From 2cb145e74c162c0686d8e3d026bfc43115c88a16 Mon Sep 17 00:00:00 2001 From: Joerg Marks Date: Fri, 31 Mar 2023 17:34:03 +0200 Subject: [PATCH] Update --- ...asics_ex_1_display_Cifar10_Greyscale.ipynb | 154 +++++++++++ ...ics_tf_binary_classification_example.ipynb | 247 ++++++++++++++++++ .../03_ml_basics_tf_mlp_mnist_digits.ipynb | 240 +++++++++++++++++ slides/03_ml_intro.pdf | Bin 2444717 -> 2290296 bytes 4 files changed, 641 insertions(+) create mode 100644 notebooks/03_ml_basics_ex_1_display_Cifar10_Greyscale.ipynb create mode 100644 notebooks/03_ml_basics_tf_binary_classification_example.ipynb create mode 100644 notebooks/03_ml_basics_tf_mlp_mnist_digits.ipynb diff --git a/notebooks/03_ml_basics_ex_1_display_Cifar10_Greyscale.ipynb b/notebooks/03_ml_basics_ex_1_display_Cifar10_Greyscale.ipynb new file mode 100644 index 0000000..430c38d --- /dev/null +++ b/notebooks/03_ml_basics_ex_1_display_Cifar10_Greyscale.ipynb @@ -0,0 +1,154 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "50680cbc", + "metadata": {}, + "source": [ + "Read/load the cifar10 dataset using tf.keras.datasets\n", + "- Display the first 25 images\n", + "- Convert them to greyscale images by reducing the 3 colors (r,g,b) to \n", + " one greyscale\n", + " using the formula gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\n", + "- Display the first 25 images in greyscale" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2dc5ea2f", + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e846355a", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the CIFAR-10 dataset\n", + "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85a493d2", + "metadata": {}, + "outputs": [], + "source": [ + "# In case of special pictures\n", + "selectPicture = -1 # -1 for all or number for a class starting from 0\n", + "\n", + "# Define a list of class names for CIFAR-10\n", + "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4eacb734", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the indices of images with a special label in the training set\n", + "special_indices = np.where(y_train == selectPicture)[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be2d0cd2", + "metadata": {}, + "outputs": [], + "source": [ + "# Display the first 25 images in the training set\n", + "plt.figure(figsize=(10,10))\n", + "for i in range(25):\n", + " plt.subplot(5, 5, i+1)\n", + " if selectPicture == -1 :\n", + " plt.imshow(x_train[i]) # all images\n", + " plt.title(class_names[y_train[i][0]])\n", + " else : \n", + " plt.imshow(x_train[special_indices[i]]) # special Picture only\n", + " plt.title(class_names[selectPicture]) \n", + " plt.xticks([])\n", + " plt.yticks([])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "938e632d", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert images to grayscale\n", + "train_images_gray = np.dot(x_train[..., :3], [0.2989, 0.5870, 0.1140])\n", + "test_images_gray = np.dot(x_test[..., :3], [0.2989, 0.5870, 0.1140])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96333686", + "metadata": {}, + "outputs": [], + "source": [ + "# Normalize pixel values to [0, 1]\n", + "train_images_gray = train_images_gray / 255.0\n", + "test_images_gray = test_images_gray / 255.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "64d3d2ae", + "metadata": {}, + "outputs": [], + "source": [ + "# Display the first 25 images in the training set\n", + "plt.figure(figsize=(10,10))\n", + "for i in range(25):\n", + " plt.subplot(5, 5, i+1)\n", + " if selectPicture == -1 :\n", + " plt.imshow(train_images_gray[i],cmap='gray') # all images\n", + " plt.title(class_names[y_train[i][0]])\n", + " else : \n", + " plt.imshow(train_images_gray[special_indices[i]],cmap='gray') # special Picture only\n", + " plt.title(class_names[selectPicture]) \n", + " plt.xticks([])\n", + " plt.yticks([])\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/03_ml_basics_tf_binary_classification_example.ipynb b/notebooks/03_ml_basics_tf_binary_classification_example.ipynb new file mode 100644 index 0000000..7802f91 --- /dev/null +++ b/notebooks/03_ml_basics_tf_binary_classification_example.ipynb @@ -0,0 +1,247 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "344b183c", + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# train a simple TensorFlow model to perform binary classification on a generated\n", + "# 2-dimensional dataset \n", + "# 02/2023\n", + "# " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92c9d0a1", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3814ea1d", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate toy data\n", + "np.random.seed(4321)\n", + "n_samples = 1000" + ] + }, + { + "cell_type": "markdown", + "id": "84d8bc5e", + "metadata": {}, + "source": [ + "machine learning algorithms need data close to 1 , this generated here, it is not needed to normalize data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1d437df", + "metadata": {}, + "outputs": [], + "source": [ + "class1_data = np.random.multivariate_normal([-1., -1.], [[1., 0.], [0., 1.]], n_samples)\n", + "class2_data = np.random.multivariate_normal([1.0, 1.0], [[1., 0.], [0., 1.]], n_samples)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6bbccf56", + "metadata": {}, + "outputs": [], + "source": [ + "# the data is merged together and the toy labels are asigned as [1, 0] and [0,1]\n", + "train_data = np.concatenate([class1_data, class2_data])\n", + "toy_labels = np.zeros(train_data.shape)\n", + "toy_labels[:n_samples, 0] = 1\n", + "toy_labels[n_samples:, 1] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f8dd0511", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the input data with points colored according to their labels\n", + "plt.scatter(class1_data[:, 0], class1_data[:, 1], color='red')\n", + "plt.scatter(class2_data[:, 0], class2_data[:, 1], color='blue')\n", + "plt.title(\"Input data with points colored according to their labels\")\n", + "plt.xlabel(\"Feature 1\")\n", + "plt.ylabel(\"Feature 2\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "75fb1af9", + "metadata": {}, + "source": [ + "Build a model, the sequential model is a linear stack of pre-made layers. In a Dense layer all neueral network layer is connected with all other layers. Here we have 32 nodes with input_shape 2 with means 2 dimensional data. The activation is 'relu' (rectified linear unit)\n", + "Softmax maps the output of a model to probability distributions of the 2 classes. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad6dcef9", + "metadata": {}, + "outputs": [], + "source": [ + "model = tf.keras.models.Sequential([\n", + " tf.keras.layers.Dense(32, activation='relu', input_shape=(2,)),\n", + " tf.keras.layers.Dense(32, activation='relu'),\n", + " tf.keras.layers.Dense(2, activation='softmax')\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "a9c38493", + "metadata": {}, + "source": [ + "Adam optimizer is a gradient-based optimization algorithm for updating the weights in \n", + "a neural network. The leanrning rate depends on the first and second moments of the \n", + "gradients of the loss function with respect to the weights\n", + "The loss function is BinaryCrossentropy since we have 2 classes of data\n", + "The accuracy metric measures the percentage of instances where the model \n", + "correctly predicted the class label and it can be computed as the number of correct\n", + "predictions divided by the total number of instances in the test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f6dc4ef", + "metadata": {}, + "outputs": [], + "source": [ + "model.compile(optimizer='adam',\n", + "# loss=tf.keras.losses.CategoricalCrossentropy(),\n", + " loss=tf.keras.losses.BinaryCrossentropy(),\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "id": "a6397c0f", + "metadata": {}, + "source": [ + "The object history contains loss and accuracy from the training process\n", + "The model is trained by dividing the entire training data into smaller batches\n", + "of a specified size, updating the model's parameters after each batch.\n", + "The batch_size parameter determines the number of samples to be used in each batch. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b98e46ba", + "metadata": {}, + "outputs": [], + "source": [ + "history = model.fit(train_data, toy_labels, epochs=20, batch_size=32, verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "806497d2", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot loss and accuracy\n", + "plt.figure(figsize=(12, 4))\n", + "\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(history.history['loss'])\n", + "plt.title('Loss')\n", + "plt.xlabel('Epoch')\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(history.history['accuracy'])\n", + "plt.title('Accuracy')\n", + "plt.xlabel('Epoch')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca6da322", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot data points and decision boundary\n", + "x_min, x_max = train_data[:, 0].min() - .5, train_data[:, 0].max() + .5\n", + "y_min, y_max = train_data[:, 1].min() - .5, train_data[:, 1].max() + .5\n", + "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02))\n", + "# creating a 2D grid from the arrays xx and yy which is the area of our inputs \n", + "grid = np.c_[xx.ravel(), yy.ravel()]\n", + "# get the predicted class probabilities for each data point in the grid.\n", + "# The result Z is an array with shape (n_samples, n_classes) where n_samples\n", + "# is the number of data points in the grid and n_classes is the number of\n", + "# classes in the toy_labels. Z contains the predicted class probabilities\n", + "# for each data point in the grid.\n", + "Z = model.predict(grid)\n", + "# The line Z = np.argmax(Z, axis=1) is used to convert the predicted probabilities\n", + "# into class labels.\n", + "Z = np.argmax(Z, axis=1)\n", + "# reshaped Z variable is used to create the contour plot of the model's predictions \n", + "# on the grid.\n", + "Z = Z.reshape(xx.shape)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54c02602", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(8, 8))\n", + "plt.contourf(xx, yy, Z, cmap=plt.cm.RdBu, alpha=.8)\n", + "plt.scatter(train_data[:, 0], train_data[:, 1], c=np.argmax(toy_labels, axis=1), cmap=plt.cm.RdBu)\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/03_ml_basics_tf_mlp_mnist_digits.ipynb b/notebooks/03_ml_basics_tf_mlp_mnist_digits.ipynb new file mode 100644 index 0000000..c806f67 --- /dev/null +++ b/notebooks/03_ml_basics_tf_mlp_mnist_digits.ipynb @@ -0,0 +1,240 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "13fa2f64", + "metadata": {}, + "outputs": [], + "source": [ + "# In this example, we used the TensorFlow library to load the MNIST data,\n", + "# define an MLP model with three dense layers, compile the model, train it\n", + "# for 10 epochs, evaluate it on the test set, and make predictions on\n", + "# the test set. Finally, we plot some examples of the predictions made\n", + "# by the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c4405f8", + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e7e58fb", + "metadata": {}, + "outputs": [], + "source": [ + "# Normalize the pixel values to be between 0 and 1\n", + "x_train = x_train / 255\n", + "x_test = x_test / 255" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d8a7370", + "metadata": {}, + "outputs": [], + "source": [ + "# Flatten the 2D images into 1D arrays\n", + "x_train = x_train.reshape(x_train.shape[0], -1)\n", + "x_test = x_test.reshape(x_test.shape[0], -1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2df0f54", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the labels into one-hot encoded arrays\n", + "y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)\n", + "y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2b249ea", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the model\n", + "# The number of parameters depends on the shapes and sizes of the layers.\n", + "# In the given model, the first layer Dense(512, activation='relu',\n", + "# input_shape=(784,)) has 784 input nodes and 512 output nodes. Therefore,\n", + "# the number of parameters in this layer would be (784 * 512) + 512 = 401920,\n", + "# where the +512 term is for the bias terms.\n", + "# The second layer also has 512 input nodes and 512 output nodes, which makes\n", + "# 512512 = 262,144 parameters. The third and last layer has 512 input nodes\n", + "# and 10 output nodes, which makes 512*10 = 5,120 parameters.\n", + "model = tf.keras.models.Sequential()\n", + "model.add(tf.keras.layers.Dense(512, activation='relu', input_shape=(784,)))\n", + "model.add(tf.keras.layers.Dense(512, activation='relu'))\n", + "model.add(tf.keras.layers.Dense(10, activation='softmax'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bab8730a", + "metadata": {}, + "outputs": [], + "source": [ + "# over come overfitting by regularization\n", + "#model = tf.keras.models.Sequential([\n", + "# tf.keras.layers.Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001),input_shape=(784,)),\n", + "# tf.keras.layers.Dense(64, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001)),\n", + "# tf.keras.layers.Dense(10, activation='softmax')\n", + "#])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3223c61", + "metadata": {}, + "outputs": [], + "source": [ + "# Compile the model\n", + "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51e7758f", + "metadata": {}, + "outputs": [], + "source": [ + "# Train the model and record the history\n", + "history = model.fit(x_train, y_train, epochs=10, batch_size=64, validation_data=(x_test, y_test))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e3240069", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the weights of the Dense layer\n", + "# plot the weights as a heatmap or image, where the weights are represented\n", + "# as pixel values.\n", + "# model.layers[2].get_weights()[0] returns only the weights of the third\n", + "# layer. If you wanted to get the biases, you would use\n", + "# model.layers[2].get_weights()[1].\n", + "dense_weights = model.layers[2].get_weights()[0]\n", + "\n", + "# Plot the weights as a heatmap\n", + "plt.imshow(dense_weights, cmap='coolwarm')\n", + "plt.colorbar()\n", + "plt.title('weights in the output layer')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13118686", + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate the model on the test set\n", + "test_loss, test_acc = model.evaluate(x_test, y_test)\n", + "print('Test accuracy:', test_acc)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40f35fe5", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot loss and accuracy\n", + "plt.figure(figsize=(12, 4))\n", + "\n", + "# Plot the loss and accuracy for training and validation data\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(history.history['loss'], label='training loss')\n", + "plt.plot(history.history['val_loss'], label='validation loss')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.legend()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(history.history['accuracy'])\n", + "plt.plot(history.history['val_accuracy'])\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "plt.legend()\n", + "\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6176ce1e", + "metadata": {}, + "outputs": [], + "source": [ + "# Make predictions on the test set\n", + "y_pred = model.predict(x_test)\n", + "y_pred = np.argmax(y_pred, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3635ded5", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot some examples from the test set and their predictions\n", + "fig, axes = plt.subplots(4, 4, figsize=(14, 14))\n", + "for i, ax in enumerate(axes.ravel()):\n", + " ax.matshow(x_test[i].reshape(28, 28), cmap='gray')\n", + " ax.set_title(\"True: %d\\nPredict: %d\" % (np.argmax(y_test[i]), y_pred[i]))\n", + " ax.axis(\"off\")\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.8.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/slides/03_ml_intro.pdf b/slides/03_ml_intro.pdf index ecc4d176fa0899bc58280e1c84c3b9d364dd05b9..a6adb1045b6aa4f9f3f7239fa3487a4a2958e12a 100644 GIT binary patch delta 101594 zcmY(pWmsInwk?VT2ol`gq0!*(9^BpC9fCGN0|a+>4;tLvEfCz@A-Ka^*=L`7@8cgI ztgc$K=9ojOD>ITHA9f=l1A-xgp@N}MhgBEj2w&-j2es< zj2?^;j2Vm-j2(;j2}!8Oc+cQOdL#7Pue>O#vgH@AG(~ZPW=Tb-|EdQj7l0e zL&gP-GTbv99**(B8}d}vU#Q`hbyq1d`wUL?XZm@S-$|aX@#Dr;(*UD`dnXf2UwWh= 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