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{ "cells": [ { "cell_type": "markdown", "id": "0b7d591a", "metadata": {}, "source": [ "\n", " An example of the minimzer usage in tensorflow\n", " the loss function is plotted and the result in terms of a line\n" ] }, { "cell_type": "code", "execution_count": null, "id": "270932f3", "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": "77cd99a8", "metadata": {}, "outputs": [], "source": [ "# Define the training data\n", "train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n", " 7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n", "train_Y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n", " 2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n" ] }, { "cell_type": "markdown", "id": "f39cbcd9", "metadata": {}, "source": [ "The input to the model is represented by the train_X \n", "Y_train represents the target or the truth values for the training data\n", "The model will recieve train_X and make predictions on the weights\n", "The difference between these predictions and the actual target values\n", "train_Y will be used to update the weights and minimize the loss function." ] }, { "cell_type": "code", "execution_count": null, "id": "ed8449c3", "metadata": {}, "outputs": [], "source": [ "# Define the model to a simple linear regression with only one dense layer and\n", "# no activation function for the first layer all train_X points are input\n", "\n", "# model = tf.keras.models.Sequential([\n", "# tf.keras.layers.Dense(1, input_shape=[1])\n", "#])" ] }, { "cell_type": "code", "execution_count": null, "id": "71e072b4", "metadata": {}, "outputs": [], "source": [ "# This model has 2 dense layers the first with relu activation\n", "# and the 2nd layer has 1 output unit and uses the default\n", "# linear activation function.\n", "\n", "model = tf.keras.models.Sequential([\n", " tf.keras.layers.Dense(17, activation='relu',input_shape=[1]),\n", " tf.keras.layers.Dense(1)\n", "])" ] }, { "cell_type": "code", "execution_count": null, "id": "5fabf184", "metadata": {}, "outputs": [], "source": [ "# different optimizer methods can be enabled\n", "\n", "model.compile(optimizer=tf.keras.optimizers.Adam(0.01), loss='mean_squared_error')\n", "#model.compile(optimizer=tf.keras.optimizers.SGD(0.01), loss='mean_squared_error')\n", "#model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.01), loss='mean_squared_error')\n", "#model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.01), loss='mean_squared_error')\n", "#model.compile(optimizer=tf.keras.optimizers.Ftrl(learning_rate=0.015), loss='mean_squared_error')" ] }, { "cell_type": "code", "execution_count": null, "id": "22c4124f", "metadata": {}, "outputs": [], "source": [ "# Train the model and access training parameters\n", "history = model.fit(train_X, train_Y, epochs=60)\n", "print(history.params)" ] }, { "cell_type": "code", "execution_count": null, "id": "46615960", "metadata": {}, "outputs": [], "source": [ "# Get the weights of the Dense layer\n", "weights = model.layers[0].get_weights()\n", "# Print the weight matrix and bias vector\n", "print('Weight matrix shape:', weights[0].shape)\n", "print('Bias vector shape:', weights[1].shape)\n", "print (weights[0])" ] }, { "cell_type": "code", "execution_count": null, "id": "da12fc5b", "metadata": {}, "outputs": [], "source": [ "# Plot the loss function\n", "plt.plot(history.history['loss'])\n", "plt.title(\"Loss Function\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "plt.show()\n", "\n", "# Plot the input data and the predicted values\n", "plt.plot(train_X, train_Y, 'ro', label=\"Original Data\")\n", "plt.plot(train_X, model.predict(train_X), label=\"Predicted\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "60417d5f", "metadata": {}, "outputs": [], "source": [] } ], "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}
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