Neural_Network_in_NP_Fashion_MNIST.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/fsaudm/NeuralNet_in_NumPy/blob/main/Neural_Network_in_NP_Fashion_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "# A Neural Network using NumPy for *Fashion* MNIST"
      ],
      "metadata": {
        "id": "tTA8_Vn-pY4z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note**\n",
        "\n",
        "For 2-D arrays, np.dot performs matrix multiplication. To be more readable and explicit:\n",
        "- **np.matmul()** is used for matrix multiplication\n",
        "- \"*\" is used for element-wise multiplication"
      ],
      "metadata": {
        "id": "MKaQHX02pWeU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing mighty NumPy!\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "tBFxzWXwcxOC"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "\n",
        "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n",
        "# Normalize the images\n",
        "X_train = X_train.astype('float32') / 255.0\n",
        "X_test = X_test.astype('float32') / 255.0\n",
        "# Flatten the images\n",
        "X_train = X_train.reshape(-1, 784)\n",
        "X_test = X_test.reshape(-1, 784)\n",
        "\n",
        "# Flatten y\n",
        "y_train = y_train.reshape(-1, 1)\n",
        "y_test = y_test.reshape(-1, 1)\n",
        "\n",
        "\n",
        "# Shape\n",
        "print(f\"X_train shape: {X_train.shape}\")\n",
        "print(f\"y_train shape: {y_train.shape}\")\n",
        "print(f\"X_test shape: {X_test.shape}\")\n",
        "print(f\"y_test shape: {y_test.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_tZ_gWBLLhk",
        "outputId": "cad2b262-0bd9-4973-afd8-76e4bf9b82c3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "\u001b[1m29515/29515\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "\u001b[1m26421880/26421880\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "\u001b[1m5148/5148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "\u001b[1m4422102/4422102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
            "X_train shape: (60000, 784)\n",
            "y_train shape: (60000, 1)\n",
            "X_test shape: (10000, 784)\n",
            "y_test shape: (10000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Label distribution in y_train: \\n {np.unique(y_train, return_counts=True)} \\n\")\n",
        "print(f\"Label distribution in y_test: \\n {np.unique(y_test, return_counts=True)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BVy3xNjsPSXd",
        "outputId": "a4d7adc8-9d46-416a-f6a9-16b3a68ffd49"
      },
      "execution_count": 3,
      "outputs": [
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          "text": [
            "Label distribution in y_train: \n",
            " (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8), array([6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000])) \n",
            "\n",
            "Label distribution in y_test: \n",
            " (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8), array([1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Neural Network definition & Functions"
      ],
      "metadata": {
        "id": "lapVfpFHfyTT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ReLu\n",
        "def relu(array):\n",
        "    return np.maximum(0, array)  # Vectorized ReLU"
      ],
      "metadata": {
        "id": "qyTDZVKVjPdz"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Softmax\n",
        "def softmax(array):\n",
        "    # Clip values to prevent overflow\n",
        "    array = np.clip(array, -500, 500)\n",
        "    exps = np.exp(array - np.max(array, axis=1, keepdims=True)) # Substract the max, broadcast\n",
        "    return exps / np.sum(exps, axis=1, keepdims=True)  # Divide by sum, broadcast"
      ],
      "metadata": {
        "id": "Uu2QIB-mlkHf"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Predict Y\n",
        "def predict_y(array):\n",
        "    return np.argmax(array, axis=1)  # Divide by sum, broadcast"
      ],
      "metadata": {
        "id": "-ka-qcZfsot1"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title One-hot encoding for Labels\n",
        "def one_hot_encode(y, n_classes = 10):\n",
        "    return np.eye(n_classes)[y].reshape(-1, n_classes)"
      ],
      "metadata": {
        "id": "JOLtFHMVtQI1"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Cross-entropy\n",
        "def cross_entropy_loss(y_true, y_pred):\n",
        "    loss = -np.mean(y_true * np.log(y_pred + 1e-9)) # Average loss over the batch\n",
        "    return loss"
      ],
      "metadata": {
        "id": "pyKtlhvKv97i"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Network Architecture & Params"
      ],
      "metadata": {
        "id": "4PjPiIjpF_Fy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Parameters\n",
        "def initialize_parameters(input_dim, hidden1_dim, hidden2_dim, output_dim):\n",
        "    W1 = np.random.randn(input_dim, hidden1_dim) * np.sqrt(2. / input_dim)\n",
        "    b1 = np.zeros((1, hidden1_dim))\n",
        "    W2 = np.random.randn(hidden1_dim, hidden2_dim) * np.sqrt(2. / hidden1_dim)\n",
        "    b2 = np.zeros((1, hidden2_dim))\n",
        "    W3 = np.random.randn(hidden2_dim, output_dim) * np.sqrt(2. / hidden2_dim)\n",
        "    b3 = np.zeros((1, output_dim))\n",
        "    return W1, W2, W3, b1, b2, b3"
      ],
      "metadata": {
        "id": "AgrkqvpkuaTo"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Forward pass, Vetorized!\n",
        "np.random.seed(77)\n",
        "\n",
        "i=10\n",
        "x = X_train[:i]\n",
        "y = y_train[:i]\n",
        "num_classes = 10\n",
        "\n",
        "# Initialize parameters\n",
        "input_dim = 784\n",
        "hidden1_dim = 128\n",
        "hidden2_dim = 64\n",
        "output_dim = 10\n",
        "\n",
        "W1, W2, W3, b1, b2, b3 = initialize_parameters(input_dim, hidden1_dim, hidden2_dim, output_dim)\n",
        "\n",
        "### Network Example\n",
        "### 1 forward pass\n",
        "# From input to hidden 1\n",
        "z1 = np.matmul(x,W1) + b1\n",
        "a1 = relu(z1)\n",
        "\n",
        "# From hidden 1 to hidden 2\n",
        "z2 = np.matmul(a1,W2) + b2\n",
        "a2 = relu(z2)\n",
        "\n",
        "# From hidden 2 to output\n",
        "z3 = np.matmul(a2,W3) + b3\n",
        "a3 = softmax(z3)\n",
        "\n",
        "# From output to prediction\n",
        "y_hat = predict_y(a3).reshape(-1,1)\n",
        "\n",
        "# Calculate loss\n",
        "loss = cross_entropy_loss(one_hot_encode(y, num_classes), a3)\n",
        "\n",
        "\n",
        "\n",
        "# Results:\n",
        "print(f\"Probabilies:\\n {a3}, shape: {a3.shape}\\n\")\n",
        "print(f\"Predicted Y:\\n {y_hat}, shape: {y_hat.shape}\\n\")\n",
        "print(f\"Actual Y:\\n {y_train[:i]}, shape: {y_train[:i].shape}\\n\")\n",
        "print(f\"Loss:\\n {loss}\\n\")\n",
        "\n",
        "\n",
        "# Shapes:\n",
        "print(f\"Input shape: {x.shape}\")\n",
        "print(f\"Predicted Y shape: {y_hat.shape}\")\n",
        "\n",
        "print(f\"Parameters W1 shape: {W1.shape}\")\n",
        "print(f\"Parameters b1 shape: {b1.shape}\")\n",
        "print(f\"Parameters W2 shape: {W2.shape}\")\n",
        "print(f\"Parameters b2 shape: {b2.shape}\")\n",
        "print(f\"Parameters W3 shape: {W3.shape}\")\n",
        "print(f\"Parameters b3 shape: {b3.shape}\")"
      ],
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      "execution_count": 10,
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probabilies:\n",
            " [[0.08650725 0.05702268 0.06608429 0.05658622 0.05108793 0.0901224\n",
            "  0.10010525 0.0470514  0.25879216 0.18664042]\n",
            " [0.05634977 0.01601526 0.03880536 0.06351597 0.10359667 0.08037985\n",
            "  0.10678997 0.03601245 0.22778365 0.27075104]\n",
            " [0.10685274 0.03441664 0.08158345 0.06853418 0.11771474 0.08811645\n",
            "  0.12929595 0.052755   0.15163768 0.16909317]\n",
            " [0.07636799 0.02275297 0.05306574 0.08419851 0.09541571 0.11172897\n",
            "  0.13979769 0.04721665 0.19465369 0.17480208]\n",
            " [0.10128444 0.01029225 0.07446325 0.02585618 0.08917662 0.05192217\n",
            "  0.10269559 0.01905737 0.28142956 0.24382257]\n",
            " [0.08514709 0.02333356 0.05862734 0.05881846 0.07726048 0.05904967\n",
            "  0.15205052 0.05822344 0.17937466 0.24811478]\n",
            " [0.08169904 0.03861366 0.07890627 0.10447165 0.0980242  0.09666473\n",
            "  0.14679507 0.03571276 0.18210493 0.13700769]\n",
            " [0.04909953 0.00986098 0.0738184  0.03007237 0.05009334 0.0715083\n",
            "  0.09872245 0.05392796 0.25692162 0.30597505]\n",
            " [0.07714542 0.0749563  0.08549604 0.06111055 0.12260233 0.0756747\n",
            "  0.10920752 0.08391296 0.16210465 0.14778953]\n",
            " [0.05386158 0.06635951 0.05710121 0.05687907 0.21913775 0.05815413\n",
            "  0.08877067 0.11444614 0.16390383 0.12138611]], shape: (10, 10)\n",
            "\n",
            "Predicted Y:\n",
            " [[8]\n",
            " [9]\n",
            " [9]\n",
            " [8]\n",
            " [8]\n",
            " [9]\n",
            " [8]\n",
            " [9]\n",
            " [8]\n",
            " [4]], shape: (10, 1)\n",
            "\n",
            "Actual Y:\n",
            " [[9]\n",
            " [0]\n",
            " [0]\n",
            " [3]\n",
            " [0]\n",
            " [2]\n",
            " [7]\n",
            " [2]\n",
            " [5]\n",
            " [5]], shape: (10, 1)\n",
            "\n",
            "Loss:\n",
            " 0.25756370999828926\n",
            "\n",
            "Input shape: (10, 784)\n",
            "Predicted Y shape: (10, 1)\n",
            "Parameters W1 shape: (784, 128)\n",
            "Parameters b1 shape: (1, 128)\n",
            "Parameters W2 shape: (128, 64)\n",
            "Parameters b2 shape: (1, 64)\n",
            "Parameters W3 shape: (64, 10)\n",
            "Parameters b3 shape: (1, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Back Propagation"
      ],
      "metadata": {
        "id": "G5z75GjbGbb3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title ReLu 'prime'\n",
        "def relu_prime(array):\n",
        "    return np.where(array > 0, 1, 0) ## Boolean for z>0, 1, 0"
      ],
      "metadata": {
        "id": "XSx5OHCAIk9E"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "No need for \"Softmax prime\". A softmax function followed by a cross-entropy function is a combination that works very well, and the derivative is quite simple."
      ],
      "metadata": {
        "id": "8hBJlElAQm4W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Backwards pass, updating parameters\n",
        "\n",
        "### Network Example\n",
        "### backwards pass\n",
        "### Division by m is to average the gradients over all observations in the batch.\n",
        "\n",
        "m = y.shape[0]  # Number of examples\n",
        "\n",
        "# Step 1: Calculate error at the output layer\n",
        "delta3 = a3 - one_hot_encode(y, num_classes) # daL/dzL * dC/daL\n",
        "\n",
        "# Step 2: Calculate gradients for W3 and b3\n",
        "dW3 = np.matmul(a2.T, delta3) / m  # dzL/dWL\n",
        "db3 = np.sum(delta3, axis=0, keepdims=True) / m  # dzL/dbL\n",
        "\n",
        "# Step 3: Propagate the error to hidden layer 2\n",
        "delta2 = np.matmul(delta3, W3.T) * relu_prime(z2)  #  da(L-1)/dz(L-1) * dzL/da(L-1)\n",
        "\n",
        "# Step 4: Calculate gradients for W2 and b2\n",
        "dW2 = np.matmul(a1.T, delta2) / m  # dz(L-1)/dW(L-1)\n",
        "db2 = np.sum(delta2, axis=0, keepdims=True) / m  # dz(L-1)/db(L-1)\n",
        "\n",
        "# Step 5: Propagate the error to hidden layer 1\n",
        "delta1 = np.matmul(delta2, W2.T) * relu_prime(z1)  # da(L-2)/dz(L-2) * dz(L-1)/da(L-2)\n",
        "\n",
        "# Step 6: Calculate gradients for W1 and b1\n",
        "dW1 = np.matmul(x.T, delta1) / m  # dz(L-2)/dW(L-2)\n",
        "db1 = np.sum(delta1, axis=0, keepdims=True) / m  # dz(L-2)/db(L-2)\n",
        "\n",
        "\n",
        "### Parameters update\n",
        "lr = 3e-2\n",
        "\n",
        "# Update weights and biases\n",
        "W1 -= lr * dW1\n",
        "b1 -= lr * db1\n",
        "W2 -= lr * dW2\n",
        "b2 -= lr * db2\n",
        "W3 -= lr * dW3\n",
        "b3 -= lr * db3"
      ],
      "metadata": {
        "id": "hQQeWYp3upDH"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "-2xxRewXZma5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def forward_pass(x, y, num_classes, W1, W2, W3, b1, b2, b3):\n",
        "\n",
        "  # From input to hidden 1\n",
        "  z1 = np.matmul(x,W1) + b1\n",
        "  a1 = relu(z1)\n",
        "\n",
        "  # From hidden 1 to hidden 2\n",
        "  z2 = np.matmul(a1,W2) + b2\n",
        "  a2 = relu(z2)\n",
        "\n",
        "  # From hidden 2 to output\n",
        "  z3 = np.matmul(a2,W3) + b3\n",
        "  a3 = softmax(z3)\n",
        "\n",
        "  # From output to prediction\n",
        "  y_hat = predict_y(a3).reshape(-1,1)\n",
        "\n",
        "  # Calculate loss\n",
        "  loss = cross_entropy_loss(one_hot_encode(y, num_classes), a3)\n",
        "\n",
        "  return loss, y_hat, a3, a2, a1, z3, z2, z1\n",
        "\n",
        "\n",
        "def backwards_pass(x, y, num_classes, lr, a3, a2, a1, W1, W2, W3, b1, b2, b3, z1, z2, z3):\n",
        "  ### Error propagation\n",
        "  m = y.shape[0]  # Number of examples\n",
        "\n",
        "  delta3 = a3 - one_hot_encode(y, num_classes)\n",
        "  dW3 = np.matmul(a2.T, delta3) / m\n",
        "  db3 = np.sum(delta3, axis=0, keepdims=True) / m\n",
        "\n",
        "  delta2 = np.matmul(delta3, W3.T) * relu_prime(z2)\n",
        "  dW2 = np.matmul(a1.T, delta2) / m\n",
        "  db2 = np.sum(delta2, axis=0, keepdims=True) / m\n",
        "\n",
        "  delta1 = np.matmul(delta2, W2.T) * relu_prime(z1)\n",
        "  dW1 = np.matmul(x.T, delta1) / m\n",
        "  db1 = np.sum(delta1, axis=0, keepdims=True) / m\n",
        "\n",
        "  # Update weights and biases\n",
        "  W1 -= lr * dW1\n",
        "  b1 -= lr * db1\n",
        "  W2 -= lr * dW2\n",
        "  b2 -= lr * db2\n",
        "  W3 -= lr * dW3\n",
        "  b3 -= lr * db3\n",
        "\n",
        "  return W1, W2, W3, b1, b2, b3"
      ],
      "metadata": {
        "id": "KsC8t3y4ZqsF"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training loop"
      ],
      "metadata": {
        "id": "9m3h8XDOarNZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install wandb -qqq"
      ],
      "metadata": {
        "id": "tFr2Bmz5QQbg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "863d4e98-87df-41c8-f2f1-0e5947e8d473"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m22.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.3/207.3 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m301.8/301.8 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "# Plots\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Accuracy & Confusion matrix\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "# Logging\n",
        "import wandb\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Use wandb-core, temporary for wandb's new backend\n",
        "wandb.require(\"core\")\n",
        "wandb.login()\n",
        "\n",
        "#wandb login --relogin"
      ],
      "metadata": {
        "id": "yL0qjFHUDeUK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "ddc85d91-4948-4711-9e94-30cbfba4ed07"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n"
          ]
        },
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              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
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              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
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              "                    clearTimeout(timeout)\n",
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              "            })\n",
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              "    "
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
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        {
          "output_type": "execute_result",
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    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(y_true, y_pred, figsize=(10, 8), cmap=\"viridis\"):\n",
        "    # Calculate the confusion matrix\n",
        "    conf_matrix = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "    plt.figure(figsize=figsize)\n",
        "    sns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=cmap)\n",
        "    plt.title(f'Confusion Matrix (Accuracy: {accuracy_score(y_true, y_pred):.2f})')\n",
        "    plt.xlabel('Predicted')\n",
        "    plt.ylabel('True')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "KyDvMPgtawEn"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Random seed\n",
        "np.random.seed(42)\n",
        "\n",
        "# Initialize wandb\n",
        "wandb.init(project=\"Fashion-mnist-neural-network\")\n",
        "\n",
        "\n",
        "X = X_train\n",
        "Y = y_train\n",
        "num_classes = 10\n",
        "\n",
        "x_test = X_test\n",
        "y_test = y_test\n",
        "\n",
        "# Initialize parameters\n",
        "input_dim = 784\n",
        "hidden1_dim = 511\n",
        "hidden2_dim = 254\n",
        "output_dim = num_classes\n",
        "\n",
        "W1, W2, W3, b1, b2, b3 = initialize_parameters(input_dim, hidden1_dim, hidden2_dim, output_dim)\n",
        "num_params = W1.size + W2.size + W3.size + b1.size + b2.size + b3.size\n",
        "\n",
        "# Learning rate & Epochs\n",
        "lr=3e-1\n",
        "epochs = 100\n",
        "\n",
        "## Training loop\n",
        "for epoch in tqdm(range(epochs), desc=\"Training Progress\"):\n",
        "\n",
        "  ## Random Batches\n",
        "  # Shuffle the array\n",
        "  random_indices = np.random.randint(0, len(Y), size=(len(Y)))\n",
        "  x = X[random_indices]\n",
        "  y = Y[random_indices]\n",
        "\n",
        "  ## Training\n",
        "  # Forward pass\n",
        "  training_loss, y_hat_tr, a3, a2, a1, z3, z2, z1 = forward_pass(x, y, num_classes, W1, W2, W3, b1, b2, b3)\n",
        "\n",
        "  # Backwards pass\n",
        "  W1, W2, W3, b1, b2, b3 = backwards_pass(x, y, num_classes, lr, a3, a2, a1, W1, W2, W3, b1, b2, b3, z1, z2, z3)\n",
        "\n",
        "  ## Validation\n",
        "  # Forward pass\n",
        "  val_loss, y_hat_val, _, _, _, _, _, _ = forward_pass(x_test, y_test, num_classes, W1, W2, W3, b1, b2, b3)\n",
        "\n",
        "  ## Logging\n",
        "  train_accuracy = accuracy_score(y, y_hat_tr)\n",
        "  val_accuracy = accuracy_score(y_test, y_hat_val)\n",
        "\n",
        "  ## Logging with wandb\n",
        "  wandb.log({\"train_loss\": training_loss, \"val_loss\": val_loss, \"train_accuracy\": train_accuracy, \"val_accuracy\": val_accuracy})\n",
        "\n",
        "## Logging\n",
        "wandb.config.activation_function = \"relu\"\n",
        "wandb.config.batch_size = len(Y)\n",
        "wandb.config.epochs = epochs\n",
        "wandb.config.input_dims = input_dim\n",
        "wandb.config.hidden_layer_1 = hidden1_dim\n",
        "wandb.config.hidden_layer_2 = hidden2_dim\n",
        "wandb.config.output = output_dim\n",
        "wandb.config.learning_rate = lr\n",
        "wandb.config.loss_function = \"cross_entropy\"\n",
        "wandb.config.parameters = num_params\n",
        "\n",
        "wandb.finish()\n",
        "\n",
        "# Number of parameters\n",
        "print(f\"Number of parameters: {num_params}\")\n",
        "print()\n",
        "\n",
        "# Shapes:\n",
        "print(f\"Input shape: {x.shape}\")\n",
        "print(f\"Parameters W1 shape: {W1.shape}\")\n",
        "print(f\"Parameters b1 shape: {b1.shape}\")\n",
        "print(f\"Parameters W2 shape: {W2.shape}\")\n",
        "print(f\"Parameters b2 shape: {b2.shape}\")\n",
        "print(f\"Parameters W3 shape: {W3.shape}\")\n",
        "print(f\"Parameters b3 shape: {b3.shape}\")\n",
        "print(f\"Predicted Y shape: {y_hat.shape}\")\n",
        "\n",
        "plot_confusion_matrix(y_test, y_hat_val)"
      ],
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        "colab": {
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        {
          "data": {
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              "Tracking run with wandb version 0.17.6"
            ],
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            "Training Progress: 100%|██████████| 100/100 [13:10<00:00,  7.91s/it]\n"
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              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train_accuracy</td><td>▂▁▁▄▃▃▄▅▆▆▇▇▆▅▆▆▇▇▇▇▇▇█▇▇██▇▇▇▇████████▇</td></tr><tr><td>train_loss</td><td>█▅▄▄▄▃▃▂▂▂▂▂▂▂▂▂▁▂▂▂▂▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▂▁▁</td></tr><tr><td>val_accuracy</td><td>▂▁▁▂▄▃▃▆▅▇▅▆▇▆▇▇▆▆▇▇▆▇▇▇███▇▇▇▇███▇█▇███</td></tr><tr><td>val_loss</td><td>█▅▄▃▄▃▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train_accuracy</td><td>0.80793</td></tr><tr><td>train_loss</td><td>0.05144</td></tr><tr><td>val_accuracy</td><td>0.7983</td></tr><tr><td>val_loss</td><td>0.05389</td></tr></table><br/></div></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">cool-feather-19</strong> at: <a href='https://wandb.ai/gfs3-university-of-illinois-urbana-champaign/Fashion-mnist-neural-network/runs/kplawiwt' target=\"_blank\">https://wandb.ai/gfs3-university-of-illinois-urbana-champaign/Fashion-mnist-neural-network/runs/kplawiwt</a><br/> View project at: <a href='https://wandb.ai/gfs3-university-of-illinois-urbana-champaign/Fashion-mnist-neural-network' target=\"_blank\">https://wandb.ai/gfs3-university-of-illinois-urbana-champaign/Fashion-mnist-neural-network</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20240813_064648-kplawiwt/logs</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of parameters: 533733\n",
            "\n",
            "Input shape: (60000, 784)\n",
            "Parameters W1 shape: (784, 511)\n",
            "Parameters b1 shape: (1, 511)\n",
            "Parameters W2 shape: (511, 254)\n",
            "Parameters b2 shape: (1, 254)\n",
            "Parameters W3 shape: (254, 10)\n",
            "Parameters b3 shape: (1, 10)\n",
            "Predicted Y shape: (10, 1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_imgs = 15\n",
        "\n",
        "# Create a figure with subplots\n",
        "fig, axes = plt.subplots(1, num_imgs, figsize=(15, 3))\n",
        "\n",
        "for i in range(num_imgs):\n",
        "    # Randomly select an index\n",
        "    random_index = np.random.randint(0, len(X_test))\n",
        "    x_obs = X_test[random_index]  # Select the image at the random index\n",
        "    y_obs = y_test[random_index]  # Select the corresponding label\n",
        "\n",
        "    # Forward pass to get the prediction\n",
        "    _ , prediction, _, _, _, _, _, _ = forward_pass(x_obs, y_obs, num_classes, W1, W2, W3, b1, b2, b3)\n",
        "\n",
        "    # Visualize the digit\n",
        "    axes[i].imshow(x_obs.reshape(28, 28), cmap='viridis')\n",
        "    axes[i].set_title(f'Label: {y_obs}\\nPred: {prediction}')\n",
        "    axes[i].axis('off')  # Turn off the axes\n",
        "\n",
        "# Adjust layout and show the plot\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "66XQ7ZRCDXFf",
        "outputId": "0c269508-17ca-47f1-fb92-c908086af75c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x300 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}