Single_neural_node_activation_functions.ipynb

{
 "cells":[
  {
   "cell_type":"markdown",
   "source":[
    "**In this notebook we will cover Neurons and layers of an artificial neural modal** "
   ],
   "attachments":{
    
   },
   "metadata":{
    "datalore":{
     "node_id":"zKsYkSwScMHNXwO4VwQ3Sq",
     "type":"MD",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# let's start by importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense, Input\n",
    "from tensorflow.keras.losses import MeanSquaredError, BinaryCrossentropy\n",
    "from tensorflow.keras.activations import sigmoid\n",
    "from tensorflow.keras import Sequential\n",
    "\n",
    "import logging"
   ],
   "execution_count":39,
   "outputs":[
    
   ],
   "metadata":{
    "datalore":{
     "node_id":"1k7ZBl4twx8IdP0CbOTj5A",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# Let's taking housing pricing example\n",
    "\n",
    "X_train = np.array([[1.0], [2.0]], dtype=np.float32)           #(size in 1000 square feet)\n",
    "Y_train = np.array([[300.0], [500.0]], dtype=np.float32)       #(price in 1000s of dollars)\n",
    "\n",
    "fig, ax = plt.subplots(1,1)\n",
    "ax.scatter(X_train, Y_train, marker='x', c='r', label=\"Data Points\")\n",
    "ax.legend( fontsize='xx-large')\n",
    "ax.set_ylabel('Price (in 1000s of dollars)', fontsize='xx-large')\n",
    "ax.set_xlabel('Size (1000 sqft)', fontsize='xx-large')\n",
    "plt.show()"
   ],
   "execution_count":6,
   "outputs":[
    {
     "data":{
      "image\/png":[
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\n"
      ]
     },
     "metadata":{
      "image\/png":{
       
      }
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"gGxaFDld5IcdTpJ6XSbIvB",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# Let's working with an example of neurons without activation\n",
    "\n",
    "# Function implemented by a neuron with no activation function is same as that of a standard linear function f = w.x + b\n",
    "\n",
    "# now let's define a single layer with a single neuron\n",
    "\n",
    "linear_layer = tf.keras.layers.Dense(units=1, activation= 'linear')\n",
    "\n",
    "# now let's try the model on our example of housing price data\n",
    "\n",
    "layer_output = linear_layer(X_train)\n",
    "\n",
    "print(f\"Weight allocated is: {linear_layer.get_weights()[0]}\")\n",
    "print(f\"Bias allocated is: {linear_layer.get_weights()[1]}\")\n",
    "\n",
    "print(f\"Layer output is: {layer_output}\")\n",
    "\n",
    "# Note here that weight and bias are allocated randomly at initiation bias is always set to 0 whereas weights are set to small value"
   ],
   "execution_count":7,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "Weight allocated is: [[-0.48951006]]\n",
      "Bias allocated is: [0.]\n",
      "Layer output is: [[-0.48951006]\n",
      " [-0.9790201 ]]\n"
     ],
     "output_type":"stream"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"7bAa17dwcnGYWBOVJ8IO1i",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# now let's set the weight and bias to train the model on specific values\n",
    "\n",
    "set_w = np.array([[200]])\n",
    "set_b = np.array([100])\n",
    "\n",
    "# set_weights takes a list of numpy arrays\n",
    "linear_layer.set_weights([set_w,set_b])\n",
    "\n",
    "print(f\"Weight allocated is: {linear_layer.get_weights()[0]}\")\n",
    "print(f\"Bias allocated is: {linear_layer.get_weights()[1]}\")"
   ],
   "execution_count":12,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "Weight allocated is: [[200.]]\n",
      "Bias allocated is: [100.]\n"
     ],
     "output_type":"stream"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"03tg6jP983IqmzgwkyM3at",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# now let's compute our model output from the new parameters\n",
    "layer_output = linear_layer(X_train)\n",
    "print(f\"Tensor flow linear layer with single node on trained housing data produced: {layer_output}\")"
   ],
   "execution_count":13,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "Tensor flow linear layer with single node on trained housing data produced: [[300.]\n",
      " [500.]]\n"
     ],
     "output_type":"stream"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"y17P5bIrh47joRv967bZb4",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# now let's compare the output above from the linear function we saw earlier f = w.x + b\n",
    "\n",
    "linear_fn_output = np.dot(X_train[0], set_w[0]) + set_b\n",
    "\n",
    "print(f\"Linear function with single input feature on trained housing data produced: {linear_fn_output}\")"
   ],
   "execution_count":14,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "Linear function with single input feature on trained housing data produced: [300.]\n"
     ],
     "output_type":"stream"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"CId48VCOjKawQHyfdKR2aU",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# now let's look at implementation of the activation function that follows sigmoid function principals\n",
    "\n",
    "# let's define sample data for classification model training\n",
    "\n",
    "X_train = np.array([0., 1, 2, 3, 4, 5], dtype=np.float32).reshape(-1,1)  # 2-D Matrix\n",
    "Y_train = np.array([0,  0, 0, 1, 1, 1], dtype=np.float32).reshape(-1,1)  # 2-D Matrix\n",
    "\n",
    "# now let's create an array of all positive and negative indexes from our output trained data\n",
    "\n",
    "pos = Y_train == 1\n",
    "neg = Y_train == 0\n",
    "#print(f\"Index of positive results are: {pos}\")\n",
    "#print(f\"Index of negative results are: {neg}\")\n",
    "# this fetches all the values where boolean mask created previously is true from y_train\n",
    "X_train[pos]"
   ],
   "execution_count":15,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([3., 4., 5.], dtype=float32)<\/pre>"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"TANDvSoWOsofVZE1pb6oz4",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# now let's plot these examples\n",
    "\n",
    "pos = Y_train == 1\n",
    "neg = Y_train == 0\n",
    "\n",
    "fig,ax = plt.subplots(1,1,figsize=(4,3))\n",
    "ax.scatter(X_train[pos], Y_train[pos], marker='x', s=80, c = 'red', label=\"y=1\")\n",
    "ax.scatter(X_train[neg], Y_train[neg], marker='o', s=100, label=\"y=0\", lw=3)\n",
    "\n",
    "ax.set_ylim(-0.08,1.1)\n",
    "ax.set_ylabel('y', fontsize=12)\n",
    "ax.set_xlabel('x', fontsize=12)\n",
    "ax.set_title('one variable plot')\n",
    "ax.legend(fontsize=12)\n",
    "plt.show()"
   ],
   "execution_count":16,
   "outputs":[
    {
     "data":{
      "image\/png":[
       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\n"
      ]
     },
     "metadata":{
      "image\/png":{
       
      }
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"oxkcSuaq9bBJM4f1ZjGASn",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# let's create a single neuron activation model using tensorflow\n",
    "\n",
    "model = Sequential(\n",
    "    [tf.keras.layers.Dense(1, input_dim=1, activation = 'sigmoid', name='L1')]\n",
    ")\n",
    "\n",
    "model.summary()"
   ],
   "execution_count":17,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " L1 (Dense)                  (None, 1)                 2         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ],
     "output_type":"stream"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"3hAQGcFtGpaSgGz6zzjjrD",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# let's look at the parameters of the logistic layer defined previously\n",
    "\n",
    "logistic_layer = model.get_layer(\"L1\")\n",
    "w,b = logistic_layer.get_weights()\n",
    "\n",
    "print(f\"Weight allocated is: {w}\")\n",
    "print(f\"Bias allocated is: {b}\")"
   ],
   "execution_count":18,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "Weight allocated is: [[1.5941397]]\n",
      "Bias allocated is: [0.]\n"
     ],
     "output_type":"stream"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"xqw536ytuaBZfGmoLQyx55",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# now let's set weights to specific model weights for testing our training data\n",
    "\n",
    "set_w = np.array([[2]])\n",
    "set_b = np.array([-4.5])\n",
    "\n",
    "# set_weights takes a list of numpy arrays\n",
    "logistic_layer.set_weights([set_w, set_b])\n",
    "print(logistic_layer.get_weights())"
   ],
   "execution_count":19,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "[array([[2.]], dtype=float32), array([-4.5], dtype=float32)]\n"
     ],
     "output_type":"stream"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"pKMgGj8RP0MeGdZ5mjSV2N",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# now let's find the prediction using tensorflow and our previously learned sigmoid function\n",
    "\n",
    "# Set weights for the model\n",
    "model.layers[0].set_weights([set_w, set_b])\n",
    "\n",
    "model_output = model.predict(X_train)\n",
    "\n",
    "print(f\"Model output from tensorflow single layer sigmoid activation: {model_output}\")\n",
    "\n",
    "z = np.dot(X_train,set_w) + set_b\n",
    "sigmoid_fn_output = 1\/(1 + np.exp(-z))\n",
    "\n",
    "print(f\"Model output from sigmoid function is : {sigmoid_fn_output}\")"
   ],
   "execution_count":38,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "\r1\/1 [==============================] - ETA: 0s\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r1\/1 [==============================] - 0s 23ms\/step\n",
      "Model output from tensorflow single layer sigmoid activation: [[0.01098694]\n",
      " [0.07585818]\n",
      " [0.37754068]\n",
      " [0.8175745 ]\n",
      " [0.97068775]\n",
      " [0.99592984]]\n",
      "Model output from sigmoid function is : [[0.01098694]\n",
      " [0.07585818]\n",
      " [0.37754067]\n",
      " [0.81757448]\n",
      " [0.97068777]\n",
      " [0.99592986]]\n"
     ],
     "output_type":"stream"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"zokggdQAFZjsC6H9LTqiTp",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    
   ],
   "execution_count":null,
   "outputs":[
    
   ],
   "metadata":{
    "datalore":{
     "node_id":"We4ZNbl8dEu1NbSf5rnSk3",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  }
 ],
 "metadata":{
  "kernelspec":{
   "display_name":"Python",
   "language":"python",
   "name":"python"
  },
  "datalore":{
   "computation_mode":"JUPYTER",
   "package_manager":"pip",
   "base_environment":"default",
   "packages":[
    
   ],
   "report_row_ids":[
    
   ],
   "version":3
  }
 },
 "nbformat":4,
 "nbformat_minor":4
}