{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Previous Class Definitions\n",
    "The previously defined Layer_Dense, Activation_ReLU, and Activation_Softmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import numpy as np\n",
    "import nnfs\n",
    "from nnfs.datasets import spiral_data\n",
    "nnfs.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [],
   "source": [
    "class Layer_Dense:\n",
    "    def __init__(self, n_inputs, n_neurons):\n",
    "        # Initialize the weights and biases\n",
    "        self.weights = 0.01 * np.random.randn(n_inputs, n_neurons)  # Normal distribution of weights\n",
    "        self.biases = np.zeros((1, n_neurons))\n",
    "\n",
    "    def forward(self, inputs):\n",
    "        # Calculate the output values from inputs, weights, and biases\n",
    "        self.output = np.dot(inputs, self.weights) + self.biases        # Weights are already transposed\n",
    "\n",
    "class Activation_ReLU:\n",
    "    def forward(self, inputs):\n",
    "        self.output = np.maximum(0, inputs)\n",
    "        \n",
    "class Activation_Softmax:\n",
    "    def forward(self, inputs):\n",
    "        # Get the unnormalized probabilities\n",
    "        # Subtract max from the row to prevent larger numbers\n",
    "        exp_values = np.exp(inputs - np.max(inputs, axis=1, keepdims=True))\n",
    "\n",
    "        # Normalize the probabilities with element wise division\n",
    "        probabilities = exp_values / np.sum(exp_values, axis=1,keepdims=True)\n",
    "        self.output = probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward Pass with No Loss Consideration\n",
    "2 input neural network with 2 layers of 3 neurons each. ReLU activation in the first layer with Softmax in the second layer to normalize the outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "outputs": [],
   "source": [
    "# Create dataset\n",
    "X, y = spiral_data(samples=100, classes=3)\n",
    "# Create Dense layer with 2 input features and 3 output values\n",
    "dense1 = Layer_Dense(2, 3)\n",
    "# Create ReLU activation (to be used with Dense layer):\n",
    "activation1 = Activation_ReLU()\n",
    "# Create second Dense layer with 3 input features (as we take output\n",
    "# of previous layer here) and 3 output values\n",
    "dense2 = Layer_Dense(3, 3)\n",
    "# Create Softmax activation (to be used with Dense layer):\n",
    "activation2 = Activation_Softmax()\n",
    "\n",
    "# Make a forward pass of our training data through this layer\n",
    "dense1.forward(X)\n",
    "\n",
    "# Make a forward pass through activation function\n",
    "# it takes the output of first dense layer here\n",
    "activation1.forward(dense1.output)\n",
    "# Make a forward pass through second Dense layer\n",
    "# it takes outputs of activation function of first layer as inputs\n",
    "dense2.forward(activation1.output)\n",
    "# Make a forward pass through activation function\n",
    "# it takes the output of second dense layer here\n",
    "activation2.forward(dense2.output)\n",
    "# Let's see output of the first few samples:\n",
    "print(activation2.output[:5])"
   ]
  }
 ],
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  "language_info": {
   "name": "python"
  }
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 "nbformat": 4,
 "nbformat_minor": 2
}