|
|
{ "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": 1, "id": "270932f3", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-04-11 16:44:42.299418: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", "2023-04-11 16:44:42.360104: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n" ] } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": 2, "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": 3, "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": 4, "id": "71e072b4", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-04-11 16:44:43.775141: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" ] } ], "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": 5, "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": 6, "id": "22c4124f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/60\n", "1/1 [==============================] - 0s 396ms/step - loss: 15.2606\n", "Epoch 2/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 12.7440\n", "Epoch 3/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 10.4745\n", "Epoch 4/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 8.4519\n", "Epoch 5/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 6.6738\n", "Epoch 6/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 5.1356\n", "Epoch 7/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 3.8306\n", "Epoch 8/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 2.7499\n", "Epoch 9/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.8823\n", "Epoch 10/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 1.2142\n", "Epoch 11/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7292\n", "Epoch 12/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4087\n", "Epoch 13/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2310\n", "Epoch 14/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1722\n", "Epoch 15/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2060\n", "Epoch 16/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.3049\n", "Epoch 17/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4419\n", "Epoch 18/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5909\n", "Epoch 19/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.7296\n", "Epoch 20/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.8423\n", "Epoch 21/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.9194\n", "Epoch 22/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.9578\n", "Epoch 23/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.9579\n", "Epoch 24/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.9233\n", "Epoch 25/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.8606\n", "Epoch 26/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.7777\n", "Epoch 27/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.6828\n", "Epoch 28/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.5839\n", "Epoch 29/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.4879\n", "Epoch 30/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.4003\n", "Epoch 31/60\n", "1/1 [==============================] - 0s 2ms/step - loss: 0.3253\n", "Epoch 32/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2652\n", "Epoch 33/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2208\n", "Epoch 34/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1917\n", "Epoch 35/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1766\n", "Epoch 36/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1731\n", "Epoch 37/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1785\n", "Epoch 38/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1899\n", "Epoch 39/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2046\n", "Epoch 40/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2202\n", "Epoch 41/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2345\n", "Epoch 42/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2460\n", "Epoch 43/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2537\n", "Epoch 44/60\n", "1/1 [==============================] - 0s 5ms/step - loss: 0.2572\n", "Epoch 45/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2565\n", "Epoch 46/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2520\n", "Epoch 47/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2444\n", "Epoch 48/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2346\n", "Epoch 49/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2234\n", "Epoch 50/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.2119\n", "Epoch 51/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.2010\n", "Epoch 52/60\n", "1/1 [==============================] - 0s 11ms/step - loss: 0.1912\n", "Epoch 53/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1831\n", "Epoch 54/60\n", "1/1 [==============================] - 0s 4ms/step - loss: 0.1771\n", "Epoch 55/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1730\n", "Epoch 56/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1709\n", "Epoch 57/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1705\n", "Epoch 58/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1713\n", "Epoch 59/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1730\n", "Epoch 60/60\n", "1/1 [==============================] - 0s 3ms/step - loss: 0.1751\n", "{'verbose': 1, 'epochs': 60, 'steps': 1}\n" ] } ], "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": 7, "id": "46615960", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Weight matrix shape: (1, 17)\n", "Bias vector shape: (17,)\n", "[[-0.2720451 0.25746062 0.00145817 -0.14928943 -0.18011677 -0.38295865\n", " -0.10688066 -0.08276322 -0.06117761 -0.415354 0.21152566 -0.015845\n", " -0.22856292 0.25203183 0.29251993 -0.08234903 0.6508041 ]]\n" ] } ], "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": 8, "id": "da12fc5b", "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1/1 [==============================] - 0s 61ms/step\n" ] }, { "data": { "image/png": "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 "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "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}
|
