{ "cells": [ { "cell_type": "code", "execution_count": 9, "id": "ff2a1ed8-2d8c-4189-96c8-045c735dfdf3", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from sklearn.preprocessing import MinMaxScaler\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "aabd8481-c171-46f0-8f8d-586a2850e0dc", "metadata": {}, "source": [ "Neural networks are great at learning trends in both large and small data sets. However, be aware of the dangers of overfitting, which are more evident in projects where small data sets are used. Overfitting is when an algorithm is trained and modeled to fit a set of data points too closely so that it does not generalize well to new data points.\n", "\n", "Often, overfitting machine learning models have very high accuracy on the data sets they are trained on, but the goal is usually to predict new data points as precisely as possible. To make sure that the model is evaluated based on how good it is to predict new data points, and not how well it is modeled to the current ones, it is common to split the datasets into one training set and one test set (and sometimes a validation set)." ] }, { "cell_type": "markdown", "id": "dc33e112-6471-4606-b905-40894c046e15", "metadata": {}, "source": [ "For this simple neural network, we will classify 1x3 vectors with 10 as the first element. Input and output training and test sets are created using NumPy’s array function, and input_pred is created to test a prediction function that will be defined later. Both the training and the test data are comprised of six samples with three features each, and since the output is given, we understand that this is an example of supervised learning." ] }, { "cell_type": "code", "execution_count": 19, "id": "c3a4eab4-8c95-4dd3-a551-1c0e455c467e", "metadata": {}, "outputs": [], "source": [ "input_train = np.array([[0, 1, 0], [0, 1, 1], [0, 0, 0], \n", " [10, 0, 0], [10, 1, 1], [10, 0, 1]])\n", "output_train = np.array([[0], [0], [0], [1], [1], [1]])\n", "\n", "input_pred = np.array([1, 1, 0])\n", "\n", "input_test = np.array([[1, 1, 1], [10, 0, 1], [0, 1, 10], \n", " [10, 1, 10], [0, 0, 0], [0, 1, 1]])\n", "output_test = np.array([[0], [1], [0], [1], [0], [0]])" ] }, { "cell_type": "code", "execution_count": 20, "id": "44575e28-05a4-4658-8ef2-b5df03cbec4d", "metadata": {}, "outputs": [], "source": [ "scaler = MinMaxScaler()\n", "input_train_scaled = scaler.fit_transform(input_train)\n", "output_train_scaled = scaler.fit_transform(output_train)\n", "input_test_scaled = scaler.fit_transform(input_test)\n", "output_test_scaled = scaler.fit_transform(output_test)" ] }, { "cell_type": "markdown", "id": "7e638ab2-aedb-4957-8ea6-5b7d599efcec", "metadata": {}, "source": [ "Many machine learning models are not able to understand the difference between e.g. units, and will naturally apply more weight to features of high magnitudes. This can destroy an algorithm’s ability to predict new data points well. Further, training machine learning models with features of high magnitude will be slower than necessary, at least if gradient descent is used. This is because gradient descent converges faster when the input values are in approximately the same range.\n", "\n", "In our training and test data sets the values are in a relatively small range, and it might therefore not be necessary to do feature scaling. Doing feature scaling is extremely easy in Python due to the Scikit-learn package, and its MinMaxScaler class. Simply create a MinMaxScaler object, and use the fit_transform function with your non-scaled data as input, and the function will return the same data scaled.\n", "\n", "One of the easiest ways to get familiar with all the elements of a neural network is to create a neural network class. Such a class should include all the variables and functions that will be necessary for the neural network to work properly, the power of object-oriented programming." ] }, { "cell_type": "code", "execution_count": 21, "id": "405d7555-1f60-4ed9-8e57-c48dcaed0822", "metadata": {}, "outputs": [], "source": [ "class NeuralNetwork():\n", " def __init__(self, ):\n", " self.inputSize = 3\n", " self.outputSize = 1\n", " self.hiddenSize = 3\n", "\n", " self.W1 = np.random.rand(self.inputSize, self.hiddenSize)\n", " self.W2 = np.random.rand(self.hiddenSize, self.outputSize)\n", "\n", " self.error_list = []\n", " self.limit = 0.5\n", " self.true_positives = 0\n", " self.false_positives = 0\n", " self.true_negatives = 0\n", " self.false_negatives = 0\n", "\n", " def forward(self, X):\n", " self.z = np.matmul(X, self.W1)\n", " self.z2 = self.sigmoid(self.z)\n", " self.z3 = np.matmul(self.z2, self.W2)\n", " o = self.sigmoid(self.z3)\n", " return o\n", "\n", " def sigmoid(self, s):\n", " return 1 / (1 + np.exp(-s))\n", "\n", " def sigmoidPrime(self, s):\n", " return s * (1 - s)\n", "\n", " def backward(self, X, y, o):\n", " self.o_error = y - o\n", " self.o_delta = self.o_error * self.sigmoidPrime(o)\n", " self.z2_error = np.matmul(self.o_delta,\n", " np.matrix.transpose(self.W2))\n", " self.z2_delta = self.z2_error * self.sigmoidPrime(self.z2)\n", " self.W1 += np.matmul(np.matrix.transpose(X), self.z2_delta)\n", " self.W2 += np.matmul(np.matrix.transpose(self.z2),\n", " self.o_delta)\n", "\n", " def train(self, X, y, epochs):\n", " for epoch in range(epochs):\n", " o = self.forward(X)\n", " self.backward(X, y, o)\n", " self.error_list.append(np.abs(self.o_error).mean())\n", "\n", " def predict(self, x_predicted):\n", " return self.forward(x_predicted).item()\n", "\n", " def view_error_development(self):\n", " plt.plot(range(len(self.error_list)), self.error_list)\n", " plt.title('Mean Sum Squared Loss')\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Loss')\n", "\n", " def test_evaluation(self, input_test, output_test):\n", " for i, test_element in enumerate(input_test):\n", " if self.predict(test_element) > self.limit and \\\n", " output_test[i] == 1:\n", " self.true_positives += 1\n", " if self.predict(test_element) < self.limit and \\\n", " output_test[i] == 1:\n", " self.false_negatives += 1\n", " if self.predict(test_element) > self.limit and \\\n", " output_test[i] == 0:\n", " self.false_positives += 1\n", " if self.predict(test_element) < self.limit and \\\n", " output_test[i] == 0:\n", " self.true_negatives += 1\n", " print('True positives: ', self.true_positives,\n", " '\\nTrue negatives: ', self.true_negatives,\n", " '\\nFalse positives: ', self.false_positives,\n", " '\\nFalse negatives: ', self.false_negatives,\n", " '\\nAccuracy: ',\n", " (self.true_positives + self.true_negatives) /\n", " (self.true_positives + self.true_negatives +\n", " self.false_positives + self.false_negatives))" ] }, { "attachments": { 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" } }, "cell_type": "markdown", "id": "dfbce211-e6f1-4351-a6cf-355a11bca384", "metadata": {}, "source": [ "The __init__ function is called on when we create a class in Python so that the variables are initialized properly. For our test, we will use the following net, and initialize the parameters to match this diagram.\n", "\n", "![Screen Shot 2024-05-22 at 3.59.19 PM.png](attachment:13237678-5f05-4ae8-a52d-477bd2eecb97.png)" ] }, { "cell_type": "code", "execution_count": 22, "id": "86534313-e1ba-4e81-bef9-bd1a825c7040", "metadata": {}, "outputs": [], "source": [ "def __init__(self, ):\n", " self.inputSize = 3\n", " self.outputSize = 1\n", " self.hiddenSize = 3\n", "\n", " self.W1 = torch.randn(self.inputSize, self.hiddenSize)\n", " self.W2 = torch.randn(self.hiddenSize, self.outputSize)\n", "\n", " self.error_list = []\n", " self.limit = 0.5\n", " self.true_positives = 0\n", " self.false_positives = 0\n", " self.true_negatives = 0\n", " self.false_negatives = 0" ] }, { "cell_type": "markdown", "id": "91c0c60d-b6df-4cc0-8c3c-df3133b21237", "metadata": {}, "source": [ "Our neural network has three input nodes, three nodes in the hidden layer, and one output node. The above __init__ function initializes variables describing the size of the neural network. inputSize is the number of input nodes, which should be equal to the number of features in our input data. outputSize is equal to the number of output nodes, and hiddenSize describes the number of nodes in the hidden layer. Further, there will be weights between the different nodes in our network that will be adjusted during training." ] }, { "cell_type": "markdown", "id": "4ed809a5-518f-407a-8e5c-a57da275fdb8", "metadata": {}, "source": [ "We create a forward pass function whose purpose is to iterate forward through the different layers of the neural network to predict output for that particular epoch. Then, looking at the difference between the predicted output and the actual output, the weights will be updated during backward propagation." ] }, { "cell_type": "raw", "id": "a52e9e85-a43d-46a8-a92a-2cbdf4d63fc7", "metadata": {}, "source": [ "def forward(self, X):\n", " self.z = np.matmul(X, self.W1)\n", " self.z2 = self.sigmoid(self.z)\n", " self.z3 = np.matmul(self.z2, self.W2)\n", " o = self.sigmoid(self.z3)\n", " return o" ] }, { "cell_type": "markdown", "id": "4ca57e59-0fe7-499a-ac34-6d559208c95b", "metadata": {}, "source": [ "To calculate the values at each node in every layer, the values at the nodes in the previous layer will be matrix multiplied with the applicable weights before a non-linear activation function (the sigmoid function) will be applied to widen the possibilities for the final output function." ] }, { "cell_type": "markdown", "id": "0d09f78c-eef9-4e7f-a0dc-27e40e6f8ebe", "metadata": {}, "source": [ "Backpropagation is the process that updates the weights for the different nodes in the neural network and hence decides their importance." ] }, { "cell_type": "code", "execution_count": 23, "id": "0a733307-b543-429c-86fc-f4c9ce1b78d0", "metadata": {}, "outputs": [], "source": [ "def backward(self, X, y, o):\n", " self.o_error = y - o\n", " self.o_delta = self.o_error * self.sigmoidPrime(o)\n", " self.z2_error = np.matmul(self.o_delta,\n", " np.matrix.transpose(self.W2))\n", " self.z2_delta = self.z2_error * self.sigmoidPrime(self.z2)\n", " self.W1 += np.matmul(np.matrix.transpose(X), self.z2_delta)\n", " self.W2 += np.matmul(np.matrix.transpose(self.z2),\n", " self.o_delta)" ] }, { "cell_type": "markdown", "id": "4d8eaa3f-4b04-42dc-b5f8-58086901e1fb", "metadata": {}, "source": [ "The output error from the output layer is calculated as the difference between the predicted output from forwarding propagation and the actual output. Then, this error is multiplied with the sigmoid prime in order to run gradient descent, before the entire process is repeated until the input layer is reached. Finally, the weights between the different layers are updated." ] }, { "cell_type": "markdown", "id": "4690e40c-9071-493d-8a65-31bdc634664f", "metadata": {}, "source": [ "During training, the algorithm will run forward and backward pass and thereby updating the weights as many times as there are epochs. This is necessary in order to end up with the most precise weights.\n", "\n", "In addition to running forward and backward pass, we save the mean absolute error (MAE) to an error list so that we can later observe how the mean absolute error develops during the course of the training." ] }, { "cell_type": "code", "execution_count": 24, "id": "62220006-7a3e-4b1f-adae-e836cc199d30", "metadata": {}, "outputs": [], "source": [ "def train(self, X, y, epochs):\n", " for epoch in range(epochs):\n", " o = self.forward(X)\n", " self.backward(X, y, o)\n", " self.error_list.append(np.abs(self.o_error).mean())" ] }, { "cell_type": "markdown", "id": "200d3ffd-205b-4e6f-84f9-f2524392f26c", "metadata": {}, "source": [ "After the weights are fine-tuned during training, the algorithm is ready to predict the output for new data points. This is done through a single iteration of forwarding pass. The predicted output will be a number that hopefully will be quite close to the actual output." ] }, { "cell_type": "code", "execution_count": 25, "id": "da061c23-0d8b-4f67-a917-b048aa8a1603", "metadata": {}, "outputs": [], "source": [ "def predict(self, x_predicted):\n", " return self.forward(x_predicted).item()" ] }, { "cell_type": "code", "execution_count": 26, "id": "5651a781-d78a-4b3d-b56a-a85a1e98ac54", "metadata": {}, "outputs": [], "source": [ "def view_error_development(self):\n", " plt.plot(range(len(self.error_list)), self.error_list)\n", " plt.title('Mean Sum Squared Loss')\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Loss')" ] }, { "cell_type": "markdown", "id": "96522dac-e37d-47b7-9594-16166fcba1ed", "metadata": {}, "source": [ "The number of true positives, false positives, true negatives, and false negatives describes the quality of a machine learning classification algorithm. After training the neural network, the weights should be updated so that the algorithm is able to accurately predict new data points. " ] }, { "cell_type": "code", "execution_count": 27, "id": "3469b405-85c3-4358-b479-a0d1800e3a06", "metadata": {}, "outputs": [], "source": [ "def test_evaluation(self, input_test, output_test):\n", " for i, test_element in enumerate(input_test):\n", " if self.predict(test_element) > self.limit and \\\n", " output_test[i] == 1:\n", " self.true_positives += 1\n", " if self.predict(test_element) < self.limit and \\\n", " output_test[i] == 1:\n", " self.false_negatives += 1\n", " if self.predict(test_element) > self.limit and \\\n", " output_test[i] == 0:\n", " self.false_positives += 1\n", " if self.predict(test_element) < self.limit and \\\n", " output_test[i] == 0:\n", " self.true_negatives += 1\n", " print('True positives: ', self.true_positives,\n", " '\\nTrue negatives: ', self.true_negatives,\n", " '\\nFalse positives: ', self.false_positives,\n", " '\\nFalse negatives: ', self.false_negatives,\n", " '\\nAccuracy: ',\n", " (self.true_positives + self.true_negatives) /\n", " (self.true_positives + self.true_negatives +\n", " self.false_positives + self.false_negatives))" ] }, { "cell_type": "markdown", "id": "dc999953-9941-41ea-8fa7-3bdc4b0e6d4f", "metadata": {}, "source": [ "ok, do it." ] }, { "cell_type": "code", "execution_count": 31, "id": "fe6e4773-a706-48f7-b355-da5b45ff2424", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True positives: 2 \n", "True negatives: 4 \n", "False positives: 0 \n", "False negatives: 0 \n", "Accuracy: 1.0\n", "[1 1 0] 0.8886811541130609\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "NN = NeuralNetwork()\n", "NN.train(input_train_scaled, output_train_scaled, 200)\n", "\n", "NN.view_error_development()\n", "NN.test_evaluation(input_test_scaled, output_test_scaled)\n", "\n", "print(input_pred,NN.predict(input_pred))" ] }, { "cell_type": "code", "execution_count": null, "id": "40a9c16b-0c69-4d50-bc2a-296af66a298f", "metadata": {}, "outputs": [], "source": [ "# https://github.com/henrysky/astroNN/blob/master/demo_tutorial/galaxy10/Galaxy10_Tutorial.ipynb" ] } ], "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }