{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A single layer ANN exercise"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this python notebook you should:\n",
    "1. Import a dataset from a text file\n",
    "2. Split the dataset into input and ouput data\n",
    "3. Build a single layer ANN using tensorflow and keras\n",
    "4. Visualize the accuracy of the ANN\n",
    "5. What can you try to improve the accuracy of the ANN?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dataset description"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset we are interested in is stored in the file called 'Data_Exercise_1.txt'.\\\n",
    "The file contains 1000 points where the three first columns represent the input variables while the 3 last columns represents the ouput variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ANN description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ANN we want to build contains 3 input and 3 output variables.\\\n",
    "The activation functions should be defined as rectified linear units (relu).\\\n",
    "The output layer should be defined by a linear activation function.\\\n",
    "As a starting point the number of neurons should be set to 50.\\\n",
    "The number of epochs should be set to 50."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Import a dataset from a text file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "data = np.loadtxt('Data_Exercise_1.txt')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Split the dataset into input and ouput data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_data  = data[:,0:3] #De tre første kolonnene \n",
    "output_data = data[:,3:6] #De tre siste kolonnene"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Build a single layer ANN using tensorflow and keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0378   \n",
      "Epoch 2/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0162 \n",
      "Epoch 3/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0141 \n",
      "Epoch 4/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 750us/step - loss: 0.0116\n",
      "Epoch 5/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 995us/step - loss: 0.0104\n",
      "Epoch 6/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0096 \n",
      "Epoch 7/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0080 \n",
      "Epoch 8/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0073 \n",
      "Epoch 9/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0064 \n",
      "Epoch 10/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0059 \n",
      "Epoch 11/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0055 \n",
      "Epoch 12/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0046 \n",
      "Epoch 13/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0044 \n",
      "Epoch 14/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0044 \n",
      "Epoch 15/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0039 \n",
      "Epoch 16/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0036 \n",
      "Epoch 17/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0037 \n",
      "Epoch 18/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0030 \n",
      "Epoch 19/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0032 \n",
      "Epoch 20/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0030 \n",
      "Epoch 21/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0027 \n",
      "Epoch 22/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0024 \n",
      "Epoch 23/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0024 \n",
      "Epoch 24/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0026 \n",
      "Epoch 25/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0021 \n",
      "Epoch 26/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0020 \n",
      "Epoch 27/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0019 \n",
      "Epoch 28/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0018 \n",
      "Epoch 29/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0018 \n",
      "Epoch 30/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0015 \n",
      "Epoch 31/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 859us/step - loss: 0.0015\n",
      "Epoch 32/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0015 \n",
      "Epoch 33/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0012 \n",
      "Epoch 34/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0013     \n",
      "Epoch 35/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0012 \n",
      "Epoch 36/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0011     \n",
      "Epoch 37/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0011 \n",
      "Epoch 38/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0010     \n",
      "Epoch 39/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 8.7411e-04 \n",
      "Epoch 40/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 9.1933e-04\n",
      "Epoch 41/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 8.3242e-04 \n",
      "Epoch 42/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.3522e-04 \n",
      "Epoch 43/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 8.0194e-04 \n",
      "Epoch 44/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.7115e-04 \n",
      "Epoch 45/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.4779e-04 \n",
      "Epoch 46/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.1569e-04 \n",
      "Epoch 47/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.1275e-04 \n",
      "Epoch 48/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.1071e-04 \n",
      "Epoch 49/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.6674e-04 \n",
      "Epoch 50/50\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.0016e-04 \n",
      "Minimum error : 0.0006000015418976545, Maximum error: 0.028259925544261932\n"
     ]
    }
   ],
   "source": [
    "inputs = tf.keras.Input(shape=(3,)) # We define the number of inputs of the ANN\n",
    "#\n",
    "x = tf.keras.layers.Dense(50, activation='relu')(inputs) # This add a layer of of neurons with the relu activation function\n",
    "#\n",
    "outputs = tf.keras.layers.Dense(3, activation='linear')(x) # We define here the output layer with 3 ouput and the linear activation function\n",
    "#\n",
    "model = tf.keras.Model(inputs=inputs, outputs=outputs) # We setup the inputs and outputs of the ANN\n",
    "#\n",
    "model.compile(loss='mse', optimizer='adam') # We compile the ANN model specifying the loss metric and optimizer\n",
    "\n",
    "\n",
    "history = model.fit(input_data, output_data, epochs=50,verbose=1)\n",
    "print('Minimum error : {}, Maximum error: {}'.format(np.min(history.history['loss']),np.max(history.history['loss'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Visualize the accuracy of the ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction = model.predict(input_data)\n",
    "\n",
    "# Visualization\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 7)\n",
    "fig, axs = plt.subplots(1, 2)\n",
    "\n",
    "axs[0].semilogy(history.history['loss'], 'k', label='Training Loss')\n",
    "axs[0].set_xlabel('Epoch')\n",
    "axs[0].set_ylabel('Error')\n",
    "axs[0].legend()\n",
    "axs[0].grid()\n",
    "\n",
    "# Scatter plot for predictions\n",
    "for i in range(3):  # Plot for each output variable\n",
    "    axs[1].scatter(output_data[:, i], prediction[:, i], label=f'Output {i+1}')\n",
    "    \n",
    "axs[1].plot([np.min(output_data), np.max(output_data)], \n",
    "            [np.min(prediction), np.max(prediction)], \n",
    "            'r', label='Reference')\n",
    "axs[1].set_xlabel('Measured')\n",
    "axs[1].set_ylabel('Predicted')\n",
    "axs[1].legend()\n",
    "axs[1].grid()\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. What can you try to improve the accuracy of the ANN?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To improve the accuracy one would often train the model on more relevant data.\n",
    "\n",
    "Increase number of neurons\n",
    "\n",
    "Increase number of epochs"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}