{"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":"## ANN using MNIST: ","metadata":{}},{"cell_type":"markdown","source":"\n\nIn this tutorial we are going to use the MNIST dataset.\n\nMNIST is a dataset of <b>handwritten digits</b>, comprising a <b>training set</b> of <em> 60,000 examples </em> and a <b>test set</b> of <em> 10,000 examples </em>. It is widely used as a benchmark for image classification and machine learning algorithms, as well as a simple dataset for testing and experimenting with machine learning techniques.\n\nEach example in the MNIST dataset consists of a <b>28x28</b> grayscale image of a <b>handwritten digit (0-9)</b> and a corresponding <b>label</b> indicating the <b>correct digit</b>. \n\nThe MNIST dataset is a popular choice for testing machine learning algorithms because it is relatively small, easy to work with, and well-studied. It is also widely available, with many libraries and frameworks including built-in support for loading and working with the MNIST dataset.\n\n### Loading the data: \n\nkeras includes a number of built-in datasets that we can use for experimentation and testing, it just so happens that MNIST is one of the datasets included in keras ensemble of datasets. ","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:22:51.680131Z","iopub.execute_input":"2023-04-12T00:22:51.680489Z","iopub.status.idle":"2023-04-12T00:22:51.950489Z","shell.execute_reply.started":"2023-04-12T00:22:51.680461Z","shell.execute_reply":"2023-04-12T00:22:51.949204Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"path = \"/kaggle/input/digit-recognizer/train.csv\"\nmnist = pd.read_csv(path)","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:22:51.954644Z","iopub.execute_input":"2023-04-12T00:22:51.955010Z","iopub.status.idle":"2023-04-12T00:22:53.563143Z","shell.execute_reply.started":"2023-04-12T00:22:51.954978Z","shell.execute_reply":"2023-04-12T00:22:53.562049Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X, y = mnist.drop([\"label\"],axis=1), mnist['label']","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:22:53.564572Z","iopub.execute_input":"2023-04-12T00:22:53.565177Z","iopub.status.idle":"2023-04-12T00:22:53.622480Z","shell.execute_reply.started":"2023-04-12T00:22:53.565146Z","shell.execute_reply":"2023-04-12T00:22:53.621363Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### A bit of exploration : \n","metadata":{}},{"cell_type":"code","source":"y.unique()","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:22:53.624061Z","iopub.execute_input":"2023-04-12T00:22:53.624374Z","iopub.status.idle":"2023-04-12T00:22:53.636972Z","shell.execute_reply.started":"2023-04-12T00:22:53.624348Z","shell.execute_reply":"2023-04-12T00:22:53.635744Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can see that our target has 10 categories, which corresponds to the digits, this is our target; what we are trying to predict, remember that our objectif is to see what handwritten digit corresponds to!","metadata":{}},{"cell_type":"code","source":"first_image = X.iloc[0]","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:22:53.641309Z","iopub.execute_input":"2023-04-12T00:22:53.642041Z","iopub.status.idle":"2023-04-12T00:22:53.648104Z","shell.execute_reply.started":"2023-04-12T00:22:53.641999Z","shell.execute_reply":"2023-04-12T00:22:53.646403Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(first_image.to_numpy().reshape(28,28), cmap='binary')\nplt.axis(\"off\")\nplt.show","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:22:53.649553Z","iopub.execute_input":"2023-04-12T00:22:53.650039Z","iopub.status.idle":"2023-04-12T00:22:53.719513Z","shell.execute_reply.started":"2023-04-12T00:22:53.650009Z","shell.execute_reply":"2023-04-12T00:22:53.718303Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"That allows us to see that every observation in the dataset corresponds to a hand written digit, in this case it corresponds to a <b>badly written 1</b>. \nNow that we've understood a bit more what our objectif entails and what our data is, we can start dealing with our problem.","metadata":{}},{"cell_type":"markdown","source":"### Normalizing the images: ","metadata":{}},{"cell_type":"markdown","source":"Normalizing data is very important, so every feature will have the same scale, every pixel in this case, it also allows us to use a higher learning rate because when features have a large scale, their gradient will also be big, which will make the optimization using gradient descent more likely to overshoot the optimal solution.","metadata":{}},{"cell_type":"code","source":"X = X / 255","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:22:53.721004Z","iopub.execute_input":"2023-04-12T00:22:53.722023Z","iopub.status.idle":"2023-04-12T00:22:53.782012Z","shell.execute_reply.started":"2023-04-12T00:22:53.721980Z","shell.execute_reply":"2023-04-12T00:22:53.781135Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Splitting into train and test:","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2, random_state = 42)","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:22:53.782958Z","iopub.execute_input":"2023-04-12T00:22:53.783248Z","iopub.status.idle":"2023-04-12T00:22:53.990413Z","shell.execute_reply.started":"2023-04-12T00:22:53.783220Z","shell.execute_reply":"2023-04-12T00:22:53.989030Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### One hot encoding the target: \n\nWe have the labels for each input data.\nThey are numbers between 0 and 9 that indicate which digit represents the image, that is, to which class they are associated.\nWe will represent this label with a vector of 10 positions, where the position corresponding to the digit that represents the image contains a 1 and the remaining positions of the vector contain the value 0.\n\nSo, we will use the one-hot encoding procedure.\nIt consists of transforming the labels into a vector of as many zeros as the number of different labels, and containing the value of 1 in the index that corresponds to the value of the label.\nKeras offers many support functions, including <i> to_categorical </i> to perform this transformation, which we can import from <i> keras.utils</i>:\n\n","metadata":{}},{"cell_type":"code","source":"from keras.utils import np_utils","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:22:53.991722Z","iopub.execute_input":"2023-04-12T00:22:53.992221Z","iopub.status.idle":"2023-04-12T00:23:03.267161Z","shell.execute_reply.started":"2023-04-12T00:22:53.992189Z","shell.execute_reply":"2023-04-12T00:23:03.265819Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train = np_utils.to_categorical(y_train, 10)\ny_test = np_utils.to_categorical(y_test, 10)","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:23:03.268787Z","iopub.execute_input":"2023-04-12T00:23:03.269639Z","iopub.status.idle":"2023-04-12T00:23:03.277183Z","shell.execute_reply.started":"2023-04-12T00:23:03.269584Z","shell.execute_reply":"2023-04-12T00:23:03.276052Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Modeling: \n\nWe are going to use Keras. Keras is a high-level neural network API that is built on top of TensorFlow, Theano, or CNTK. There are several reasons why Keras is a good choice for building neural networks:\n\n   * User-friendly interface: Keras provides a simple, intuitive, and easy-to-use interface for building neural networks. It offers a wide range of pre-built layers and models, which makes it easier to get started with deep learning.\n\n   * Modular design: Keras is designed in a modular way, which means that you can build and modify neural networks by adding or removing layers. This makes it easier to experiment with different architectures and hyperparameters.","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.layers import Dense\nfrom tensorflow.keras.models import Sequential","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:23:03.278295Z","iopub.execute_input":"2023-04-12T00:23:03.278590Z","iopub.status.idle":"2023-04-12T00:23:03.706937Z","shell.execute_reply.started":"2023-04-12T00:23:03.278561Z","shell.execute_reply":"2023-04-12T00:23:03.706025Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = Sequential()\nmodel.add(Dense(128, activation = \"relu\", input_shape =(784,)))\nmodel.add(Dense(64, activation = \"relu\"))\nmodel.add(Dense(10, activation =\"softmax\"))","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:23:03.708038Z","iopub.execute_input":"2023-04-12T00:23:03.708322Z","iopub.status.idle":"2023-04-12T00:23:03.890351Z","shell.execute_reply.started":"2023-04-12T00:23:03.708296Z","shell.execute_reply":"2023-04-12T00:23:03.889103Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n\n* Here, the neural network has been defined as a sequence of two layers that are densely connected (or fully connected).\n\n* It means that all the neurons in each layer are connected to all the neurons in the next layer.\n\n#### First layer:\n\n   - We explicitly express in the input_shape argument of the first layer what the input data is like: it indicates that we have 784 features of the model.\n   - The input layer is not included when we define the architecture, instead Keras knows to put an input layer when it sees the input_shape argument (The first layer is the first hidden layer in the network).\n\n#### Second layer: \n - We add a hidden layer having 64 neurons, activation argument specifies the activation function, here we use relu.\n\n#### Third layer (Output layer) : \n \n - The second layer is a softmax layer of 10 neurons, which means that it will return a matrix of 10 probability values representing the 10 possible digits.\n\n - Each value will be the probability that the image of the current digit belongs to each one of them.\n\n","metadata":{}},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:23:03.891699Z","iopub.execute_input":"2023-04-12T00:23:03.892089Z","iopub.status.idle":"2023-04-12T00:23:03.910929Z","shell.execute_reply.started":"2023-04-12T00:23:03.892049Z","shell.execute_reply":"2023-04-12T00:23:03.909927Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n\n* Keras provides a very useful method to check the architecture of the model. It is the .summary() method.\n\n### Parameters of the model:\n\nFor our example, we see that it indicates that 109,386 parameters are required.\nIf we see (column Param #), which correspond to 100480 parameters to the first layer and 8256 to the second and 650 to the third.\n\n#### Parameters of the first layer:\n\nIn the first layer, for each neuron i (between 0 and 127) we require a weight wij and therefore 128×784=100352 parameters to store the weights of the 128 neurons.\n\nAlso, 128 additional parameters for the 128 bi biases corresponding to each one of them is required.\n\nSo, for the first layer we require 100352 + 128 = 100480 parameters.\n\n#### Parameters of the second layer:\n\nIn the second layer, each neuron j (between 0 and 63) is connected to each neuron i in the previous layer so we need 128x64=8192  to store the weights of the 64 new neurons.\n\nAlso, 64 additional parameters for the 64 bj biases corresponding to each one of them is required.\n\nSo, for the second layer we require 8192 + 64 = 8256 parameters.\n\n#### Parameters of the second layer:\n\nIn the third layer, being a softmax function, it is required to connect all 10 neurons with the 64 neurons of the previous layer.\n\nTherefore 10x64=640 wi parameters are required and in addition 10 bj biases corresponding to each node is required.\n\nSo, we require 640 + 10 = 650 parameters for the second layer.\n\n","metadata":{}},{"cell_type":"markdown","source":"### Implementation of Neural Network in Keras : \n\nThe implementation of Neural Network in Keras comprises of three steps:-\n * Compiling the model with the <b> compile()</b> method.\n * Training the model with <b>fit()</b> method.\n * Evaluating the model with <b>evaluate()</b> method.\n\n#### Compile model with compile() method\n\nStarting with the Sequential model, we can define the layers in a simple way with the add() method.\nThe next step is to compile the model with the compile() method, with which we can specify some properties through method arguments which are as follows:\n\n<i> Loss Function :  </i>\n\nThe first argument is the loss function. We will use it to evaluate the degree of error between calculated outputs and the desired outputs of the training data.\n\n<i>Optimizer : </i>\n\nThe second argument is the optimizer. It is the way we have to specify the optimization algorithm that allows the neural network to calculate the weights of the parameters from the input data and the defined loss function.\n\n<i> Metrics : </i>\n\nThe third argument is the metrics. We must indicate the metric that we will use to monitor the learning process and test of our neural network. In this example, we will only consider the accuracy - the fraction of images that are correctly classified.\n\n","metadata":{}},{"cell_type":"code","source":"model.compile(loss=\"categorical_crossentropy\",\n              optimizer=\"Adam\",\n              metrics = ['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:23:03.914276Z","iopub.execute_input":"2023-04-12T00:23:03.914659Z","iopub.status.idle":"2023-04-12T00:23:03.933626Z","shell.execute_reply.started":"2023-04-12T00:23:03.914630Z","shell.execute_reply":"2023-04-12T00:23:03.931759Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Train the model with fit() method\n\nNow, that we have compiled our model, the next step is to train the model.\nWe can train the model with the fit() method as follows:\n","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\ntf.config.run_functions_eagerly(True)\nimport warnings\nwarnings.filterwarnings(\"ignore\") #this is to ignore warnings generated by code","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:23:03.935755Z","iopub.execute_input":"2023-04-12T00:23:03.936284Z","iopub.status.idle":"2023-04-12T00:23:03.942695Z","shell.execute_reply.started":"2023-04-12T00:23:03.936236Z","shell.execute_reply":"2023-04-12T00:23:03.941051Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.fit(X_train,y_train, batch_size = 100, epochs=10,validation_data=(X_test, y_test))","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:23:03.944920Z","iopub.execute_input":"2023-04-12T00:23:03.945356Z","iopub.status.idle":"2023-04-12T00:24:46.773314Z","shell.execute_reply.started":"2023-04-12T00:23:03.945311Z","shell.execute_reply":"2023-04-12T00:24:46.772231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* In the first two arguments (x_train, y_train) we have indicated the data with which we will train the model in the form of Numpy arrays.\n* The batch_size argument indicates the number of data that we will use for each update of the model parameters.\n* The epochs argument indicate the number of times we will use all the data in the learning process.\n* The validation_data argument allows us to see model performance on unseen data.\n","metadata":{}},{"cell_type":"markdown","source":"#### Evaluate model with evaluate() method\n\nOur model has been compiled and trained. Now, we come to the final step. The model can now be evaluated with the evaluate() method as follows:\n","metadata":{}},{"cell_type":"code","source":"test_loss, test_acc = model.evaluate(X_test, y_test)","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:24:46.774925Z","iopub.execute_input":"2023-04-12T00:24:46.775287Z","iopub.status.idle":"2023-04-12T00:24:49.418910Z","shell.execute_reply.started":"2023-04-12T00:24:46.775251Z","shell.execute_reply":"2023-04-12T00:24:49.417659Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(test_acc)","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:24:49.420534Z","iopub.execute_input":"2023-04-12T00:24:49.420962Z","iopub.status.idle":"2023-04-12T00:24:49.426600Z","shell.execute_reply.started":"2023-04-12T00:24:49.420921Z","shell.execute_reply":"2023-04-12T00:24:49.424985Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Confusion matrix : \n\n\nIn Machine Learning, a very useful tool to evaluate the model is Confusion Matrix. It is a table with rows and columns that count the predictions in comparison with the real values. This table can be used to understand the performance of the model.\n\nIt is very useful to show explicitly when one class is confused with another.\nA confusion matrix for a binary classifier is shown below:-\n\n![image.png](attachment:image.png)\n\n\n<b> True Positives (TP), True Negatives (TN), False Positives (FP) and False Negatives (FN) </b> are the four different possible outcomes of a single prediction for a two-class case with classes “1” (“positive”) and “0” (“negative”).\n\n<b> A False Positive (FP) </b> is when the outcome is incorrectly classified as positive, when it is in fact negative. <b>A False Negative (FN) </b> is when the outcome is incorrectly classified as negative when it is in fact positive. <b>True Positives (TP)</b> and <b>True Negatives (TN)</b> are obviously correct classifications.\n\nWith this confusion matrix, the accuracy can be calculated by adding the values of the diagonal and dividing them by the total:\n\n<b> Accuracy = (TP + TN) / (TP + FP + FN + TN)</b>\nThere is another metric called Sensitivity (or recall) that tells us how well the model avoids false negatives:\n\n<b>Sensitivity = TP / (TP + FN)</b>\n\n\nFrom the confusion matrix, several other metrics can be obtained\n\nBut in this case, there are 10 classes instead of 2. So, in this case, Scikit-learn package can be used to evaluate the quality of the model by calculating the confusion matrix.\n\n\n\n","metadata":{},"attachments":{"image.png":{"image/png":"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"}}},{"cell_type":"code","source":"y_pred = model.predict(X_test)\n# Convert predictions classes back to the digit \ny_pred_classes = np.argmax(y_pred, axis = 1) ","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:25:12.063098Z","iopub.execute_input":"2023-04-12T00:25:12.063453Z","iopub.status.idle":"2023-04-12T00:25:13.096740Z","shell.execute_reply.started":"2023-04-12T00:25:12.063427Z","shell.execute_reply":"2023-04-12T00:25:13.094858Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(np.round(y_pred[0]))","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:25:15.073946Z","iopub.execute_input":"2023-04-12T00:25:15.074304Z","iopub.status.idle":"2023-04-12T00:25:15.081665Z","shell.execute_reply.started":"2023-04-12T00:25:15.074273Z","shell.execute_reply":"2023-04-12T00:25:15.079947Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Rounding y_pred[0] gives 1 just at the 7th position meaning that the probability of the digit being 7 is way higher than every other digit (the index of the probability gives us the digit), which is why np.argmax (which gives the position of the highest probability) would give us what digit was predicted!","metadata":{}},{"cell_type":"code","source":"# revert y_test back to its original form before one-hot-encoding\ny_true = np.argmax(y_test, axis = 1) ","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:25:18.412468Z","iopub.execute_input":"2023-04-12T00:25:18.412848Z","iopub.status.idle":"2023-04-12T00:25:18.418683Z","shell.execute_reply.started":"2023-04-12T00:25:18.412816Z","shell.execute_reply":"2023-04-12T00:25:18.417056Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\nconfusion = confusion_matrix(y_true, y_pred_classes)\nsns.heatmap(confusion, annot=True, fmt='d')\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:25:26.682865Z","iopub.execute_input":"2023-04-12T00:25:26.683228Z","iopub.status.idle":"2023-04-12T00:25:27.152852Z","shell.execute_reply.started":"2023-04-12T00:25:26.683202Z","shell.execute_reply":"2023-04-12T00:25:27.151500Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Classification report: ","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import classification_report\nprint(classification_report(y_true,y_pred_classes))","metadata":{"execution":{"iopub.status.busy":"2023-04-12T00:25:30.879591Z","iopub.execute_input":"2023-04-12T00:25:30.880000Z","iopub.status.idle":"2023-04-12T00:25:30.907509Z","shell.execute_reply.started":"2023-04-12T00:25:30.879972Z","shell.execute_reply":"2023-04-12T00:25:30.905604Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Looks like our model does pretty well, it manages to have a good accuracy score, and also great precision, recall and f1 score on every class! The confusion matrix also shows us that the class are predicted well most of the time.","metadata":{}}]}