{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Contributors\n\n* Borbolla Alejandro 195004\n* Dubois Hugo 195347","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.layers import (Conv2D, MaxPooling2D,\n                                     Dense, Dropout, Flatten, \n                                     Activation, BatchNormalization)\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\nfrom tensorflow.keras.optimizers import Adam, RMSprop","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Introduction\n\nIn this project we'll use a neural network to identify metastatic tissue in histopathologic scans of lymph node sections. The main purpose is to create an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans.\n\nThe dataset is taken from [Histopathologic cancer detection](https://www.kaggle.com/competitions/histopathologic-cancer-detection).\n\n\n<img src=\"attachment:8fd790df-408f-4e56-81be-dad490e0e92c.png\" \n     width=\"20%\"\n     alt=\"8fd790df-408f-4e56-81be-dad490e0e92c\" \n     style=\"display: block; margin: 0 auto\" 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"}}},{"cell_type":"markdown","source":"# Constant","metadata":{}},{"cell_type":"code","source":"BASE_PATH = '/kaggle/input/histopathologic-cancer-detection'\nBASE_TRAIN_PATH = f'{BASE_PATH}/train'\nBASE_TEST_PATH = f'{BASE_PATH}/test'\nBASE_TRAIN_LABELS_PATH = '/kaggle/input/histo-dataset/histo_dataset/train_labels.csv'\nBASE_PATH_HISTO = f'/kaggle/input/histo-dataset/histo_dataset'\nBASE_TEST_TEST_PATH = f'{BASE_PATH_HISTO}/test'\nBASE_TEST_TRAIN_10000_PATH = f'{BASE_PATH_HISTO}/10000/train'\nBASE_TEST_TRAIN_50000_PATH = f'{BASE_PATH_HISTO}/50000/train'\nBASE_TEST_TRAIN_ALL_PATH = f'{BASE_PATH_HISTO}/all/train'\nBASE_TEST_VALID_10000_PATH = f'{BASE_PATH_HISTO}/10000/valid'\nBASE_TEST_VALID_50000_PATH = f'{BASE_PATH_HISTO}/50000/valid'\nBASE_TEST_VALID_ALL_PATH = f'{BASE_PATH_HISTO}/all/valid'\n\nIMAGE_SIZE = 96\nTEST_BATCH_SIZE = 32\nMODEL_FILE = 'model.h5'","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:15:15.101262Z","iopub.execute_input":"2023-05-16T08:15:15.102350Z","iopub.status.idle":"2023-05-16T08:15:15.108969Z","shell.execute_reply.started":"2023-05-16T08:15:15.102323Z","shell.execute_reply":"2023-05-16T08:15:15.107917Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Load target\n\nWe load the `train_labels.csv` under the `BASE_TRAIN_LABELS_PATH` denomination.\n\nThen we convert our `.csv` file into a DataFrame by using the `pandas` function `pd.read_csv`.\n\nFrom now, we'll use the `id` of our images as the index to discard the unrelevant information of the previous index","metadata":{}},{"cell_type":"code","source":"train_labels_df = pd.read_csv(BASE_TRAIN_LABELS_PATH)\ntrain_labels_df.set_index('id', inplace=True)\ntrain_labels_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:15:15.115766Z","iopub.execute_input":"2023-05-16T08:15:15.116444Z","iopub.status.idle":"2023-05-16T08:15:15.731228Z","shell.execute_reply.started":"2023-05-16T08:15:15.116419Z","shell.execute_reply":"2023-05-16T08:15:15.728543Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# File tree\n\nFrom the original dataset, we created several subsets so that we can have three versions with several numbers of images:\n *  10000 images at \"/kaggle/input/histo-dataset/histo_dataset/10000\"\n *  50000 images at \"/kaggle/input/histo-dataset/histo_dataset/50000\"\n *  The entire dataset at \"/kaggle/input/histo-dataset/histo_dataset/all\"\n\nFor each subset, we created a `training` and `test` set, where the training set is 80% of the subset.\n\nIn order to be able to load the images from the dataset, we must categorise our data when each folder mean one category. For each training and test set of our subset, we defined two categories:\n * cancer\n * non_cancer\n\nThis categorisation will be useful for our training phase.\n\n```\n.\n└── histo-dataset/\n    ├── 10000/\n    │   ├── train/\n    │   │   ├── cancer\n    │   │   └── non_cancer\n    │   └── valid/\n    │       ├── cancer\n    │       └── non_cancer\n    ├── 50000/\n    │   ├── train/\n    │   │   ├── cancer\n    │   │   └── non_cancer\n    │   └── valid/\n    │       ├── cancer\n    │       └── non_cancer\n    ├── all/\n    │   ├── train/\n    │   │   ├── cancer\n    │   │   └── non_cancer\n    │   └── valid/\n    │       ├── cancer\n    │       └── non_cancer\n    ├── test/\n    │   └── test_inner\n    ├── sample_submission.csv\n    └── train_labels.csv\n```\n\n# Load images from database\n\nKeras has this `ImageDataGenerator` class which allows the users to perform image augmentation on the fly in a very easy way.\n\nThe `ImageDataGenerator` class has a method `flow_from_directory()` to read images from folders.\n\nBut just before we use the `rescale` argument (rescale=1.0/255). This will convert the pixels in the range [0,255] to [0,1]\n\nThis process is also called Normalizing the input. Scaling every image to the same range [0,1] will make images contributes more evenly to the total loss.\n\nWithout scaling, the high pixel range images will have a large say to determine how to update the weights. For example, black/white cat image could be in higher pixel range than pure black cat image, but it just doesn't mean black/white cat image is more important for training.\n\nAlso, the neural network has a higher chance of converging as it makes the coefficients in the range of [0,1] as opposed to [0,255], and so helps the model process input faster.\n\nDownload the train dataset and test dataset, extract them into 2 different folders named as “train” and “test”. \nThe train folder should contain ‘n’ folders each containing images of the respective classes, in our case it will be `cancer` and `non_cancer`.\n\nCreate a validation set, often you have to manually create a validation data by sampling images from the train folder (you can either sample randomly or in the order your problem needs the data to be fed) and moving them to a new folder named `valid`.\n\n * `train_gen`: training set\n * `val_gen`: validation set\n \nIn the `flow_from_directory` function, we set the `classes` parameters to specify that the `cancer` category has `1` as label, and `non_cancer` has `0`.","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n\n# Put the value between 0. and 1.\ndatagen = ImageDataGenerator(rescale=1.0/255)\n\n# Train dataset\ntrain_gen = datagen.flow_from_directory(BASE_TEST_TRAIN_ALL_PATH,\n                                        target_size=(IMAGE_SIZE,IMAGE_SIZE),\n                                        batch_size=TEST_BATCH_SIZE,\n                                        class_mode='categorical',\n                                        classes= {'non_cancer': 0, 'cancer': 1},)\n\n# Valid dataset\nvalid_gen = datagen.flow_from_directory(BASE_TEST_VALID_ALL_PATH,\n                                        target_size=(IMAGE_SIZE,IMAGE_SIZE),\n                                        batch_size=TEST_BATCH_SIZE,\n                                        class_mode='categorical',\n                                        classes={'non_cancer': 0, 'cancer': 1},)","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:15:15.735815Z","iopub.execute_input":"2023-05-16T08:15:15.736320Z","iopub.status.idle":"2023-05-16T08:19:35.209428Z","shell.execute_reply.started":"2023-05-16T08:15:15.736281Z","shell.execute_reply":"2023-05-16T08:19:35.208217Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The function `flow_from_directory` detect automatically two classes from the file tree:\n * `non_cancer`\n * `cancer`","metadata":{}},{"cell_type":"code","source":"print(f'Train classes: {train_gen.class_indices}')\nprint(f'Valid classes: {valid_gen.class_indices}')","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:19:35.210807Z","iopub.execute_input":"2023-05-16T08:19:35.216437Z","iopub.status.idle":"2023-05-16T08:19:35.225345Z","shell.execute_reply.started":"2023-05-16T08:19:35.216394Z","shell.execute_reply":"2023-05-16T08:19:35.224195Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Dataset balance\n\nIn order to train our model, we need to have a balanced dataset.\n\nIn our case, the dataset is provided with two classes (cancer or non_cancer). \n\nTo have a balanced dataset, each class must contain the same amount of elements.\n\nLet's now have a look on our data : ","metadata":{}},{"cell_type":"code","source":"# Merge the train and validation test (report purpose)\ntrain_df = train_labels_df\n\n# Count the total rows\ntotal_rows = len(train_df)\n\n# Count occurrence of each values\noccurrences = train_df['label'].value_counts(normalize=True).mul(100)\n\n# Percentages of cancer and non cancer\ncancer_percentage = occurrences[1]\nnon_cancer_percentage = occurrences[0]\n\nprint(f'Cancer: {cancer_percentage}%')\nprint(f'Non-cancer: {non_cancer_percentage}')","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:19:35.231666Z","iopub.execute_input":"2023-05-16T08:19:35.231993Z","iopub.status.idle":"2023-05-16T08:19:35.252201Z","shell.execute_reply.started":"2023-05-16T08:19:35.231962Z","shell.execute_reply":"2023-05-16T08:19:35.250955Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"labels = ['Non-cancer', 'Cancer']\ndatas = [non_cancer_percentage, cancer_percentage]\n\nplt.pie(datas, labels=labels, autopct='%1.1f%%')","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:19:35.255822Z","iopub.execute_input":"2023-05-16T08:19:35.258322Z","iopub.status.idle":"2023-05-16T08:19:35.489829Z","shell.execute_reply.started":"2023-05-16T08:19:35.258286Z","shell.execute_reply":"2023-05-16T08:19:35.488641Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"As we can see in the chart above, the database is well balanced between non-cancer and cancer images. Given that, we don't have to do data augmentation over the dataset and we can keep it as it is.","metadata":{}},{"cell_type":"markdown","source":"# Model\n\n## Convolutional Neural Network (CNN)\n\nThe goal of a CNN is to take an input image and assign importance to various aspectc in it, and be able to differentiate one from the other.\n\nIn a CNN, we have two parts:\n\n * Feature learning: It is where the image processing is done. This parts brings to light the features of the images.\n * Classification: This part will classify our image given the features found in the Feature Learning part.\n\n<img \n     src=\"https://d33wubrfki0l68.cloudfront.net/a7664cf19de33b2c71a482629f27a0d70f715b77/6949d/images/blog/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way.jpg\" \n     width=\"70%\" \n     style=\"display: block; margin: 0 auto\" />\n\n## Dropout\n\nSometimes, it happens that the model knows too much the training set, this is called **overfitting**. The solution of this problem is to use a **Dropout layer**\n\nA Dropout Layer is just the fact to disable some neurons in a layer\n\n<img src=\"https://editor.analyticsvidhya.com/uploads/112801.gif\"  \n     width=\"40%\"\n     style=\"display: block; margin: 0 auto\" />\n\n## Flatten\n\nAfter the convolution and the max pooling layers, we have a pooled feature map. We are going to put it into a dense neural network. To be able to do that, we must flatten the pooled feature map and use the result as an input for a fully connected neural network.\n\n<img src=\"https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/73_blog_image_1.png\" \n     width=\"40%\" \n     style=\"display: block; margin: 0 auto\" />\n\n## Batch Normalization\n\nAfter every max pooling layer, we use a Batch Normalization to normalize the activations within a mini-batch. \n\nThis step helps improving the stability and the convergence of the model.\n     \n# Activation function\n\nAn Activation Function decides whether a neuron should be activated or not. This means that it will decide whether the neuron’s input to the network is important or not in the process of prediction using simpler mathematical operations. \nIt is used to determine the output of neural network like yes or no. It generally maps the resulting values in between 0 to 1.\nThere are two types of activation functions : `Linear` and `Non-Linear`.\n\nThe `Linear` activation function is typically a function like : $$f(x) = x$$\n\nThe `Non-Linear` activation function is typically a `step function` :\n\n$$f(x) = \n\\begin{cases}\n    1 & \\text{if } x > 0\\\\\n    0 & \\text{if } x < 0\n\\end{cases}$$ \n<img src=\"https://www.intmath.com/laplace-transformation/svg/svgphp-unit-step-functions-definition-1a-s0.svg\"  \n     width=\"30%\"\n     style=\"display: block; margin: 0 auto\" />\n     \nOr a a ReLu function :\n\n$$f(x) = max(0,x)$$\n\n<img src=\"https://sebastianraschka.com/images/faq/relu-derivative/relu_3.png\"  \n     width=\"30%\"\n     style=\"display: block; margin: 0 auto\" />\n\n## Softmax\n\nThe softmax function is a function that turns a vector of K real values into a vector of K real values that sum to 1.\nThe input values can be positive, negative, zero, or greater than one, but the softmax transforms them into values between 0 and 1, so that they can be interpreted as probabilities. \n\n<img src=\"https://vitalflux.com/wp-content/uploads/2020/10/Softmax-Function.png\"  \n     width=\"50%\"\n     style=\"display: block; margin: 0 auto\" />\n\n## Our model\n\nIn each convolution step, we added a padding to our images, so that we reduce the information loss.\n\n<img src=\"attachment:fa45d213-407f-4ea8-a40f-d5e2acd6e223.png\" \n     width=\"60%\"\n     style=\"display: block; margin: 0 auto\" />","metadata":{},"attachments":{"fa45d213-407f-4ea8-a40f-d5e2acd6e223.png":{"image/png":"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"}}},{"cell_type":"code","source":"num_train_samples = len(train_gen)\nnum_val_samples = len(valid_gen)\n\ntrain_steps = np.ceil(num_train_samples / TEST_BATCH_SIZE)\nval_steps = np.ceil(num_val_samples / TEST_BATCH_SIZE)","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:19:35.491446Z","iopub.execute_input":"2023-05-16T08:19:35.491794Z","iopub.status.idle":"2023-05-16T08:19:35.498331Z","shell.execute_reply.started":"2023-05-16T08:19:35.491762Z","shell.execute_reply":"2023-05-16T08:19:35.496836Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"dropout_factor = 0.3\n\ntf.keras.backend.clear_session()\n\nmodel = Sequential()\n\nmodel.add(Conv2D(32, (3, 3), padding=\"same\", activation='relu', input_shape = (IMAGE_SIZE, IMAGE_SIZE, 3))) # (96, 96, 32)\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2, 2))) # (48, 48, 32)\nmodel.add(Dropout(dropout_factor))\n\nmodel.add(Conv2D(64, (3, 3), padding=\"same\", activation='relu')) # (48, 48, 64)\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2, 2))) # (24, 24, 64)\nmodel.add(Dropout(dropout_factor))\n\nmodel.add(Conv2D(128, (3, 3), padding=\"same\", activation='relu')) # (24, 24 128)\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2, 2))) # (12, 12, 128)\nmodel.add(Dropout(dropout_factor))\n\nmodel.add(Conv2D(128, (3, 3), padding=\"same\", activation='relu')) # (12, 12, 128)\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(3, 3))) # (4, 4, 128)\nmodel.add(Dropout(dropout_factor))\n\nmodel.add(Flatten()) # (2048, 1)\nmodel.add(Dense(2048, activation = \"relu\")) # (2048, 1)\nmodel.add(Dropout(dropout_factor))\nmodel.add(Dense(2, activation = \"softmax\")) # (2, 1)\n\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:19:35.500369Z","iopub.execute_input":"2023-05-16T08:19:35.500726Z","iopub.status.idle":"2023-05-16T08:19:39.740694Z","shell.execute_reply.started":"2023-05-16T08:19:35.500692Z","shell.execute_reply":"2023-05-16T08:19:39.739975Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Compiler\n\nThe `compile()` function from Keras is used to configure the learning. We set the following parameters:\n * optimizer: Instance of the optimizer to use, in our case we use Adam\n * loss: The loss function to use\n * metrics: List of metrics to be evaluated. We use the accuracy to know how far are we from the target.\n\nAs optimizer, we try `Adam`, because it is an optimization of a stochastic gradient descent. But then we switched to `RMSprop` as it seems to give better results.\n\nFor the loss function, we use Binary Cross Entropy because it works well with binary classification problems.","metadata":{}},{"cell_type":"code","source":"model.compile(RMSprop(lr=0.00001), loss='binary_crossentropy', metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:19:39.741743Z","iopub.execute_input":"2023-05-16T08:19:39.742278Z","iopub.status.idle":"2023-05-16T08:19:39.775803Z","shell.execute_reply.started":"2023-05-16T08:19:39.742245Z","shell.execute_reply":"2023-05-16T08:19:39.774829Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Fitting\n\nNow, we will train our model. For that, we use the `fit()` function of our model.\n\nWe settled 3 callbacks:\n * EarlyStopping\n * ReduceLROnPlateau\n * ModelCheckpoint\n\n## Early stopping\n\nEarly stopping, as said in the name, is used to stop the learning earlier than expected. It has been configured so that the training is stopped when it obtains results lower than the previous one during 5 epochs. \n\nIn other words, we stop the learning of our model when it starts to overfit.\n\nWe also set the `epoch` at an unreachable value, so that the training will be stopped earlier and we won't have an underfitted model.\n\n<img src=\"https://miro.medium.com/v2/resize:fit:1400/1*iAK5uMoOlX1gZu-cSh1nZw.png\"\n     width=\"40%\"\n     style=\"display: block; margin: 0 auto\" />\n\n## Reduce learning rate\n\nThe learning rate is a hyper-parameters that regulate the weights of a neural network.\n\nIt can't be too slow, because the weight will converge too slowly to the optimal values and will increase the learning time. But it can't be too high, because if you make too large step, you could never reach the optimal value.\n\nGiven that, we can see that it is hard to set this value. That's why we use `ReduceLROnPlateau`. This callback will change the learning rate regarding the accuracy of our training. If the accuracy is lower than the previous during a certain amount of epochs, called `patience`, then the learning rate is decreased by a factor.\n\n<img src=\"https://hasty.ai/media/pages/docs/mp-wiki/scheduler/reducelronplateau/dcd1eb191f-1684142773/snimok-ekrana-1382.webp\"\n     width=\"40%\"\n     style=\"display: block; margin: 0 auto\" />\n\n## Model checkpoint\n\nAs said before, we stop the learning of our model when it starts to overfit. Given that condition, the models is already deteriorated.\n\nTo take the best version of our model, we use `ModelCheckpoint`. It has been configured so that it saved the weight of our best model in the `model.h5` file.","metadata":{}},{"cell_type":"code","source":"# Configure the optimization of the learning rate regarding the validation accuracy\nreduce_lr = ReduceLROnPlateau(monitor='val_accuracy', factor=0.7, patience=2, \n                              verbose=1, mode='max', min_lr=0.0000001)\n\n# Stop the learning earlier if the loss is lower than before during 5 epochs\nearly_stopping = EarlyStopping(monitor='loss', patience=5)\n\n# Write the best model on model.h5\nmodel_checkpoint = ModelCheckpoint(MODEL_FILE, monitor='val_accuracy', \n                                   save_best_only=True, mode='max')\n\n\n# Insert callbacks in list\ncallbacks_list = [early_stopping, reduce_lr, model_checkpoint]","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:19:39.776914Z","iopub.execute_input":"2023-05-16T08:19:39.777998Z","iopub.status.idle":"2023-05-16T08:19:39.785195Z","shell.execute_reply.started":"2023-05-16T08:19:39.777964Z","shell.execute_reply":"2023-05-16T08:19:39.784298Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Delete the model.h and my_submission files\n!rm /kaggle/working/model.h5\n!rm /kaggle/working/my_submission.csv\n\n# Train the model\nhistory = model.fit(train_gen,\n                    steps_per_epoch=train_steps, \n                    validation_data=valid_gen,\n                    validation_steps=val_steps,\n                    epochs=50, verbose=1,\n                    callbacks=callbacks_list)","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:19:39.789598Z","iopub.execute_input":"2023-05-16T08:19:39.790490Z","iopub.status.idle":"2023-05-16T08:23:31.021715Z","shell.execute_reply.started":"2023-05-16T08:19:39.790456Z","shell.execute_reply":"2023-05-16T08:23:31.020054Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"In the plots below, we display two plots:\n\n * Loss of the training and validation set at each epoch\n * Accuracy of the training and validation set at each epoch","metadata":{}},{"cell_type":"code","source":"# Take every parameters registered during the training phase\nacc = history.history['accuracy']\nval_acc = history.history['val_accuracy']\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\n# Generate x axis\nepochs = range(1, len(acc) + 1)\n\n# Loss plot\nplt.plot(epochs, loss, 'g', label='Training loss')\nplt.plot(epochs, val_loss, 'b', label='Validation loss')\nplt.title('Training and validation loss')\nplt.xlabel(\"Epochs\")\nplt.ylabel(\"Loss\")\nplt.legend()\nplt.figure()\n\n# Accuracy plot\nplt.plot(epochs, acc, 'g', label='Training acc')\nplt.plot(epochs, val_acc, 'b', label='Validation acc')\nplt.title('Training and validation accuracy')\nplt.legend()\nplt.figure()","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:23:31.023501Z","iopub.status.idle":"2023-05-16T08:23:31.024422Z","shell.execute_reply.started":"2023-05-16T08:23:31.024119Z","shell.execute_reply":"2023-05-16T08:23:31.024164Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Interpretation\n\nAs you can see on our graph, the validation loss is most of the time superior to our training loss. \n\nBut it tends to decrease through the epochs, in such a way that the difference between the training loss and the validation loss is decreasing also. \n\nAt the end, we can see that the validation loss starts to increase again and the difference between the training and the validation loss starts to increase again. \n\nThis might be the sign that our model is `Overfitting`. But thank to `ModelCheckpoint`, we memorise the best model.","metadata":{}},{"cell_type":"markdown","source":"# Learning graph\n\n## Training and validation loss\n\nThe training loss is a metric used to assess how a deep learning model fits the training data. \n\nThe training loss is calculated by taking the sum of errors for each example in the training set. It's also important to notice that the training loss is measured after each `Batch`.\n\nOn the other hand, validation loss is a metric used to assess the performance of a deep learning model on the validation set. \nSimilarly to the training loss, the validation loss is measured after each `Epoch`. \n\n## Overfitting : \n\n<img src=\"attachment:8b789d00-7afa-48bf-b0d6-1ec2467d2441.png\"\n     width=\"40%\"\n     style=\"display: block; margin: 0 auto\" />\n     \n## Underfitting : \n\n<img src=\"attachment:c789973e-fc3b-4700-80f0-65263a9cc04a.png\"\n     width=\"40%\"\n     style=\"display: block; margin: 0 auto\" />\n     \n## Good fitting : \n\n<img src=\"attachment:e17fccfc-3369-4609-8137-682f5407df22.png\"\n     width=\"40%\"\n     style=\"display: block; margin: 0 auto\" 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"}}},{"cell_type":"markdown","source":"# The weights and the validation dataset\n\nThe `validation` dataset is the sample of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters\n\nNow that our model has been trained with the `Train` dataset and that his hyperparameters have been fixed. We can now load our model from `model.h5` and evaluate it using the `validation` dataset.","metadata":{}},{"cell_type":"code","source":"# Define steps\nsteps = valid_gen.samples / TEST_BATCH_SIZE\n\n# Load best model\nmodel.load_weights(MODEL_FILE)\n\n# Check the accuracy of our model over the validation set\nval_loss, val_acc = model.evaluate(valid_gen, steps=steps, verbose=0)\n\nprint(f'Accuracy: {val_acc * 100.0}%')\nprint(f'Loss: {val_loss * 100.0}%')","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:23:31.025967Z","iopub.status.idle":"2023-05-16T08:23:31.026809Z","shell.execute_reply.started":"2023-05-16T08:23:31.026537Z","shell.execute_reply":"2023-05-16T08:23:31.026562Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Load Test dataset\n\nThe `test` dataset is the sample of data is used to make our predictions on a dataset with unknown labels. We will use it for our submission.\n\nWe defined `TEST_BATCH_SIZE` equals to 1, we load the images one by one.\n\nThe `test_gen` contains now the images of the `test` dataset.","metadata":{}},{"cell_type":"code","source":"# Predict image one by one\nTEST_BATCH_SIZE = 1\n\n# Load images\ntest_gen = datagen.flow_from_directory(BASE_TEST_TEST_PATH,\n                                        target_size=(IMAGE_SIZE,IMAGE_SIZE),\n                                        batch_size=TEST_BATCH_SIZE,\n                                        class_mode='categorical',\n                                        shuffle=False)","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:23:31.028302Z","iopub.status.idle":"2023-05-16T08:23:31.029089Z","shell.execute_reply.started":"2023-05-16T08:23:31.028845Z","shell.execute_reply":"2023-05-16T08:23:31.028869Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Prediction\n\nThe `model.predict()` generates output predictions for the input samples. \n\nThe computation is done in batches. \n\nThis method is designed for batch processing of large numbers of inputs. It is not intended for use inside of loops that iterate over your data and process small numbers of inputs at a time.\n\nOnce the prediction is done, we convert the `predictions` numpy array into a DataFram with two columns, `non-cancer` and `cancer`.\n\nFor each index of the DataFrame, the sum of the two columns `non-cancer` and `cancer` is equal to one.\n\nThe values in these two columns represent the presence of a metastatic cancer in a given image. \n\nFor example : \n\n* If a value is superior to 0.5 and this value is in the `non-cancer` column, then the odds to not get a cancer are high.\n* On the other hand, if a value is superior to 0.5 and this value is in the `cancer` column, then the odds to get a metastatic cancer are high.\n\nWe then switched these `float` values into binary values `0` or `1` depending if the value is superior or inferior to 0.5.","metadata":{}},{"cell_type":"code","source":"num_test_images = test_gen.samples / TEST_BATCH_SIZE\n\nmodel.load_weights('model.h5')\npredictions = model.predict(test_gen, steps=num_test_images, verbose=1)","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:23:31.030507Z","iopub.status.idle":"2023-05-16T08:23:31.031335Z","shell.execute_reply.started":"2023-05-16T08:23:31.031044Z","shell.execute_reply":"2023-05-16T08:23:31.031069Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_preds = pd.DataFrame(predictions, columns=['non-cancer', 'cancer'])\n\ndf_preds.head()","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:23:31.032744Z","iopub.status.idle":"2023-05-16T08:23:31.033553Z","shell.execute_reply.started":"2023-05-16T08:23:31.033278Z","shell.execute_reply":"2023-05-16T08:23:31.033302Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_preds_label = pd.DataFrame()\ndf_preds_label['label'] = np.where(df_preds['non-cancer'] >= 0.5, 0, 1)\n\ndf_preds_label.head()","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:23:31.034959Z","iopub.status.idle":"2023-05-16T08:23:31.035790Z","shell.execute_reply.started":"2023-05-16T08:23:31.035532Z","shell.execute_reply":"2023-05-16T08:23:31.035557Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"preds_df = df_preds_label\n\n# Count the total rows\ntotal_rows = len(preds_df)\n\n# Count occurrence of each values\noccurrences = preds_df.value_counts(normalize=True).mul(100)\n\n# Percentages of cancer and non cancer\ncancer_percentage = occurrences[1]\nnon_cancer_percentage = occurrences[0]","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:23:31.037192Z","iopub.status.idle":"2023-05-16T08:23:31.037947Z","shell.execute_reply.started":"2023-05-16T08:23:31.037704Z","shell.execute_reply":"2023-05-16T08:23:31.037728Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"labels = ['Non-cancer', 'Cancer']\ndatas = [non_cancer_percentage, cancer_percentage]\n\nplt.pie(datas, labels=labels, autopct='%1.1f%%')\nplt.title('Model prediction of the percentage of images into the Test dataset\\n'+' that contains a metastatic cancer')","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:23:31.039322Z","iopub.status.idle":"2023-05-16T08:23:31.040151Z","shell.execute_reply.started":"2023-05-16T08:23:31.039868Z","shell.execute_reply":"2023-05-16T08:23:31.039893Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Submission\n\nOn associe cancer label avec ID du fichier...","metadata":{}},{"cell_type":"code","source":"import re\n\n# Create empty dataframe\ndf_submission = pd.DataFrame({\n    'id': pd.Series(dtype='str'), \n    'label': pd.Series(dtype='int')\n}).set_index('id')\n\n# Loop through the prediction DataFrame\nfor i in range(0, len(predictions)):\n    # Extract ID from filename\n    re_filename = re.search(r'(\\w+).tiff?$', test_gen.filenames[i])\n    file_id = re_filename.group(1)\n    \n    # Extract label\n    label = df_preds_label.loc[[i], 'label'].sum()\n    \n    # Create new row in the submission dataset\n    df_submission.loc[file_id] = [label]\n    \ndf_submission.head()","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:23:31.041565Z","iopub.status.idle":"2023-05-16T08:23:31.042341Z","shell.execute_reply.started":"2023-05-16T08:23:31.042071Z","shell.execute_reply":"2023-05-16T08:23:31.042094Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_submission.to_csv('/kaggle/working/my_submission.csv')","metadata":{"execution":{"iopub.status.busy":"2023-05-16T08:23:31.043798Z","iopub.status.idle":"2023-05-16T08:23:31.044611Z","shell.execute_reply.started":"2023-05-16T08:23:31.044369Z","shell.execute_reply":"2023-05-16T08:23:31.044393Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Result\n \nAfter submissions in the competition, here is our result:\n\n![best-result.png](attachment:51699780-9d92-4220-a57a-7a73db346182.png)","metadata":{},"attachments":{"51699780-9d92-4220-a57a-7a73db346182.png":{"image/png":"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"}}}]}