{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3004,"databundleVersionId":861823,"sourceType":"competition"},{"sourceId":10112647,"sourceType":"datasetVersion","datasetId":6239016}],"dockerImageVersionId":30786,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"<a class=\"anchor\" id=\"0\"></a>\n# Multi-class classification using ANNs and Vanilla (simple) DL Models\n# Digit recognition (Pattern Recognition) using the MNIST Dataset- with Keras\n![image.png](attachment:44ab7f55-b208-4942-8edb-1f5cdf9b41f1.png)\n\n# ","metadata":{},"attachments":{"44ab7f55-b208-4942-8edb-1f5cdf9b41f1.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# Objective of the notebook <a class=\"anchor\" id=\"1\"></a>\n\n\n\n- In this notebook, we will design a **Multilayer Perceptron (MLP)** model of the MNIST dataset. \n\n- **Multilayer Perceptrons (MLPs)** usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The \"fully-connectedness\" of these networks makes them prone to overfitting the data. \n\n- These MLP models are also referred to as **feedforward neural networks**. MLPs are common in simple logistic and linear regression problems.\n\n- So, the objective is to create a neural network for identifying numbers based on handwritten digits. For example, when the input to the network is an image of a handwritten number 8, the corresponding prediction must also be the digit 8. \n\n- To both train and validate a neural network, there must be a sufficiently large dataset of handwritten digits.\n\n- The Modified National Institute of Standards and Technology dataset or MNIST dataset for short, is often considered as the Hello World! of deep learning and is a suitable dataset for handwritten digit classification.\n\n- MNIST is used to explain and validate deep learning theories because the 70,000 samples it contains are small, yet sufficiently rich in information (MNIST dataset is described later).","metadata":{}},{"cell_type":"markdown","source":"So, we will start by importing the necessary libraries:","metadata":{}},{"cell_type":"markdown","source":"# I - Designing an ANN model using Sci-learn ML Library <a class=\"anchor\" id=\"5\"></a>","metadata":{}},{"cell_type":"markdown","source":"# 1. Import necessary libraries <a class=\"anchor\" id=\"2\"></a>\n\n","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\n# conda install  TensorFlow\n# The Official Keras Deep Neural Network Example for CNN \n###############################\n# Setting Seed Random Number for Reproducibility\nimport numpy as np # linear algebra\nnp.random.seed(123)  # for reproducibility\n\n# Setting Seed Random Number for the TensorFlow Backend\nimport tensorflow as tf\ntf.random.set_seed(123) # for reproducibility\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt # plotting library\n#%matplotlib inline\n\nfrom tensorflow import keras\nfrom keras import  backend as K","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:19:32.517030Z","iopub.execute_input":"2024-12-05T17:19:32.517563Z","iopub.status.idle":"2024-12-05T17:19:46.656411Z","shell.execute_reply.started":"2024-12-05T17:19:32.517499Z","shell.execute_reply":"2024-12-05T17:19:46.655373Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 2. Loading MNIST dataset <a class=\"anchor\" id=\"3\"></a>\n\n\n\nThe **MNIST** dataset (**Modified National Institute of Standards and Technology database**) is a large collection of **handwritten digits** that is widely used for training and testing in the field of machine learning and image processing. Here are some key points about it:\n\n- MNIST is a collection of handwritten digits ranging from the number 0 to 9. \n\n- It consists of 60,000 training images and 10,000 test images of handwritten digits, each image being 28x28 pixels in grayscale.\n\n- The dataset is commonly used to **benchmark machine learning algorithms**, particularly in the areas of image recognition and classification.\n\n- To use the MNIST dataset in Keras, an API is provided to download and extract images and labels automatically.\n\n- Variants: There are extended versions like EMNIST, which includes handwritten letters, and Fashion MNIST, which contains images of fashion products.\n","metadata":{}},{"cell_type":"markdown","source":"The following Keras code shows how to load MNIST dataset, plot 25 random samples, and count the number of labels for train and test datasets.","metadata":{}},{"cell_type":"code","source":"# import dataset\nfrom keras.datasets import mnist\n\n# load dataset\n(x_train, y_train),(x_test, y_test) = mnist.load_data() # ouput numpy arrays\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:19:51.382699Z","iopub.execute_input":"2024-12-05T17:19:51.383058Z","iopub.status.idle":"2024-12-05T17:19:51.983952Z","shell.execute_reply.started":"2024-12-05T17:19:51.383026Z","shell.execute_reply":"2024-12-05T17:19:51.982801Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 3. Data exploration and visualization <a class=\"anchor\" id=\"4\"></a>\n\n\n\n\n- The following code will help to sample the 25 random MNIST digits and visualize them.","metadata":{}},{"cell_type":"code","source":"print(type(x_train))\nprint(\"x_train shape:\", x_train.shape )\nprint(\"x_test shape:\",x_test.shape )\nprint(\"y_train shape:\",y_train.shape )\nprint(\"y_test shape:\",y_test.shape )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:19:59.712870Z","iopub.execute_input":"2024-12-05T17:19:59.713254Z","iopub.status.idle":"2024-12-05T17:19:59.719547Z","shell.execute_reply.started":"2024-12-05T17:19:59.713223Z","shell.execute_reply":"2024-12-05T17:19:59.718460Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(\"labels: \",np.unique(y_train)) # multi-classification\n\n# count the number of unique train labels\ncategory_name, counts = np.unique(y_train, return_counts=True)\nprint(\"Train labels: \", dict(zip(category_name, counts)))\n\n# count the number of unique test labels\ncategory_name, counts = np.unique(y_test, return_counts=True)\nprint(\"\\nTest labels: \", dict(zip(category_name, counts)))\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:20:05.386110Z","iopub.execute_input":"2024-12-05T17:20:05.386512Z","iopub.status.idle":"2024-12-05T17:20:05.397596Z","shell.execute_reply.started":"2024-12-05T17:20:05.386476Z","shell.execute_reply":"2024-12-05T17:20:05.396362Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# sample 9 mnist digits from train dataset\nindexes = np.random.randint(0, x_train.shape[0], size=9)\nimages = x_train[indexes]\nlabels = y_train[indexes]\n\n# plot the 9 mnist digits\nplt.figure(figsize=(3,3))\nfor i in range(len(indexes)):\n    plt.subplot(3, 3, i + 1)\n    image = images[i]\n    plt.imshow(image, cmap='gray')\n    plt.axis('off')\n    \nplt.show()\nplt.savefig(\"mnist-samples.png\")\nplt.close('all')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:20:12.228956Z","iopub.execute_input":"2024-12-05T17:20:12.229330Z","iopub.status.idle":"2024-12-05T17:20:12.576731Z","shell.execute_reply.started":"2024-12-05T17:20:12.229298Z","shell.execute_reply":"2024-12-05T17:20:12.575193Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 4 Data Preprocessing <a class=\"anchor\" id=\"4\"></a>\n\n- **One-Hot Encode the Labels**: Convert the class labels (0-9) into one-hot encoded vectors. This is necessary for the output layer of the ANN.\n- **Reshape the Data**: Since ANNs expect 1D input, you need to flatten the 28x28 images into 1D vectors of size 784.\n- **Normalize the Data**: Scale the pixel values to the range [0, 1] by dividing by 255. This helps the network train faster and more effectively.","metadata":{}},{"cell_type":"markdown","source":"### 4.1 One-Hot Encode the Labels <a class=\"anchor\" id=\"5.2\"></a>\n\nThe number of labels is computed as:","metadata":{}},{"cell_type":"code","source":"np.unique(y_train)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:20:26.550001Z","iopub.execute_input":"2024-12-05T17:20:26.550415Z","iopub.status.idle":"2024-12-05T17:20:26.559162Z","shell.execute_reply.started":"2024-12-05T17:20:26.550368Z","shell.execute_reply":"2024-12-05T17:20:26.558017Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# compute the number of labels\nnum_labels = len(np.unique(y_train))\nprint(num_labels)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:20:31.272273Z","iopub.execute_input":"2024-12-05T17:20:31.272659Z","iopub.status.idle":"2024-12-05T17:20:31.280086Z","shell.execute_reply.started":"2024-12-05T17:20:31.272626Z","shell.execute_reply":"2024-12-05T17:20:31.278790Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"Keras requires your output variable to be one-hot encoded values in the case of multy-classification.\n\n![image.png](attachment:8f1a6439-f36a-419e-8c1a-ac525d71a403.png)\n\n**One-hot encoding** is necessary if you use **categorical_crossentropy loss** when training the model. However if you use **sparse_categorical_crossentropy** then you can skip this step.\n\n**The following lines convert each label into a one-hot vector**:","metadata":{},"attachments":{"8f1a6439-f36a-419e-8c1a-ac525d71a403.png":{"image/png":"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"}}},{"cell_type":"code","source":"from tensorflow.keras.utils import to_categorical, plot_model\n# convert to one-hot vector\nY_train = to_categorical(y_train)\nY_test = to_categorical(y_test)\n\nprint (\"A sample output before encoding: \",y_train[0])\nprint (\"A sample output after encoding: \",Y_train[0])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:20:40.198345Z","iopub.execute_input":"2024-12-05T17:20:40.199348Z","iopub.status.idle":"2024-12-05T17:20:40.284220Z","shell.execute_reply.started":"2024-12-05T17:20:40.199308Z","shell.execute_reply":"2024-12-05T17:20:40.283195Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 4.2 Reshape the Data\nEach image is **28 pixels in height** and **28 pixels in width**, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive.\n\nThe training data set, (train.csv), has 785 columns. The first column, called \"label\", is the digit that was drawn by the user. The rest of the columns contain the pixel-values of the associated image.\n\n![image.png](attachment:5f951dc9-409c-4fda-a2fc-25241876f700.png)\n","metadata":{},"attachments":{"5f951dc9-409c-4fda-a2fc-25241876f700.png":{"image/png":"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"}}},{"cell_type":"code","source":"# image dimensions\nimage_height = x_train.shape[1]\ninput_width = x_train.shape[2]\ninput_size = image_height * input_width\nprint(input_size)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:20:48.680431Z","iopub.execute_input":"2024-12-05T17:20:48.680827Z","iopub.status.idle":"2024-12-05T17:20:48.687104Z","shell.execute_reply.started":"2024-12-05T17:20:48.680792Z","shell.execute_reply":"2024-12-05T17:20:48.685884Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Resize the images\nX_train = np.reshape(x_train, [-1, input_size])\nX_test = np.reshape(x_test, [-1, input_size])\n\nprint(\"x_train shape before the re-shaping:\", x_train.shape )\nprint(\"x_train shape after flattening:\", X_train.shape )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:20:51.952790Z","iopub.execute_input":"2024-12-05T17:20:51.953195Z","iopub.status.idle":"2024-12-05T17:20:51.959506Z","shell.execute_reply.started":"2024-12-05T17:20:51.953161Z","shell.execute_reply":"2024-12-05T17:20:51.958158Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 4.3 Normalize the Data","metadata":{}},{"cell_type":"code","source":"# Preprocess input data\nprint(\"Range of X_train data before re-scaling: [ \", max(X_train[0].round(2)), ',', min(X_train[0]), ' ]' )\n# Scale image data to the [0, 1] range\nX_train_ = X_train.astype('float32') / 255\nX_test_ = X_test.astype('float32') / 255\n\nprint(\"Range of X_train data after re-scaling: [ \", max(X_train_[0].round(2)), ',', min(X_train[0]), ' ]' )","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:20:56.252555Z","iopub.execute_input":"2024-12-05T17:20:56.252972Z","iopub.status.idle":"2024-12-05T17:20:56.369001Z","shell.execute_reply.started":"2024-12-05T17:20:56.252916Z","shell.execute_reply":"2024-12-05T17:20:56.367836Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 5. Designing ANN model architecture using Sci-learn ML Library <a class=\"anchor\" id=\"5\"></a>\n\n\n- The MLP model, discussed above can be used for MNIST digits classification. \n\n- The MLP model is a fully connected network.","metadata":{}},{"cell_type":"markdown","source":"### 5.1 Design ANN model <a class=\"anchor\" id=\"5.5\"></a>\n\n- Now, we will set the network parameters as follows:","metadata":{}},{"cell_type":"code","source":"from sklearn.neural_network import MLPClassifier\n# ('identity', 'logistic', 'tanh', 'relu')\n\n# ('identity', 'logistic', 'tanh', 'relu')\n# Design an ANN model\nANN = MLPClassifier(hidden_layer_sizes=(256, 128), \n                    solver='adam', \n                    activation='relu',\n                    max_iter=50,\n                    random_state=1, \n                    alpha=1e-04) # alpha=1e-04 \nANN\n# ANN = MLPClassifier(hidden_layer_sizes=350, solver='adam', activation=\"relu\", random_state=1, alpha=1e-04,  max_iter=1000, early_stopping=True, n_iter_no_change =1000, validation_fraction=0.1 )\n\n\n# Hidden layer activation functions {‘identity’, ‘logistic’, ‘tanh’, ‘relu’}, default=’relu’\n# Activation function for the all hidden layers is the same\n# solver{‘lbfgs’, ‘sgd’, ‘adam’}, default=’adam’\n# MLPClassifier uses the Average Cross-Entropy loss function. In the current version you can not set this parameter.\n# MLPClassifier uses softmax transfer function as output activation function for Multiclass-Classification. In the current version you can not set this parameter.\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:21:38.713345Z","iopub.execute_input":"2024-12-05T17:21:38.713747Z","iopub.status.idle":"2024-12-05T17:21:39.466995Z","shell.execute_reply.started":"2024-12-05T17:21:38.713711Z","shell.execute_reply":"2024-12-05T17:21:39.465826Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 5.2 Train the ANN model <a class=\"anchor\" id=\"5.5\"></a>","metadata":{}},{"cell_type":"code","source":"# Train the ANN model\nANN.fit(X_train_, Y_train)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:21:54.120584Z","iopub.execute_input":"2024-12-05T17:21:54.121241Z","iopub.status.idle":"2024-12-05T17:23:45.546162Z","shell.execute_reply.started":"2024-12-05T17:21:54.121202Z","shell.execute_reply":"2024-12-05T17:23:45.544160Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(ANN.out_activation_)\nprint(ANN.n_outputs_)\nprint(ANN.n_features_in_)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:25:48.663682Z","iopub.execute_input":"2024-12-05T17:25:48.664114Z","iopub.status.idle":"2024-12-05T17:25:48.669906Z","shell.execute_reply.started":"2024-12-05T17:25:48.664078Z","shell.execute_reply":"2024-12-05T17:25:48.668800Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 5.3 Evaluate the ANN model <a class=\"anchor\" id=\"5.5\"></a>","metadata":{}},{"cell_type":"code","source":"# Find out and display the test and training accuracies\ntrain_accuracy = ANN.score(X_train_, Y_train)\nprint(f\"Training Accuracy (Scikit-learn): {train_accuracy * 100:.2f}%\")\ntest_accuracy = ANN.score(X_test_, Y_test)\nprint(f\"Test Accuracy (Scikit-learn): {test_accuracy * 100:.2f}%\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:26:17.331915Z","iopub.execute_input":"2024-12-05T17:26:17.332339Z","iopub.status.idle":"2024-12-05T17:26:17.915869Z","shell.execute_reply.started":"2024-12-05T17:26:17.332303Z","shell.execute_reply":"2024-12-05T17:26:17.913496Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Visualize Evaluation Metrics of ML Models\nfrom sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n# Make predictions\nY_pred = ANN.predict(X_test_)\nY_pred_categorical = np.argmax(Y_pred, axis=1) # convert softmax output (ANN prediction) into the categorical label\nY_test_categorical = np.argmax(Y_test, axis=1) # convert one-hot encoded label into the categorical label\n\n# Compute the confusion matrix\ncm = confusion_matrix(Y_test_categorical, Y_pred_categorical)\n\n# Plot the confusion matrix\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm)\ndisp.plot(cmap=plt.cm.Blues)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:26:31.991767Z","iopub.execute_input":"2024-12-05T17:26:31.992168Z","iopub.status.idle":"2024-12-05T17:26:32.580478Z","shell.execute_reply.started":"2024-12-05T17:26:31.992133Z","shell.execute_reply":"2024-12-05T17:26:32.579493Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 5.4 Monitoring the training process <a class=\"anchor\" id=\"5.5\"></a>","metadata":{}},{"cell_type":"code","source":"print('Number of the training: {}'.format(ANN.n_iter_)) # Default value for the max_iter parameter is 200","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:26:44.386618Z","iopub.execute_input":"2024-12-05T17:26:44.387048Z","iopub.status.idle":"2024-12-05T17:26:44.393153Z","shell.execute_reply.started":"2024-12-05T17:26:44.387009Z","shell.execute_reply":"2024-12-05T17:26:44.391934Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print('Number of the training: {}'.format(ANN.n_iter_))\ntrn_loss= ANN.loss_curve_ #errors\n#validation_scores = ANN.validation_scores_ #validation_scores_ is not available if early_stopping=False\n\nimport matplotlib.pyplot as plt\nplt.rcParams['figure.figsize'] = 5, 5\nplt.plot( trn_loss, label='Losses') # errors plot\nplt.legend()\nplt.xlabel('Iterations')\nplt.ylabel('Losses')\nplt.title('Training Losses over Iterations')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:26:55.647160Z","iopub.execute_input":"2024-12-05T17:26:55.647590Z","iopub.status.idle":"2024-12-05T17:26:55.949447Z","shell.execute_reply.started":"2024-12-05T17:26:55.647546Z","shell.execute_reply":"2024-12-05T17:26:55.948262Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# II - Designing ANN model using Keras DL Library <a class=\"anchor\" id=\"5\"></a>","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Input, Activation, Dropout\nfrom tensorflow.keras.utils import to_categorical, plot_model","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:27:10.210916Z","iopub.execute_input":"2024-12-05T17:27:10.211345Z","iopub.status.idle":"2024-12-05T17:27:10.219513Z","shell.execute_reply.started":"2024-12-05T17:27:10.211311Z","shell.execute_reply":"2024-12-05T17:27:10.218572Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# network parameters\nbatch_size = 128","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:27:22.781519Z","iopub.execute_input":"2024-12-05T17:27:22.781963Z","iopub.status.idle":"2024-12-05T17:27:22.786935Z","shell.execute_reply.started":"2024-12-05T17:27:22.781906Z","shell.execute_reply":"2024-12-05T17:27:22.785829Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"- The **batch_size** argument indicates the number of data that we will use for each update of the model parameters.\n","metadata":{}},{"cell_type":"markdown","source":"# 1. Designing ANN model using Keras DL Library <a class=\"anchor\" id=\"5\"></a>","metadata":{}},{"cell_type":"markdown","source":"### 1.1 Designing the model architecture <a class=\"anchor\" id=\"5.6\"></a>\n\n\n- The next step is to design the model architecture. The proposed model is made of three MLP layers. \n\n- In Keras, an MLP layer is referred to as Dense, which stands for the densely connected layer. \n\n- Both the first and second MLP layers are identical in nature with 256 neurons each and with relu activation. \n\n- Output layer has 10 neurons, which is equal to the number of the categories and with softmax activation. ","metadata":{}},{"cell_type":"markdown","source":"- The main data structure in Keras is the Sequential class, which allows the creation of a basic neural network.\n\n- The Sequential class of the Keras library is a wrapper for the sequential neural network model that Keras offers and can be created in the following way:\n\n`from keras.models import Sequential`\n\n`model = Sequential()`\n\n- The model in Keras is considered as a sequence of layers and each of them gradually “distills” the input data to obtain the desired output.\n\n- In Keras, we can add the required types of layers through the **add()** method.","metadata":{}},{"cell_type":"code","source":"# Design an ANN model with a 3-layer and  ReLU using the Keras\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Activation\nmodel = Sequential([\n    Dense(256, input_shape=(784,), activation='relu'), \n    Dense(256, activation='relu'),                    \n    Dense(10, activation='softmax')                  \n])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:37:14.501405Z","iopub.execute_input":"2024-12-05T17:37:14.501861Z","iopub.status.idle":"2024-12-05T17:37:14.534579Z","shell.execute_reply.started":"2024-12-05T17:37:14.501825Z","shell.execute_reply":"2024-12-05T17:37:14.533459Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"- Since a Dense layer is a linear operation, a sequence of Dense layers can only approximate a linear function. \n\n- The problem is that the MNIST digit classification is inherently a non-linear process. Inserting a relu activation between Dense layers will enable MLPs to model non-linear mappings. \n\n- relu or Rectified Linear Unit (ReLU) is a simple non-linear function. It allows positive inputs to pass through unchanged while clamping everything else to zero.","metadata":{}},{"cell_type":"markdown","source":"### 1.2 View model summary <a class=\"anchor\" id=\"5.7\"></a>\n\n- Keras library provides us **summary()** method to check the model description.","metadata":{}},{"cell_type":"code","source":"model.summary()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:37:19.199401Z","iopub.execute_input":"2024-12-05T17:37:19.199786Z","iopub.status.idle":"2024-12-05T17:37:19.222320Z","shell.execute_reply.started":"2024-12-05T17:37:19.199754Z","shell.execute_reply":"2024-12-05T17:37:19.221280Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"- The above listing shows the model summary of the proposed network. It requires a total of 269,322 parameters.\n\n- This is substantial considering that we have a simple task of classifying MNIST digits. So, MLPs are not parameter efficient. \n\n- The total number of parameters required can be computed as follows:\n\n  - From input to Dense layer: 784 × 256 + 256 = 200,960. \n  \n  - From first Dense to second Dense: 256 × 256 + 256 = 65,792. \n  \n  - From second Dense to the output layer: 10 × 256 + 10 = 2,570. \n  \n  - The total is 200,690 + 65,972 + 2,570 = 269,322.","metadata":{}},{"cell_type":"markdown","source":"- Another way of verifying the network is by calling the **plot_model()** method as follows:","metadata":{}},{"cell_type":"code","source":"plot_model(model, to_file='mlp-mnist_plot.png', \n           show_shapes=True, \n           show_layer_names=True, \n           dpi=50, \n           show_layer_activations=True,\n           show_trainable=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:37:25.108674Z","iopub.execute_input":"2024-12-05T17:37:25.109092Z","iopub.status.idle":"2024-12-05T17:37:25.383333Z","shell.execute_reply.started":"2024-12-05T17:37:25.109055Z","shell.execute_reply":"2024-12-05T17:37:25.382226Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 1.3 Compile the model with compile() method <a class=\"anchor\" id=\"6.1\"></a>\n\n\n- Compilation of model can be done as follows:","metadata":{}},{"cell_type":"code","source":"model.compile(loss='categorical_crossentropy', \n              optimizer='adam',\n              metrics=['accuracy'])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:37:32.922796Z","iopub.execute_input":"2024-12-05T17:37:32.923203Z","iopub.status.idle":"2024-12-05T17:37:32.938855Z","shell.execute_reply.started":"2024-12-05T17:37:32.923167Z","shell.execute_reply":"2024-12-05T17:37:32.937667Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### Loss function (categorical_crossentropy)\n\n- How far the predicted tensor is from the one-hot ground truth vector is called **loss**.\n\n- In this example, we use **categorical_crossentropy** as the loss function. It is the negative of the sum of the product of the target and the logarithm of the prediction. \n\n- There are other loss functions in Keras, such as mean_absolute_error and binary_crossentropy. The choice of the loss function is not arbitrary but should be a criterion that the model is learning. \n\n- For classification by category, categorical_crossentropy or mean_squared_error is a good choice after the softmax activation layer. The binary_crossentropy loss function is normally used after the sigmoid activation layer while mean_squared_error is an option for tanh output.","metadata":{}},{"cell_type":"markdown","source":"### Optimization (optimizer adam)\n\n- With optimization, the objective is to minimize the loss function. The idea is that if the loss is reduced to an acceptable level, the model has indirectly learned the function mapping input to output.\n\n- In Keras, there are several choices for optimizers. The most commonly used optimizers are; **Stochastic Gradient Descent (SGD)**, **Adaptive Moments (Adam)** and **Root Mean Squared Propagation (RMSprop)**. \n\n- Each optimizer features tunable parameters like learning rate, momentum, and decay. \n\n- Adam and RMSprop are variations of SGD with adaptive learning rates. In the proposed classifier network, Adam is used since it has the highest test accuracy.","metadata":{}},{"cell_type":"markdown","source":"### Metrics (accuracy)\n\n- Performance metrics are used to determine if a model has learned the underlying data distribution. The default metric in Keras is loss. \n\n- During training, validation, and testing, other metrics such as **accuracy** can also be included. \n\n- **Accuracy** is the percent, or fraction, of correct predictions based on ground truth.","metadata":{}},{"cell_type":"markdown","source":"### 1.4 Train the model with fit() method <a class=\"anchor\" id=\"6.2\"></a>","metadata":{}},{"cell_type":"code","source":"# Fit your model\nhistory = model.fit(\n    X_train_,  \n    Y_train,    \n    batch_size=128,         \n    epochs=20,             \n    validation_split=0.1,  \n    verbose=1               \n) ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:38:48.157161Z","iopub.execute_input":"2024-12-05T17:38:48.157594Z","iopub.status.idle":"2024-12-05T17:39:52.768154Z","shell.execute_reply.started":"2024-12-05T17:38:48.157558Z","shell.execute_reply":"2024-12-05T17:39:52.766861Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 1.5 Evaluating model performance with evaluate() method <a class=\"anchor\" id=\"6.3\"></a>","metadata":{}},{"cell_type":"code","source":"loss, acc = model.evaluate(X_train_, Y_train, batch_size=batch_size)\nprint(\"\\nTraining accuracy: %.1f%%\" % (100.0 * acc))\n\nloss, acc = model.evaluate(X_test_, Y_test, batch_size=batch_size)\nprint(\"\\nTest accuracy: %.1f%%\" % (100.0 * acc))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:40:23.813212Z","iopub.execute_input":"2024-12-05T17:40:23.813655Z","iopub.status.idle":"2024-12-05T17:40:26.218263Z","shell.execute_reply.started":"2024-12-05T17:40:23.813618Z","shell.execute_reply":"2024-12-05T17:40:26.217001Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### 1.6 Monitoring the training process <a class=\"anchor\" id=\"6.3\"></a>","metadata":{}},{"cell_type":"code","source":"pd.DataFrame(history.history).head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:40:37.856218Z","iopub.execute_input":"2024-12-05T17:40:37.856741Z","iopub.status.idle":"2024-12-05T17:40:37.882853Z","shell.execute_reply.started":"2024-12-05T17:40:37.856705Z","shell.execute_reply":"2024-12-05T17:40:37.881334Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.title('Loss')\nplt.plot(history.history['loss'], label='train loss')\nplt.plot(history.history['val_loss'], label='validation loss')\n# Set x-axis ticks to be integers\nplt.xticks(np.arange(0, len(history.history['loss']), step=5))  # This sets ticks from 0 to 10\nplt.ylabel('Errors')  \nplt.xlabel('Iterarions')  \nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:40:51.992789Z","iopub.execute_input":"2024-12-05T17:40:51.993165Z","iopub.status.idle":"2024-12-05T17:40:52.297475Z","shell.execute_reply.started":"2024-12-05T17:40:51.993134Z","shell.execute_reply":"2024-12-05T17:40:52.295987Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plt.title('accuracy')\nplt.plot(history.history['accuracy'], label='train accuracy')\nplt.plot(history.history['val_accuracy'], label='val accuracy')\n# Set x-axis ticks to be integers\nplt.xticks(np.arange(0, len(history.history['loss']), step=5))  # This sets ticks from 0 to 10\nplt.ylabel('Accuracies')  \nplt.xlabel('Iterarions')  \nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:41:02.387562Z","iopub.execute_input":"2024-12-05T17:41:02.388005Z","iopub.status.idle":"2024-12-05T17:41:02.633109Z","shell.execute_reply.started":"2024-12-05T17:41:02.387969Z","shell.execute_reply":"2024-12-05T17:41:02.632015Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# 2. Results and Conclusion <a class=\"anchor\" id=\"8\"></a>\n\n[Back to Table of Contents](#0.1)\n\n\n- In this notebook, We build a ANN neural network model using Keras DL library to classify the MNIST digits and predict accuracy.\n\n","metadata":{}},{"cell_type":"code","source":"# Visualize Evaluation Metrics of ML Models\nfrom sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n# Make predictions\nY_pred = model.predict(X_test_)\nY_pred_categorical = np.argmax(Y_pred, axis=1)\nY_test_categorical = np.argmax(Y_test, axis=1)\n\n# Compute the confusion matrix\ncm = confusion_matrix(Y_test_categorical, Y_pred_categorical)\n\n# Plot the confusion matrix\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm)\ndisp.plot(cmap=plt.cm.Blues)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:41:21.062203Z","iopub.execute_input":"2024-12-05T17:41:21.062569Z","iopub.status.idle":"2024-12-05T17:41:22.258421Z","shell.execute_reply.started":"2024-12-05T17:41:21.062536Z","shell.execute_reply":"2024-12-05T17:41:22.257256Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn import metrics\nprint(metrics.classification_report(Y_test_categorical, Y_pred_categorical))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T17:41:31.982945Z","iopub.execute_input":"2024-12-05T17:41:31.983312Z","iopub.status.idle":"2024-12-05T17:41:32.020331Z","shell.execute_reply.started":"2024-12-05T17:41:31.983279Z","shell.execute_reply":"2024-12-05T17:41:32.019247Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Developing a User Interface to interact with the trained model\n# 3. Using the trained model in the production environment","metadata":{}},{"cell_type":"markdown","source":"*Draw a digit on the black background* using the Paint application and save it as **png** image. As seen the following example:\n\n![image.png](attachment:77f091c0-cd1b-4401-8fdd-ccea49830f20.png)","metadata":{},"attachments":{"77f091c0-cd1b-4401-8fdd-ccea49830f20.png":{"image/png":"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"}}},{"cell_type":"code","source":"# Draw a number on the black background using the Paint application and save it as png image. As seen in the example above:\n# Execute this cell to make available the upload button and upload your image from your computer to the kaggle notebook. \nimport ipywidgets as widgets\nfrom ipywidgets import interact\n\n# Define the widget\nfile_upload = widgets.FileUpload(\n    accept='.png',   # The type of files to accept\n    multiple=False    # Whether to allow to upload multiple files or not\n)\n\n# Display the widget\ndisplay(file_upload)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T18:09:32.279326Z","iopub.execute_input":"2024-12-05T18:09:32.279704Z","iopub.status.idle":"2024-12-05T18:09:32.289725Z","shell.execute_reply.started":"2024-12-05T18:09:32.279665Z","shell.execute_reply":"2024-12-05T18:09:32.288606Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#Run this cell to see the prediction for your uploaded image.\n\nif  len(file_upload.data)!=0:\n    import io\n    rawdata = file_upload.data[0]\n    #When you need to handle binary data (like images or files) without saving them to disk, io.BytesIO can be very handy\n    img = io.BytesIO(rawdata)\n    # loading and displaying the sample image\n    from PIL import Image\n    import numpy as np\n    import matplotlib.pyplot as plt \n    img=Image.open('/kaggle/input/3-picture/3.png').convert('L').resize((28, 28))\n    \n    # convert('L') loads the image as gray scale image.\n    img=np.array(img) # convert the image into the Numpy array.\n    \n    # Plot the image\n    plt.figure(figsize=(2, 2))  # Width: 2 inches, Height: 2 inches\n    plt.imshow(img, cmap='gray')\n    plt.axis('off')  # Hide the axis\n    plt.show()\n    # Print the Shape of image as it is a Numpy Array\n    print(\"Image is reshaped into:\", img.shape)\n\n    # Scalling the image so that all pixel values can fall into \n    # the range of 1 to 0 \n    # Same as our training samples\n    img=img/255\n    # reshape our image data matrix \n    # so that match input shape of the our trained perceptron network \n    img=img.reshape(-1, 28*28)\n    print('Flattened image shape:\\n',img.shape)\n    prediction = model.predict(img)\n    prediction = np.argmax(prediction, axis=1)\n    print(\"Prediction for the uploaded image is: \", prediction)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-05T18:09:57.774462Z","iopub.execute_input":"2024-12-05T18:09:57.774846Z","iopub.status.idle":"2024-12-05T18:09:57.782911Z","shell.execute_reply.started":"2024-12-05T18:09:57.774812Z","shell.execute_reply":"2024-12-05T18:09:57.781711Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# References <a class=\"anchor\" id=\"9\"></a>\n\n1. https://keras.io/api/datasets/mnist/\n\n\n\n\n\n\n","metadata":{}}]}