST = '../input/test'\nDIR_TMP_DATA = '.'\nDIR_OUTPUT = '.'\n\nIMG_SHAPE = (150,150,3)","execution_count":6,"outputs":[]},{"metadata":{"_uuid":"0df56019265c2181340398614e1fb2b7404cf177"},"cell_type":"markdown","source":"Some **imports** we'll need."},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"0c970dc66232f3e084be1498da860a9a6cb229f9"},"cell_type":"code","source":"# imports\n\nimport os\nimport matplotlib.pyplot as plt\nfrom scipy.ndimage import imread\nfrom scipy.misc import imsave\nimport numpy as np\nimport time\n\n%matplotlib inline\n","execution_count":7,"outputs":[]},{"metadata":{"_uuid":"e5cae4be03c12255e06ce88e9a3cef132281e3c7"},"cell_type":"markdown","source":"## Exploring the data\n\nNow we've imported some libraries that help us figure out the data in the dataset. Let's explore the data for a moment.\n\nFirst, visualization of a 'cat' example from the train folder using matplotlib and scipy library:"},{"metadata":{"trusted":false,"_uuid":"a4811efbf3657fccab9c1645b26818e482ca4e63"},"cell_type":"code","source":"sample_img_path = os.path.join(DIR_IMG_TRAIN,'cat.5.jpg')\nsample_img = plt.imread(sample_img_path)\nplt.imshow(sample_img)\nprint ('Image have shape: {}'.format(sample_img.shape))","execution_count":9,"outputs":[]},{"metadata":{"_uuid":"4c625593dfe0046e91dbb5975e57fbfe741ffd81"},"cell_type":"markdown","source":"Now, a dog example:"},{"metadata":{"trusted":false,"_uuid":"a9a76bf440d024ddd8de8faa2ba52a9bd9f5e193"},"cell_type":"code","source":"# dog example\nsample_img_path = os.path.join(DIR_IMG_TRAIN,'dog.0.jpg')\nsample_img = plt.imread(sample_img_path)\nplt.imshow(sample_img)\nprint ('Image have shape: {}'.format(sample_img.shape))","execution_count":10,"outputs":[]},{"metadata":{"_uuid":"b165599c32170f975938d5e89db711874c233283"},"cell_type":"markdown","source":"## Useful functions for loading, resizing and normalizing the input images\n\nWe want to have a consistent input, so here we have a function that loads and resizes the image. Note the use of **Keras** built-in functions."},{"metadata":{"trusted":false,"_uuid":"74980a021777b01b1ffe951fa5f2d1074609b461"},"cell_type":"code","source":"# as long as images have different sizes, let's resize!\n\nfrom keras.preprocessing import image\nfrom keras.applications.imagenet_utils import preprocess_input\n\ndef loadAndResizeImage (img, w, h):\n    '''\n    loads the image in 'img' path and returns a PIL image of size (w,h)\n    '''\n    return image.load_img (img, target_size=(w,h))\n\n# dog example resized\ndog_index = 0\nsample_img_path = os.path.join(DIR_IMG_TRAIN,'dog.'+str(dog_index)+'.jpg')\nsample_img = loadAndResizeImage (sample_img_path, IMG_SHAPE[0], IMG_SHAPE[1])\nplt.imshow(sample_img)\nprint ('Dog #'+str(dog_index)+' as image:')","execution_count":11,"outputs":[]},{"metadata":{"_uuid":"f8d6ff9066ee1d993fb485da3d1795b9ad07244e"},"cell_type":"markdown","source":"Some other useful functions to work with the labels and that help loading, resizing and normalizing the data.\n\n_getLabelFromScore_ returns the label for a given score. This is an important decision: \n\n* 1 -> indicates: 'in the picture there's a dog'\n* 0 -> indicates: 'in the picture there's a cat'\n\nSo, the threshold will be at 0.5."},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"ba265a75a30ea5bebf499bbabb1d8bdd28e78e8d"},"cell_type":"code","source":"from PIL import Image\n\ndef getLabel (X):\n    \"\"\"\n    Return the label for an element based on the filename: \n        dog -> 1 \n        cat -> 0\n    \"\"\"\n    if ('dog' in X):\n        return 1\n    else:\n        return 0\n    \ndef getLabels (X):\n    \"\"\"\n    Returns an array that contains the label for each X\n    \"\"\"\n    return np.array([getLabel(X[i]) for i in range(len(X))])\n\ndef getLabelFromScore (score):\n    \"\"\"\n    Returns the label based on the probability\n    if score >= 0.5, return 'dog'\n    else return 'cat'\n    \"\"\"\n    if (score >=0.5):\n        return 'dog'\n    else:\n        return 'cat'\n\ndef normalizedArrayFromImageInPath (image_path, img_shape):\n    \"\"\"\n    returns an the image in 'image' path normalized in an np array\n    \"\"\"\n    img = loadAndResizeImage (image_path, img_shape[0], img_shape[1])\n    return image.img_to_array(img) / 255.\n\ndef loadResizeNormalizeImages (basepath, path_array, img_shape):\n    \"\"\"\n    Loads the images from the path \n    and returns them in an array\n    \"\"\"\n    images = np.empty ((len(path_array), img_shape[0], img_shape[1], img_shape[2]), dtype=np.float32)\n    for i in range (len(path_array)):\n        images[i] = normalizedArrayFromImageInPath (os.path.join(basepath,path_array[i]), img_shape)\n    return images","execution_count":12,"outputs":[]},{"metadata":{"_uuid":"614ac198ad6f804d068c733351f62dc42a734765"},"cell_type":"markdown","source":"## Create training and validation sets\n\nAs stated at the beginning of this notebook, we'll be using a fraction of the total available examples. We'll use just 1000 images for training and 100 for validation.\n\nFor training we pick the first 1.000 images. For validation, we pick the following 100 images.\n\nNote that in the training folders there are **25.000** images in total."},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"ba896d93bb73724e053fce5f99fde9bed98101b0"},"cell_type":"code","source":"import os\nimport numpy as np\n\ntrain_imgs = os.listdir(DIR_IMG_TRAIN)\n\ntrain_examples = 1000\ntrain_x = train_imgs[:train_examples]\ntrain_y = getLabels (train_x)\n\nprint (\"Number of training examples = {}\".format(len(train_x)))\nprint (\"Number of training labels = {}\".format(len(train_y)))\n\nvalidation_examples = 100\nvalidation_x = train_imgs[train_examples:train_examples+validation_examples]\nvalidation_y = getLabels (validation_x)\n\nprint (\"Number of validation examples = {}\".format (len(validation_x)))\nprint (\"Number of validation labels = {}\".format(len(validation_y)))","execution_count":7,"outputs":[]},{"metadata":{"_uuid":"f219091eed28e49b3f19165cb6e54fae9b353456"},"cell_type":"markdown","source":"## Building the numpy arrays containing the data\n\nIn order to accelerate the training and validation steps, we can read the images, prepare them and save them in an numpy object on a file.\n\nBecause this action can take a long time, we save it in a file. In fact, this is not necessary because we are just using 1.100 images, but it is a good practice. You can come back later and skip the previous step. \n\nAfter that, we've reached the first **checkpoint**. "},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"76c052cd4ebbf90ecdb13b8e1abac9d4ab405653"},"cell_type":"code","source":"img_train_x = loadResizeNormalizeImages (DIR_IMG_TRAIN, train_x, IMG_SHAPE)\nnp.save (os.path.join(DIR_TMP_DATA,'train_x'), img_train_x)\nprint (img_train_x.shape)\nprint ('Input X for training saved!')\n\nimg_validation_x = loadResizeNormalizeImages (DIR_IMG_TRAIN, validation_x, IMG_SHAPE)\nnp.save (os.path.join(DIR_TMP_DATA,'validation_x'), img_validation_x)\nprint (img_validation_x.shape)\nprint ('Input X for validation saved!')\n\nprint ('Checkpoint 1')","execution_count":8,"outputs":[]},{"metadata":{"_uuid":"0318161979fa0efae417cc7a0886ca32aa6fcb3d"},"cell_type":"markdown","source":"## Dogs vs Cats proportion in the sets\n\nIt is very important that the sets we've built are not strongly polarized. That means, the percentage of dogs and cats in the set must be around 50%. To check that, we can use the following function based on the given labels:"},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"71b6b6c32292f843fce239d504263161803c0c7c"},"cell_type":"code","source":"def dogsVsCatsProportion (labels):\n    cats = 0\n    dogs = 0\n    for label in labels:\n        if (label == 1):\n            dogs = dogs + 1\n        else:\n            cats = cats + 1\n\n    cats_percent = (cats/len(labels))*100.0\n    dogs_percent = 100.0 - cats_percent\n\n    return dogs_percent, cats_percent","execution_count":9,"outputs":[]},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"bded64a79fcacf5057984a529e08b69322796297"},"cell_type":"code","source":"img_train_x = np.load (os.path.join(DIR_TMP_DATA,'train_x')+'.npy')\nimg_validation_x = np.load (os.path.join(DIR_TMP_DATA,'validation_x')+'.npy')\n\nprint ('Input X train and validation loaded!')\n\ndogs, cats = dogsVsCatsProportion (train_y)\nprint (\"--> Training set: cats represent \"+str(cats)+\"% of the total\")\nprint (\"    Dogs represent \"+str(dogs))\n\ndogs, cats = dogsVsCatsProportion (validation_y)\nprint (\"--> Validation set: cats represent \"+str(cats)+\"% of the total\")\nprint (\"    Dogs represent \"+str(dogs))","execution_count":10,"outputs":[]},{"metadata":{"_uuid":"937b047b79db8757feb7180b5c26d784a5b07c79"},"cell_type":"markdown","source":"as we can see, the proportion is balanced in the training set. It is not so well balanced in the validation set, but this is not so relevant."},{"metadata":{"_uuid":"3da5b2b67a1739fe584f15f21e5159646c1ce263"},"cell_type":"markdown","source":"## Building the model\n\nWe'll use the [inceptionV3](https://keras.io/applications/#inceptionv3) model. This is one of the best models for image classification. We'll remove the top of the model used for classification of the CIFAR dataset, and we'll plug our little model to classify the output of the inception model between dog and cat. \n\nReading the Keras documentation, we find:\n\n_\"It should have exactly 3 inputs channels, and width and height should be no smaller than 139. E.g. (150, 150, 3) would be one valid value.\"_ \n\nSo let's stick to this value. That is where the value for the global constant IMG_SHAPE comes from. \n\nIn our model in top, we'll be using 3 layers with 1024, 256 and 32 units respectively. Between them, we set **dropout** to help the model generalize better. This is specially important in that case because we're using just 1.000 images for training.\n\nIt is also very important to _freeze_ the layers of the inception model. We don't want to train the inception model. We just want that the inception model to extract the features of the images from dogs and cats. Later, our model on top will learn how to separate dogs from cats using those features."},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"81e248e3793f3b67cf774e8e2cb3aca631bfe6b0"},"cell_type":"code","source":"from keras.applications.inception_v3 import InceptionV3\nfrom keras.preprocessing import image\nfrom keras.models import Model\nfrom keras.layers import Dense, GlobalAveragePooling2D, Dropout\nfrom keras import backend as K\n\n# create the base pre-trained model\nbase_model = InceptionV3(weights='imagenet', include_top=False)\n\n# add a global spatial average pooling layer\nx = base_model.output\nx = GlobalAveragePooling2D()(x)\n# let's add a fully-connected layer\nx = Dense(1024, activation='relu')(x)\n# add dropout\nx = Dropout (0.5)(x)\n# one fully connected layer more\nx = Dense(256, activation='relu')(x)\n# add dropout\nx = Dropout (0.5)(x)\n# one fully connected layer more\nx = Dense(32, activation='relu')(x)\n# and a logistic layer --\npredictions = Dense(1, activation='sigmoid')(x)\n\n# this is the model we will train\nmodel = Model(inputs=base_model.input, outputs=predictions)\n\n# first: train only the top layers (which were randomly initialized)\n# i.e. freeze all convolutional InceptionV3 layers\nfor layer in base_model.layers:\n    layer.trainable = False\n\n# compile the model (should be done *after* setting layers to non-trainable)\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])","execution_count":11,"outputs":[]},{"metadata":{"_uuid":"52e4688f229dd81955a52dac18716bcb6634c3b2"},"cell_type":"markdown","source":"## Training\n\nWith the data and the model we've built, we are going to traing the model for only 6 epochs. That means, the model will be trained using the 1.000 images 6 times. At the end of each training, the model will be evaluated against the validation data."},{"metadata":{"trusted":false,"_uuid":"e94221633ef121d31cdecab95cd68e8a91d4b3e8"},"cell_type":"code","source":"# train the model on the new data for a few epochs\nmodel.fit (x=img_train_x, y=train_y, \n           batch_size=16, epochs=6, \n           validation_data=(img_validation_x, validation_y))","execution_count":12,"outputs":[]},{"metadata":{"_uuid":"7208242544ee53226ec9ed1382334cf27143552f"},"cell_type":"markdown","source":"Thanks to the adam optimizer we can see that the model learns very fast. The first validation accuracy is already 88%.\n\nAt the end of this training we can expect an accuracy of around 93% and a log loss of around 0.19.\n\nLet's see how to evaluate this against test data. But before that, we are going to save the model. This is the second checkpoint. If we come back later we can skip most of the steps above and directly load the model from the created file."},{"metadata":{"_uuid":"f4dd75d5e3c7cc6972498dd2a949c6ac78e8b820"},"cell_type":"markdown","source":"## Saving and loading the Keras model\n\nSave and load the Keras model from file."},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"11ee829628ac584df51009cae9bd6eb815215b30"},"cell_type":"code","source":"print ('Saving the model...')\nmodel.save(os.path.join(DIR_TMP_DATA,'model.h5'))\nprint ('Model saved! Filename: {}'.format (os.path.join(DIR_TMP_DATA,'model.h5')))\nprint ('Checkpoint 2')","execution_count":13,"outputs":[]},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"025f204a5372afdf9c0f9cdd5032847848ad7461"},"cell_type":"code","source":"from keras.models import load_model\nmodel = load_model(os.path.join(DIR_TMP_DATA,'model.h5'))\nprint ('Model loaded!')","execution_count":14,"outputs":[]},{"metadata":{"_uuid":"1e263a18d236f8ce25d2e5f5c6c632ff0e49f0e2"},"cell_type":"markdown","source":"## Evaluation\n\nIt is time to evaluate the training. We are going to use 250 images from the training set that **the model didn't see before.** This is important, because it let us know hoe well our model is generalizing. The chosen images are the last 250 available in the training folder.\n\nAs before, we build the numpy array containing the test images and we save it. We also check that the set is balanced, otherwise we could find some fake variance."},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"6a59e08d2faeba99ecb2aa10058b7b77dd79b032"},"cell_type":"code","source":"# Evaluation with test images the model didn't see before\n\ntest_examples = 250\n\nidx_test_example = (-1) * test_examples\n\ntest_x = train_imgs[idx_test_example:]\n\ntest_y = getLabels (test_x)\n\nprint (\"Number of test dev examples = \"+str(len(test_x)))\nprint (\"Number of test dev labels = \"+str(len(test_y)))\n\nprint (\"First element of test dev set \"+test_x[0])\nprint (\"Label of first element of test dev set = \"+str(test_y[0]))","execution_count":15,"outputs":[]},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"4d32a4f15a72be62cbf348a3b0a963474885624b"},"cell_type":"code","source":"img_test_x = loadResizeNormalizeImages (DIR_IMG_TRAIN, test_x, IMG_SHAPE)\nprint (img_test_x.shape)","execution_count":16,"outputs":[]},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"1d2eac62ec77297d2c7b5673b130edfcf48b1139"},"cell_type":"code","source":"print ('Saving test set...')\nnp.save (os.path.join(DIR_TMP_DATA,'test_x'), img_test_x)\nprint ('Input X for test saved!')\nprint ('Checkpoint 3')","execution_count":17,"outputs":[]},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"04b3c0be4f80cb9d5b70d064a1a4672badc4f3cf"},"cell_type":"code","source":"img_test_x = np.load (os.path.join(DIR_TMP_DATA,'test_x')+'.npy')\nprint ('Input X for evaluation loaded!')\n\n# check the proportion dogs vs cats\ndogs, cats = dogsVsCatsProportion (test_y)\n\nprint (\"--> Test set: cats represent \"+str(dogs)+\"% of the total\")\nprint (\"    dogs represent \"+str(cats)+\"%\")","execution_count":18,"outputs":[]},{"metadata":{"_uuid":"555edd8280db6a09e5d1cd99b92600adf6699f35"},"cell_type":"markdown","source":"The proportion looks good enough. We can run the evaluation using the Keras built-in function. It will do a prediction for each image and it will check it against the given label."},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"89583fefaeff53a75b3a709d6edeceaf583d2913"},"cell_type":"code","source":"preds = model.evaluate (x=img_test_x, y=test_y, batch_size=10)\nprint()\nprint (\"Loss = \" + str(preds[0]))\nprint (\"Test Accuracy = \" + str(preds[1]))","execution_count":19,"outputs":[]},{"metadata":{"_uuid":"fbbba649c084b2d4f33f98c4efe39061b7ede00e"},"cell_type":"markdown","source":"These are very good values. It is even improving the validation set. We got 93% of the predictions right and a log loss of 0.16. That means that the model is generalizing well between training and evluation and it is not overfitting the training set. We can be optimistic regarding the final test set."},{"metadata":{"_uuid":"68b84442e1afb653b06c4678566b57ea2d767fc2"},"cell_type":"markdown","source":"## Making manual predictions\n\nIt is nice to see your model in action and take some metrics, for example how long does it take to do one prediction? Is it fast enough to be used in a web service? It is important to consider what is the most important metric for the model. In some cases could be speed, in other accuracy or even the memory footprint of the model. \n\nTo make predictions we define these two useful functions:"},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"6a7b90557d745cb189299dac820ddff0952c24aa"},"cell_type":"code","source":"def predictFromPath (img_path, img_size=(150,150)):\n    img = normalizedArrayFromImageInPath (img_path, img_size)\n    return predictFromImg (img)\n\ndef predictFromImg (img):\n    x = np.expand_dims(img, axis=0)\n    return model.predict(x)[0][0]","execution_count":20,"outputs":[]},{"metadata":{"trusted":false,"_uuid":"a53f1702c05638a51ae007924345cf4ee72d7f3d"},"cell_type":"code","source":"img_path = DIR_IMG_TEST+'4.jpg'\ndisplay_img = image.load_img(img_path)\nstart = time.time()\nscore = predictFromPath (img_path)\nend = time.time()\nprint(\"Prediction took {:.3f} seconds\".format (end - start))\nprint(\"It's a {}! (with a score of {}) 0 -> cat / 1 -> dog\".format (getLabelFromScore (score), score))\nplt.imshow(display_img)","execution_count":21,"outputs":[]},{"metadata":{"_uuid":"2171875e0d271b18c9a75cc079f26147e0549255"},"cell_type":"markdown","source":"Yes, it is a dog, not bad... Let's see few predictions at once:"},{"metadata":{"trusted":false,"_uuid":"032ac80e5c2ced9e10b482a566d168b8f8973d62"},"cell_type":"code","source":"# last test\n\nfig=plt.figure()\n\nplot_test_paths = os.listdir(DIR_IMG_TEST)[50:62]\n\nfor num,plot_test_path in enumerate(plot_test_paths):\n\n    y = fig.add_subplot(3,4,num+1)\n    orig = image.load_img (DIR_IMG_TEST+plot_test_path)\n\n    prediction = predictFromPath (DIR_IMG_TEST+plot_test_path)\n    str_label = getLabelFromScore (prediction)\n        \n    y.imshow(orig)\n    plt.title(str_label)\n    y.axes.get_xaxis().set_visible(False)\n    y.axes.get_yaxis().set_visible(False)\nplt.show()","execution_count":22,"outputs":[]},{"metadata":{"_uuid":"7f9696e51a466723d4b3c220af42db17ed17d7d3"},"cell_type":"markdown","source":"All of them look correct! Let's do a submission."},{"metadata":{"_uuid":"f5159ccb94c096e61bad4b8a05b46f4b54d13eee"},"cell_type":"markdown","source":"## Submission\n\nThe submission expects a file with this format:\n\n|id|label|\n|--|:----|\n|1|0.5|\n|2|0.5|\n|3|0.5|\n|4|0.5|\n\n_id_ is the test id of the picture (contained in the filename). As _label_ the probability it is a dog.\n\nWe prepare the data as before creating a numpy object and saving it on disk for later."},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"3764a37c09b133176329dab23972b5aa1ff006d0"},"cell_type":"code","source":"# prepare data for submission\n\nsubmission_imgs = os.listdir(DIR_IMG_TEST)\nsubmission_x = submission_imgs[:]\nprint (\"Number of submission examples = {}\".format(len(submission_x)))\n\nsubmission_imgs = sorted(submission_imgs, key=lambda x: int(x[:x.index('.')]))","execution_count":23,"outputs":[]},{"metadata":{"_uuid":"4f3d4cd5adf7a1f27dfeba0e9f0dfccef9fb8f0a"},"cell_type":"markdown","source":"12.500 test images in total. Let's do the predictions and export it to a csv."},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"c5770ffdb93a84d63977a469734d48b3126d22a3"},"cell_type":"code","source":"import csv\nfrom tqdm import tqdm\n\nwith open(os.path.join(DIR_OUTPUT,'submission.csv'), 'w') as csvfile:\n    filewriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n    filewriter.writerow(['id', 'label'])\n    for elem in tqdm (submission_imgs):\n        prediction = predictFromPath (DIR_IMG_TEST+elem)\n        filewriter.writerow([elem[:elem.index('.')], \"{:.6f}\".format(prediction)])","execution_count":24,"outputs":[]},{"metadata":{"_uuid":"764f583e1da3c457b5c1ebaf54f118d043ed5614"},"cell_type":"markdown","source":"## Result\n\nWe got a log loss of **0.216**! This is not bad at all considering the little data we used. We even didn't use data augmentation! That would be the natural next step. There's some overfitting to the training set because we got **0.16** log loss in the evaluation set.\n\nAnyway, we can see the huge difference with respect of the state of the art in 2013.\n"},{"metadata":{"_uuid":"11000fad3dcdbe9030c5645d3465722fe0fdc700"},"cell_type":"markdown","source":"![Screen%20Shot%202018-03-18%20at%2016.27.19.png](attachment:Screen%20Shot%202018-03-18%20at%2016.27.19.png)","attachments":{"Screen%20Shot%202018-03-18%20at%2016.27.19.png":{"image/png":"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"}}},{"metadata":{"_uuid":"cb0e511d10733fc3dfb83b9637aff5f0db8aeb14"},"cell_type":"markdown","source":"The result is calculated with the log loss. The formula for the log loss is the following:"},{"metadata":{"_uuid":"ed3bd9766766515d13714302c88fa638cb809e11"},"cell_type":"markdown","source":"![Screen%20Shot%202018-03-18%20at%2017.47.23.png](attachment:Screen%20Shot%202018-03-18%20at%2017.47.23.png)","attachments":{"Screen%20Shot%202018-03-18%20at%2017.47.23.png":{"image/png":"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"}}},{"metadata":{"_uuid":"826202e8197b21e95980a049b3c57171b8c38e4d"},"cell_type":"markdown","source":"Its 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"}}},{"metadata":{"_uuid":"d68a545c786813c9d216172e2b787d58a6034ada"},"cell_type":"markdown","source":"## Conclusion\n\nWe could easily beat the state of the art of 2013. The modern deep learning techniques are very powerful. Even with restricted data and training for a few epochs, the result is notably better. We didn't even need a GPU.\n\nAs we saw, the model can't generalize well enough and misses around 7% of the pictures it predicts. For that reason and considering the data restriction we imposed to ourselves, **data augmentation** could be an interesting option to achieve a better result. Specially interesting is the built-in Keras functionality called [ImageDataGenerator](https://keras.io/preprocessing/image/). Using this feature and training the model for a few more epochs, we could improve the results above."},{"metadata":{"collapsed":true,"trusted":false,"_uuid":"aa78b5d92ff32ae4a438e63a20625255f957307f"},"cell_type":"code","source":"model.summary()","execution_count":25,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}