{ "cells": [ { "cell_type": "markdown", "id": "de34c7d1-02a3-4278-8077-722d54effe6b", "metadata": {}, "source": [ "# Neural Networks (part 2)" ] }, { "cell_type": "markdown", "id": "ed376cc7-3200-4c2c-9ed6-5bdf65ed383e", "metadata": { "jp-MarkdownHeadingCollapsed": true, "tags": [], "user_expressions": [] }, "source": [ "## 1. Introduction \n", "In this notebook you create a NN for recognizing handwritten digits or fashion items.\n", "\n", "#### Refrences:\n", "\n", "***MNIST dataset***: Deng, L. (2012). The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine, 29(6), 141–142.\n", "\n", "***Fashion dataset***: Han Xiao and Kashif Rasul and Roland Vollgraf, Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms, arXiv, cs.LG/1708.07747\n" ] }, { "cell_type": "markdown", "id": "d6f6f620-a36c-4ac6-b26e-a49e77ae7a82", "metadata": { "user_expressions": [] }, "source": [ "## 2. Loading the data\n", "\n", "You make use of the MNIST or the Fashion-MINST dataset. **You get to choose one of them!** Both datasets consist of 70000 images of 28$\\times$28 pixels (each pixel has a gray value 0 - 255). The dataset can be downloaded from the internet (see code below). In the text I reference to the images as numbers, if you do the fashion dataset, you can read here fashion items instead." ] }, { "cell_type": "code", "execution_count": null, "id": "5b3b3dcc-4f13-4a30-a3f4-aec9a5dda20e", "metadata": {}, "outputs": [], "source": [ "# importing the required modules\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# to get matplot figures render correctly in the notebook use:\n", "%matplotlib inline " ] }, { "cell_type": "code", "execution_count": null, "id": "37b2519c-448c-4d77-a9e3-514e6e6a3b79", "metadata": {}, "outputs": [], "source": [ "# Load data from https://www.openml.org/\n", "\n", "from sklearn.datasets import fetch_openml\n", "\n", "# Pick either the numbers or the fashion database here:\n", "#X, y = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame=False, parser='auto')\n", "X, y = fetch_openml('Fashion-MNIST', version=1, return_X_y=True, as_frame=False)\n", "\n", "\n", "X = X.T # required as the rows should be the features and the columns the samples; shape (784, 70000).\n", "y = y.astype('int') # y has values as strings ('0', '1', ...'9') and we want integers (0, 1, ..., 9)\n", "\n", "# inspect shape.\n", "print(X.shape)\n", "print(y.shape)\n" ] }, { "cell_type": "markdown", "id": "cb86344f-77b4-4bc5-9061-497f50aa2abd", "metadata": {}, "source": [ "To have an idea of the data let's print a single sample" ] }, { "cell_type": "code", "execution_count": null, "id": "cb8c0c7c-680d-4db6-86e4-b310c0b8eb07", "metadata": {}, "outputs": [], "source": [ "sample = X[:, 1] # sample number 1\n", "label = y[1] # label of sample 1\n", "plt.imshow(sample.reshape(28,28)) # need to reshape to a square image of 28 by 28\n", "print(f'label = {label}')" ] }, { "cell_type": "markdown", "id": "a7807286-9d96-4fbc-b30b-4edf423e375d", "metadata": {}, "source": [ "## 3. Create the feature matrix X and labels matrix Y\n" ] }, { "cell_type": "code", "execution_count": null, "id": "477b8da9-5139-4982-9e44-e0a4633f8c6a", "metadata": {}, "outputs": [], "source": [ "# rescale feature matrix to values between 0 and 1\n", "'''YOUR CODE GOES HERE '''\n", "\n", "# create one-hot encoded Y matrix\n", "'''YOUR CODE GOES HERE '''" ] }, { "cell_type": "code", "execution_count": null, "id": "362360e3-30b0-4a52-ada7-3d8b1cfe0ed8", "metadata": {}, "outputs": [], "source": [ "# create a train, validation and test dataset\n", "# for the test data set take the first 5000 samples\n", "# for the train data set take sample 5001 to 60000\n", "# for the validation set take sample 60001 to 70000\n", "\n", "X_test = '''YOUR CODE GOES HERE '''\n", "X_train = '''YOUR CODE GOES HERE '''\n", "X_valid = '''YOUR CODE GOES HERE '''\n", "\n", "Y_test = '''YOUR CODE GOES HERE '''\n", "Y_train = '''YOUR CODE GOES HERE '''\n", "Y_valid = '''YOUR CODE GOES HERE '''\n" ] }, { "cell_type": "markdown", "id": "3912be91-33c3-4f2b-947f-43f6a142ebd4", "metadata": {}, "source": [ "## 4. The Neural Network\n", "\n", "The NN that you will make has the following layout (this is something that will work, but feel free to make changes if you like):\n", "\n", "- input layer ($l=0$): 784 nodes\n", "- hidden layer ($l=1$): 300 nodes; ReLu activation function\n", "- hidden layer ($l=2$): 100 nodes; ReLu activation function\n", "- output layer ($l=3$): 10 nodes; Softmax activation function\n" ] }, { "cell_type": "markdown", "id": "91a2ea40-d343-413b-850f-ca28bb6224f7", "metadata": {}, "source": [ "### Define the activation functions\n", "\n", "Below you define the required activation functions and their derivatives." ] }, { "cell_type": "code", "execution_count": null, "id": "2ad24e42-6802-4977-9980-2e7bbb070d30", "metadata": {}, "outputs": [], "source": [ "# define some activation functions\n", "\n" ] }, { "cell_type": "markdown", "id": "0bef5734-f28a-4f8b-84ec-69615694882f", "metadata": {}, "source": [ "### Initalize the weights and biases\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9b598ebf-81d0-4247-a97a-4331b4485d04", "metadata": {}, "outputs": [], "source": [ "# intialize the neural network\n", "rng = np.random.default_rng()\n", "\n", "# number of nodes in each layer\n", "'''YOUR CODE GOES HERE '''\n", "\n", "# input layer\n", "# no weights and biases\n", "\n", "# hidden layer 1 \n", "'''YOUR CODE GOES HERE '''\n", "\n", "# hidden layer 2 \n", "'''YOUR CODE GOES HERE '''\n", "\n", "# output layer \n", "'''YOUR CODE GOES HERE '''\n" ] }, { "cell_type": "markdown", "id": "72a3e77b-d0c5-4985-ab01-83ac16565f7b", "metadata": {}, "source": [ "## 5. Train the NN" ] }, { "cell_type": "markdown", "id": "1575b796-6773-406d-9b1d-29c6a5465db4", "metadata": {}, "source": [ "Firt you define three parameters that can be adjusted for optimal training.\n", "- **learning_rate**: determines to what extend we update the weights and biases in the gradient descent step\n", "- **no_epochs**: the number of times we pass the training data set through the network for training\n", "- **batch_size**: how many samples we pass through the network before doing a gradient descent update" ] }, { "cell_type": "code", "execution_count": null, "id": "bc699d56-c6d0-4aac-96c1-69e0a0499678", "metadata": {}, "outputs": [], "source": [ "# train settings\n", "'''YOUR CODE GOES HERE '''" ] }, { "cell_type": "markdown", "id": "c61cf0f3-a4f0-4bc1-8490-3c8a07751ef5", "metadata": {}, "source": [ "Below you implement the training of the network. As a start copy and paste your code from part 1. Note that we use the **Cross Entropy** (CE) loss function." ] }, { "cell_type": "code", "execution_count": null, "id": "98101ed7-939d-4109-9259-6493588088fe", "metadata": {}, "outputs": [], "source": [ "# perform the training\n", "\n", "'''YOUR CODE GOES HERE '''\n" ] }, { "cell_type": "markdown", "id": "5003e1b7-1f3e-4c54-86e4-76be40cc3d39", "metadata": {}, "source": [ "## 6. Analyze the result\n", "Make a plot of the training and validation loss and training and validation accuracy as function of the epoch." ] }, { "cell_type": "code", "execution_count": null, "id": "e3a2e92f-62ae-456b-aa55-921458320ab0", "metadata": {}, "outputs": [], "source": [ "# plot the loss and accuracy as function of the epoch number\n", "'''YOUR CODE GOES HERE '''" ] }, { "cell_type": "markdown", "id": "1794c29b-bae6-4c52-9b3c-25277a1a6d0d", "metadata": {}, "source": [ "Adapt the hyperparameters and/or NN layout to try to improve the result." ] }, { "cell_type": "markdown", "id": "87619684-6615-485b-9660-9f3193370428", "metadata": {}, "source": [ "Finally use the test set to check the performance of the model" ] }, { "cell_type": "code", "execution_count": null, "id": "3ba43646-1bef-49b1-8c63-2fc5311ec292", "metadata": {}, "outputs": [], "source": [ "# compute the accuracy and losses of the test set\n", "'''YOUR CODE GOES HERE '''" ] }, { "cell_type": "markdown", "id": "f746882f-babf-4f5f-8afb-02c45bd6af6f", "metadata": {}, "source": [ "Finally it is interesting to check a few samples and their predicted label" ] }, { "cell_type": "code", "execution_count": null, "id": "eb13294a-1cd4-499f-9ee3-274268d5c305", "metadata": {}, "outputs": [], "source": [ "# check the prediction of 10 random images\n", "'''YOUR CODE GOES HERE '''" ] }, { "attachments": { "a70fc836-93cc-41d2-88b0-3fed747ab4e0.png": { "image/png": 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LNT09XVdX19vb+/bbb584cSIlJcXtdhcWFv7iF7+orKwsLi7m8Xhr6SGCIAiFQqHRaPjm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\n72S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} }, "cell_type": "markdown", "id": "31f70a1c-cd91-470a-ad93-5d1da1bb1b3a", "metadata": { "user_expressions": [] }, "source": [ "# Neural Networks - The autoencoder **(5EC mandatory)**\n", "\n", "You are going to train a neural network that is able to generate a new picture of a any on the 0..9 numbers. The shape of the NN resembles that of a bow tie:\n", "\n", "- input layer ($l=0$): 784 nodes\n", "- hidden layer ($l=1$): 300 nodes; ReLu activation function (FIXED)\n", "- hidden layer ($l=2$): 100 nodes; ReLu activation function (FIXED) \n", "- hidden layer ($l=3$): 10 nodes; Softmax activation function (FIXED)\n", "- hidden layer ($l=4$): ? nodes; ? activation function\n", "- hidden layer ($l=5$): ? nodes; ? activation function\n", "- output layer ($l=6$): 784 nodes; ? activation function\n", "\n", "You can train it by passing pictures trough it, the outgoing picture should be almost identical. Think about what loss function would be most appropriate here. Use the weights of the previously trained NN for the left side of the 'bow tie'. This setup is also known as an **autoencoder**. Layers 1-3 function as the _encoder_, wheras the layers 4-6 are the _decoder_. It can be used to find a compressed version of your input data. In case the loss is zero, you have obtained a perfect compression.\n", "\n", "Once training is complete you can use the right hand side of the bow tie to generate a new image starting from the input vector, for example, a3 = np.zeros((10)), a3[3]=1 and see if you get a figure back that resembles a 2.\n", "\n", "**Deliverables:** \n", "1. A plot of 10 newly generated numbers with your network.\n", "2. An outputweights.npz file with all weights and biases of your filal Neural Network (encoder and decoder)\n", "3. A code to read in the outputweights.npz file and use the image generator\n", "\n", "_Deliverable 1 could look like:_\n", "\n", "![numbers_small.png](attachment:a70fc836-93cc-41d2-88b0-3fed747ab4e0.png)\n", "_It would be great if some of you manage to find a solution for the dark (missing) pixels!_ \n", "\n", "_Or in case of the fashion set:_\n", "\n", "![zalando.png](attachment:cdbe129b-208e-438f-8c21-6d92276b02e6.png)" ] }, { "cell_type": "code", "execution_count": null, "id": "efe0b719-b1b3-43c4-8391-838d4dde0d34", "metadata": {}, "outputs": [], "source": [ "# Setup the Network\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "c715bfa0-7ec1-4a83-b826-3534deb0129e", "metadata": {}, "outputs": [], "source": [ "# Train the decoder\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "7d5bcee5-ad48-4089-beb2-69114b633409", "metadata": {}, "outputs": [], "source": [ "# Test your image generation and improve your training procedure (hyperparameter tuning)\n", "\n" ] }, { "cell_type": "markdown", "id": "28a8a646-3e91-4841-a819-a3aebdf7142d", "metadata": { "user_expressions": [] }, "source": [ "### Deliverable 1 " ] }, { "cell_type": "code", "execution_count": null, "id": "9ce1d8c1-aea2-4640-83aa-4ae004dca33d", "metadata": {}, "outputs": [], "source": [ "# Prove that your network does what it should do and present your deliverable convincingly!\n", "\n" ] }, { "cell_type": "markdown", "id": "22e01826-5aa5-4a6f-a4e1-a965250f3ebb", "metadata": { "user_expressions": [] }, "source": [ "### Deliverable 2 " ] }, { "cell_type": "code", "execution_count": null, "id": "b8ac2496-8162-461e-9892-fcb580b5c05c", "metadata": {}, "outputs": [], "source": [ "## saves the obtained weights\n", "#outfile='totalweightsNN.npz'\n", "#np.savez(outfile, W1,...,b1,...)\n", "#npzfile = np.load(outfile)\n", "#npzfile.files\n", "#W1=npzfile['arr_0']\n", "#W1.shape\n" ] }, { "cell_type": "markdown", "id": "6e1ade51-6d11-4021-954c-53bb86621c17", "metadata": { "user_expressions": [] }, "source": [ "### Deliverable 3 " ] }, { "cell_type": "code", "execution_count": null, "id": "515a11fd-211c-43ea-9d30-f32ae5157187", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }