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Implementing and training a neural network from scratch in numpy only
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| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/jonnyli1125/1ad95073ff218d00cc4faee133f05dcc/neural_network_from_scratch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import numpy as np" | |
| ], | |
| "metadata": { | |
| "id": "4G_wY9f7JR9u" | |
| }, | |
| "execution_count": 189, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def linear(X):\n", | |
| " return X\n", | |
| "\n", | |
| "def relu(X):\n", | |
| " return np.maximum(0, X)\n", | |
| "\n", | |
| "def softmax(X):\n", | |
| " exps = np.exp(X - np.max(X, axis=-1, keepdims=True))\n", | |
| " return exps / np.sum(exps, axis=-1, keepdims=True)" | |
| ], | |
| "metadata": { | |
| "id": "HyRoZbAvM_uE" | |
| }, | |
| "execution_count": 190, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "relu(np.array([[-1.5, 2], [3, -3]]))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "7Nvy1ecd1_m_", | |
| "outputId": "7b4df07a-dd6c-46c2-d29c-daa3264fb180" | |
| }, | |
| "execution_count": 191, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[0., 2.],\n", | |
| " [3., 0.]])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 191 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "softmax(np.array([[1, 2], [-4, -1]]))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "EvGrF8t619AL", | |
| "outputId": "66e42038-1107-4886-f13b-cc44172d5980" | |
| }, | |
| "execution_count": 192, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[0.26894142, 0.73105858],\n", | |
| " [0.04742587, 0.95257413]])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 192 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 193, | |
| "metadata": { | |
| "id": "uvvcBKPoJGe0" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "class NN:\n", | |
| " def __init__(self, input_dim, layer_dims, activations):\n", | |
| " assert len(layer_dims) > 0, \"must specify at least 1 output dimension\"\n", | |
| " assert len(layer_dims) == len(activations)\n", | |
| " self.weights = [\n", | |
| " np.random.randn(input_dim if i == 0 else layer_dims[i-1], layer_dims[i]) * 0.01\n", | |
| " for i in range(len(layer_dims))\n", | |
| " ]\n", | |
| " self.biases = [np.zeros((1, d)) for d in layer_dims]\n", | |
| " self.activations = activations\n", | |
| "\n", | |
| " def forward(self, X):\n", | |
| " outputs = []\n", | |
| " for W, b, a in zip(self.weights, self.biases, self.activations):\n", | |
| " H = outputs[-1][-1] if outputs else X\n", | |
| " Z = (H @ W) + b\n", | |
| " Y = a(Z)\n", | |
| " outputs.append((Z, Y))\n", | |
| " return outputs" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "NN(64, [20, 30, 10], [relu, relu, softmax]).forward(np.random.rand(2, 64))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "gK4NgVSZMz7J", | |
| "outputId": "8fce7a4c-5458-4e0c-f5c6-18840f902a9c" | |
| }, | |
| "execution_count": 194, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[(array([[ 0.01310614, -0.02483857, 0.01322928, 0.02418123, 0.05143695,\n", | |
| " -0.04612303, 0.04375339, -0.06159471, 0.01200021, -0.00189023,\n", | |
| " -0.01623075, -0.0520434 , -0.02721001, -0.00383264, -0.02685226,\n", | |
| " 0.01207064, -0.00309046, -0.04950506, -0.00490789, -0.00304446],\n", | |
| " [ 0.03420128, -0.00546025, 0.02816028, 0.04509331, 0.02917047,\n", | |
| " -0.08710521, 0.0704111 , -0.00904433, -0.00679622, 0.00294658,\n", | |
| " 0.02104755, -0.0312751 , -0.04877266, -0.01381729, 0.07771039,\n", | |
| " 0.01400375, 0.03111976, -0.0314119 , -0.04898396, -0.02397589]]),\n", | |
| " array([[0.01310614, 0. , 0.01322928, 0.02418123, 0.05143695,\n", | |
| " 0. , 0.04375339, 0. , 0.01200021, 0. ,\n", | |
| " 0. , 0. , 0. , 0. , 0. ,\n", | |
| " 0.01207064, 0. , 0. , 0. , 0. ],\n", | |
| " [0.03420128, 0. , 0.02816028, 0.04509331, 0.02917047,\n", | |
| " 0. , 0.0704111 , 0. , 0. , 0.00294658,\n", | |
| " 0.02104755, 0. , 0. , 0. , 0.07771039,\n", | |
| " 0.01400375, 0.03111976, 0. , 0. , 0. ]])),\n", | |
| " (array([[-1.31928013e-03, 7.64714727e-04, -8.35962676e-04,\n", | |
| " -4.93508189e-04, 5.63599309e-05, -4.50870969e-04,\n", | |
| " -6.17764161e-04, 1.52650221e-05, 6.66411872e-04,\n", | |
| " 7.81459008e-04, -5.40660850e-04, -2.61921158e-04,\n", | |
| " 6.61770423e-04, 9.84781730e-06, 6.21346661e-04,\n", | |
| " -2.85392891e-04, 7.75938524e-04, 4.07423160e-04,\n", | |
| " -8.87571601e-05, -8.17069153e-04, -4.07290133e-04,\n", | |
| " 1.21362720e-03, 1.74287740e-03, 3.20663769e-05,\n", | |
| " -1.42601931e-05, -1.19358231e-03, 6.01697832e-05,\n", | |
| " 8.03219015e-04, -4.73842574e-04, 4.96765755e-04],\n", | |
| " [-8.09555455e-04, -2.15977795e-03, -1.84286398e-03,\n", | |
| " -4.69089153e-04, 5.74575644e-04, -1.18950707e-03,\n", | |
| " 1.19987871e-03, 6.61453989e-04, 1.67396717e-03,\n", | |
| " 1.28067561e-03, -5.57167646e-04, 1.25089081e-04,\n", | |
| " -9.94843077e-05, 9.05870130e-04, 2.40733802e-04,\n", | |
| " 2.23467222e-04, 5.85984717e-04, 1.35570241e-03,\n", | |
| " -1.67211365e-04, -6.05090668e-04, 1.33439948e-04,\n", | |
| " 1.47990450e-04, 2.46812930e-03, 2.54314919e-03,\n", | |
| " -1.28367729e-03, -1.98521877e-03, -7.21389138e-04,\n", | |
| " 1.12187180e-03, -1.37987262e-04, 4.99587272e-04]]),\n", | |
| " array([[0.00000000e+00, 7.64714727e-04, 0.00000000e+00, 0.00000000e+00,\n", | |
| " 5.63599309e-05, 0.00000000e+00, 0.00000000e+00, 1.52650221e-05,\n", | |
| " 6.66411872e-04, 7.81459008e-04, 0.00000000e+00, 0.00000000e+00,\n", | |
| " 6.61770423e-04, 9.84781730e-06, 6.21346661e-04, 0.00000000e+00,\n", | |
| " 7.75938524e-04, 4.07423160e-04, 0.00000000e+00, 0.00000000e+00,\n", | |
| " 0.00000000e+00, 1.21362720e-03, 1.74287740e-03, 3.20663769e-05,\n", | |
| " 0.00000000e+00, 0.00000000e+00, 6.01697832e-05, 8.03219015e-04,\n", | |
| " 0.00000000e+00, 4.96765755e-04],\n", | |
| " [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", | |
| " 5.74575644e-04, 0.00000000e+00, 1.19987871e-03, 6.61453989e-04,\n", | |
| " 1.67396717e-03, 1.28067561e-03, 0.00000000e+00, 1.25089081e-04,\n", | |
| " 0.00000000e+00, 9.05870130e-04, 2.40733802e-04, 2.23467222e-04,\n", | |
| " 5.85984717e-04, 1.35570241e-03, 0.00000000e+00, 0.00000000e+00,\n", | |
| " 1.33439948e-04, 1.47990450e-04, 2.46812930e-03, 2.54314919e-03,\n", | |
| " 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.12187180e-03,\n", | |
| " 0.00000000e+00, 4.99587272e-04]])),\n", | |
| " (array([[ 9.32278994e-07, 2.18092434e-05, 2.76064370e-05,\n", | |
| " 1.50362725e-05, -1.98073439e-05, -5.90975310e-05,\n", | |
| " -2.51607496e-05, 6.85344206e-05, 3.04792604e-05,\n", | |
| " -7.37450552e-06],\n", | |
| " [-7.43621750e-05, 3.88445526e-05, 9.10086056e-06,\n", | |
| " 1.85258340e-05, -2.82711685e-05, -5.60267927e-05,\n", | |
| " -6.07787358e-05, 1.12768616e-04, 4.63600433e-05,\n", | |
| " 4.18420604e-05]]),\n", | |
| " array([[0.09999956, 0.10000165, 0.10000223, 0.10000097, 0.09999749,\n", | |
| " 0.09999356, 0.09999695, 0.10000632, 0.10000252, 0.09999873],\n", | |
| " [0.09999208, 0.1000034 , 0.10000043, 0.10000137, 0.09999669,\n", | |
| " 0.09999392, 0.09999344, 0.1000108 , 0.10000416, 0.1000037 ]]))]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 194 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "# use logits before softmax activation for numerical stability\n", | |
| "# detailed explanation: https://gregorygundersen.com/blog/2020/02/09/log-sum-exp/\n", | |
| "# y = (e^(x - max(x)) / sum(e^(x - max(x)) for all x))\n", | |
| "# log(y) = log(e^(x - max(x))) - log(sum(e^(x - max(x)) for all x))\n", | |
| "# = log(e^x * e^(-max(x))) - log(sum(e^(x - max(x)) for all x))\n", | |
| "# = x - max(x) - log(sum(e^(x - max(x)) for all x))\n", | |
| "def loss_cross_entropy(logits, y):\n", | |
| " m = logits.max(axis=-1, keepdims=True)\n", | |
| " normalized_logits = logits - m - np.log(np.sum(np.exp(logits - m), axis=-1, keepdims=True))\n", | |
| " return -np.sum(y * normalized_logits) / len(logits)" | |
| ], | |
| "metadata": { | |
| "id": "RD-JP-PoQL4G" | |
| }, | |
| "execution_count": 195, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "logits = np.array([[1,2,3],[4,5,6]])\n", | |
| "y = np.array([[0,0,1],[1,0,0]])\n", | |
| "loss_cross_entropy(logits, y)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "U6dz29o8quhq", | |
| "outputId": "22a91fd0-5bdf-47e5-f543-3dbe473d2e55" | |
| }, | |
| "execution_count": 196, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "1.4076059644443806" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 196 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import functools\n", | |
| "\n", | |
| "def norm_clipped(grad_fn):\n", | |
| " @functools.wraps(grad_fn)\n", | |
| " def norm_clipped_grad_fn(*args, **kwargs):\n", | |
| " tensor = grad_fn(*args, **kwargs)\n", | |
| " norm = np.linalg.norm(tensor)\n", | |
| " if norm > 1:\n", | |
| " return tensor / norm\n", | |
| " return tensor\n", | |
| " return norm_clipped_grad_fn" | |
| ], | |
| "metadata": { | |
| "id": "TpApwhsYuWQI" | |
| }, | |
| "execution_count": 197, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def grad_loss_cross_entropy_logits(y_pred, y_true):\n", | |
| " \"\"\"Compute dL/dz for cross-entropy loss.\n", | |
| "\n", | |
| " Args:\n", | |
| " y_pred: (B, num_classes), logits before softmax\n", | |
| " y_true: (B, num_classes), one-hot vectors\n", | |
| "\n", | |
| " Returns:\n", | |
| " (B, num_classes), gradients of loss w.r.t. logits\n", | |
| " \"\"\"\n", | |
| " # M = max(z)\n", | |
| " # Z = sum(exp(z[k] - M) for all k)\n", | |
| " # L = -sum(y * (z[i] - M - log(Z))) = -log[k], where y[k] == 1\n", | |
| " # when i == k\n", | |
| " # dL/dz[i] = -1 * (1 - 0 - 1/Z * dZ/dz[i])\n", | |
| " # = -1 * (1 - 1/Z * sum(d[exp(z[j] - M)]/dz[i] for all j))\n", | |
| " # = -1 * (1 - 1/Z * d[exp(z[i] - M)]/dz[i])\n", | |
| " # = -1 * (1 - 1/Z * exp(z[i] - M) * d(z[i] - M)/dz[i])\n", | |
| " # = -1 * (1 - 1/Z * exp(z[i] - M) * 1)\n", | |
| " # = -1 * (1 - exp(z[i] - M)/Z)\n", | |
| " # = -1 * (1 - y_pred[i])\n", | |
| " # = y_pred[i] - 1\n", | |
| " # when i != k\n", | |
| " # dL/dz[i] = -1 * (0 - 0 - 1/Z * dZ/dz[i])\n", | |
| " # = -1 * (0 - y_pred[i])\n", | |
| " # = y_pred[i] - 0\n", | |
| " assert y_pred.shape == y_true.shape\n", | |
| " return y_pred - y_true\n", | |
| "\n", | |
| "def grad_relu_input(z):\n", | |
| " # d(relu(z))/dz\n", | |
| " return np.where(z >= 0, 1, 0)\n", | |
| "\n", | |
| "def grad_linear_weight(X):\n", | |
| " # d(Wx)[b,i]/dW\n", | |
| " # dL/dW = 1/B * sum over b,i: dL[b]/dz[b,i] * dz[b,i]/dW\n", | |
| " # Z = X @ W\n", | |
| " # z[b,i] = dot(X[b], W[:,i])\n", | |
| " # = X[b,1]W[1,i] + ... + X[b,j]W[j,i], where j is inner dim\n", | |
| " # dz[b,i]/dW[j,i] = X[b,j], assuming i matches\n", | |
| " # dz[b,i]/dW = [[0, ..., X[b,1], ..., 0], [0, .., X[b,2], ..., 0], ...]\n", | |
| " # z'[b,i] * dz[b,i]/dW = [[0, ..., z'[b,i]X[b,1], ..., 0], ...]\n", | |
| " # sum over b,i: z'[b,i] * dz[b,i]/dW =\n", | |
| " # [[z'[1,1]X[1,1], z'[1,2]X[1,1], ...], [z'[1,1]X[1,2], z'[1,2]X[1,2], ...], ...] +\n", | |
| " # [[z'[2,1]X[2,1], z'[2,2]X[2,1], ...], [z'[2,1]X[2,2], z'[2,2]X[2,2], ...], ...] +\n", | |
| " # [[z'[B,1]X[B,1], z'[B,2]X[B,1], ...], [z'[B,1]X[B,2], z'[B,2]X[B,2], ...], ...]\n", | |
| " # = [[z'[1,1]X[1,1] + z'[2,1]X[2,1] + ... + z'[B,1]X[B,1], z'[1,2]X[1,1] + z'[2,2]X[2,1] + ... + z'[B,2]X[B,1], ...],\n", | |
| " # [z'[1,1]X[1,2] + z'[2,1]X[2,2] + ... + z'[B,1]X[B,2], z'[1,2]X[1,2] + z'[2,2]X[2,2] + ... + z'[B,2]X[B,2], ...]]\n", | |
| " # = X.T @ z'\n", | |
| " return X.T\n", | |
| "\n", | |
| "def grad_linear_input(W):\n", | |
| " # d(Wx)/dx\n", | |
| " # dz[b,i]/dX[b,j] = W[j,i]\n", | |
| " # dz[b,i]/dX = [[W[1,i], W[2,i], ..., W[j,i]], [0, ..., 0], ...]\n", | |
| " # z'[b,i] * dz[b,i]/dX = [[z'[1,i]W[1,i], z'[1,i]W[2,i], ..., z'[1,i]W[j,i]], [0, ...], ...]\n", | |
| " # sum over b,i: z'[b,i] * dz[b,i]/dX\n", | |
| " # = [[z'[1,1]W[1,1], z'[1,1]W[2,1], ..., z'[1,1]W[j,1]], [z'[2,1]W[1,1], z'[2,1]W[2,1], ..., z'[2,1]W[j,1]], ...] +\n", | |
| " # [[z'[1,2]W[1,2], z'[1,2]W[2,2], ..., z'[1,2]W[j,2]], [z'[2,2]W[1,2], z'[2,2]W[2,2], ..., z'[2,2]W[j,2]], ...] +\n", | |
| " # [[z'[1,i]W[1,i], z'[1,i]W[2,i], ..., z'[1,i]W[j,i]], [z'[2,i]W[1,i], z'[2,i]W[2,i], ..., z'[2,i]W[j,i]], ...]\n", | |
| " # = [[z'[1,1]W[1,1] + z'[1,2]W[1,2] + ... + z'[1,i]W[1,i], z'[1,1]W[2,1] + z'[1,2]W[2,2] + ... + z'[1,i]W[2,i], ...],\n", | |
| " # [z'[2,1]W[1,1] + z'[2,2]W[1,2] + ... + z'[2,i]W[1,i], z'[2,1]W[2,1] + z'[2,2]W[2,2] + ... + z'[2,i]W[2,i], ...]]\n", | |
| " # = z' @ W.T\n", | |
| " return W.T" | |
| ], | |
| "metadata": { | |
| "id": "LGZLQ2zzQQqd" | |
| }, | |
| "execution_count": 198, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from tqdm.auto import tqdm" | |
| ], | |
| "metadata": { | |
| "id": "YgUuvtQeQ8Aj" | |
| }, | |
| "execution_count": 199, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "def one_hot(y, num_classes):\n", | |
| " Y = np.zeros((len(y), num_classes))\n", | |
| " Y[np.arange(len(y)), y] = 1\n", | |
| " return Y" | |
| ], | |
| "metadata": { | |
| "id": "lCbPcDKElxXV" | |
| }, | |
| "execution_count": 200, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "one_hot(np.array([1,2]), 4)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "bqZJfUBxo9WJ", | |
| "outputId": "ab9cadfa-b522-41cb-cccf-a02e1124acfa" | |
| }, | |
| "execution_count": 201, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[0., 1., 0., 0.],\n", | |
| " [0., 0., 1., 0.]])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 201 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sklearn.utils import shuffle" | |
| ], | |
| "metadata": { | |
| "id": "4wD90ue0UB-3" | |
| }, | |
| "execution_count": 202, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class DataLoader:\n", | |
| " def __init__(self, X, y, num_classes, batch_size):\n", | |
| " self.X = X\n", | |
| " self.y = y\n", | |
| " self.num_classes = num_classes\n", | |
| " self.batch_size = batch_size\n", | |
| " self.idx = 0\n", | |
| "\n", | |
| " def __iter__(self):\n", | |
| " return self\n", | |
| "\n", | |
| " def __next__(self):\n", | |
| " if self.idx >= len(self.X):\n", | |
| " self.idx = 0\n", | |
| " # re-shuffle\n", | |
| " X, y = shuffle(self.X, self.y)\n", | |
| " self.X = X\n", | |
| " self.y = y\n", | |
| " raise StopIteration\n", | |
| " X = self.X[self.idx:self.idx+self.batch_size]\n", | |
| " y = one_hot(self.y[self.idx:self.idx+self.batch_size], self.num_classes)\n", | |
| " self.idx += self.batch_size\n", | |
| " return X, y\n", | |
| "\n", | |
| " def __len__(self):\n", | |
| " return -(len(self.X) // -self.batch_size)" | |
| ], | |
| "metadata": { | |
| "id": "AZzF0UmLbMqD" | |
| }, | |
| "execution_count": 203, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "dl = DataLoader(['a', 'b', 'c', 'd'], [0, 1, 2, 3], num_classes=10, batch_size=2)\n", | |
| "for X, y in dl:\n", | |
| " print(X, y)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "sZqKCb60bqsd", | |
| "outputId": "ce362496-10c9-4aac-ed34-4bad3da75b4e" | |
| }, | |
| "execution_count": 204, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "['a', 'b'] [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", | |
| " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]]\n", | |
| "['c', 'd'] [[0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", | |
| " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]]\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "class Trainer:\n", | |
| " def __init__(self, loss, num_epochs, learning_rate):\n", | |
| " self.loss = loss\n", | |
| " self.num_epochs = num_epochs\n", | |
| " self.learning_rate = learning_rate\n", | |
| "\n", | |
| " def fit(self, model, train_dl, valid_dl):\n", | |
| " epochs = tqdm(range(1, self.num_epochs + 1))\n", | |
| " losses = []\n", | |
| " valid_losses = []\n", | |
| " for n in epochs:\n", | |
| " train_batches = tqdm(train_dl)\n", | |
| " for X, y in train_batches:\n", | |
| " loss = self.fit_batch(model, X, y)\n", | |
| " if loss is None:\n", | |
| " print('All gradients are zero, stopping early')\n", | |
| " return\n", | |
| " losses.append(loss)\n", | |
| " train_batches.set_postfix({'loss': loss})\n", | |
| " valid_loss = 0\n", | |
| " for X, y in valid_dl:\n", | |
| " valid_loss += self.loss(model.forward(X)[-1][0], y)\n", | |
| " valid_loss = valid_loss / len(valid_dl)\n", | |
| " valid_losses.append(valid_loss)\n", | |
| " epochs.set_postfix({'epoch': n, 'valid_loss': valid_loss})\n", | |
| " return losses, valid_losses\n", | |
| "\n", | |
| " def fit_batch(self, model, X, y):\n", | |
| " B = len(X)\n", | |
| " L = len(model.weights)\n", | |
| " # forward pass\n", | |
| " outputs = model.forward(X)\n", | |
| " y_pred_logits, y_pred = outputs[-1]\n", | |
| " loss = self.loss(y_pred_logits, y)\n", | |
| " # backprop\n", | |
| " weight_grads = [None] * L\n", | |
| " bias_grads = [None] * L\n", | |
| " assert self.loss == loss_cross_entropy\n", | |
| " grad = grad_loss_cross_entropy_logits(y_pred, y)\n", | |
| " for i, (W, a) in reversed(list(enumerate(zip(model.weights, model.activations)))):\n", | |
| " if a.__name__ == 'relu':\n", | |
| " grad = grad * grad_relu_input(outputs[i][0])\n", | |
| " H = outputs[i-1][-1] if i > 0 else X\n", | |
| " weight_grads[i] = (grad_linear_weight(H) @ grad) / B\n", | |
| " bias_grads[i] = np.sum(grad, axis=0, keepdims=True) / B\n", | |
| " grad = grad @ grad_linear_input(W)\n", | |
| " # update weights\n", | |
| " assert all(g is not None for g in weight_grads)\n", | |
| " assert all(g is not None for g in bias_grads)\n", | |
| " if all(np.all(g == 0) for g in weight_grads):\n", | |
| " return None\n", | |
| " for i in range(L):\n", | |
| " model.weights[i] = model.weights[i] - self.learning_rate * weight_grads[i]\n", | |
| " model.biases[i] = model.biases[i] - self.learning_rate * bias_grads[i]\n", | |
| " return loss" | |
| ], | |
| "metadata": { | |
| "id": "uyoKOSsfN1zI" | |
| }, | |
| "execution_count": 205, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sklearn.datasets import load_digits\n", | |
| "from sklearn.model_selection import train_test_split" | |
| ], | |
| "metadata": { | |
| "id": "1-6-ghvyNlKd" | |
| }, | |
| "execution_count": 206, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "X, y = load_digits(return_X_y=True)\n", | |
| "X = X / 16 # normalize to values between 0 and 1\n", | |
| "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n", | |
| "X_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size=0.5)\n", | |
| "X_train.shape, y_train.shape" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "rz7Czgf0Nxg5", | |
| "outputId": "193a2720-05ba-427f-96ed-6fcbaa5f822a" | |
| }, | |
| "execution_count": 207, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "((1257, 64), (1257,))" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 207 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model = NN(64, [32, 32, 10], [relu, relu, softmax])\n", | |
| "model.weights[0]" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "UuoMIDAp3VYz", | |
| "outputId": "a3cc5b6d-2f5d-447b-b101-42d086896336" | |
| }, | |
| "execution_count": 208, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "array([[ 2.01187949e-02, -1.62628771e-02, -1.45041229e-02, ...,\n", | |
| " 6.60271900e-03, -1.55967580e-02, 2.37582645e-03],\n", | |
| " [-2.39105789e-03, -1.23592585e-03, -8.24851159e-03, ...,\n", | |
| " 1.78936865e-02, 1.10650807e-02, 3.79056812e-03],\n", | |
| " [-1.53236386e-02, 3.51360078e-03, -7.12072767e-04, ...,\n", | |
| " -9.83542576e-05, 1.09826290e-02, -5.93630997e-03],\n", | |
| " ...,\n", | |
| " [ 6.13841545e-03, -4.14395892e-03, 8.60704889e-03, ...,\n", | |
| " -3.87925305e-03, 3.68199517e-03, -8.29479270e-03],\n", | |
| " [ 1.02775358e-02, -4.32102881e-03, 8.15581355e-03, ...,\n", | |
| " 7.87984967e-03, -6.08074843e-03, -9.39037669e-03],\n", | |
| " [ 1.14220588e-02, 3.75991005e-03, 2.71385169e-03, ...,\n", | |
| " 1.69928180e-02, 4.22427233e-03, -1.03103667e-03]])" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 208 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "train_dl = DataLoader(X_train, y_train, num_classes=10, batch_size=32)\n", | |
| "valid_dl = DataLoader(X_val, y_val, num_classes=10, batch_size=len(X_val))\n", | |
| "len(train_dl), len(valid_dl)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "riiI6AKb3ZMY", | |
| "outputId": "53ab62ca-1c13-4bca-863b-739a84e1cb5d" | |
| }, | |
| "execution_count": 209, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "(40, 1)" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 209 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "trainer = Trainer(loss_cross_entropy, num_epochs=50, learning_rate=0.5)\n", | |
| "losses, valid_losses = trainer.fit(model, train_dl, valid_dl)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000, | |
| "referenced_widgets": [ | |
| "a3350a54a087473b86b18fe0596fdfe7", | |
| "756713d28b7b4ab28792404be18dee0a", | |
| "b311ea287866483189a0b0cb847f8652", | |
| "e179da522fb34587b7cfe6beb0ffd161", | |
| "4a64d4511197439c92c8f4237454b44b", | |
| "f6bfcf49bdeb4103a18f8d5b95b2ac41", | |
| "c86f119e82c74390836251bebf06b7f1", | |
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| "72b3433df67c4ce7b179df7341d8118d", | |
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| "b1762d88e81d48a382287f819900727a", | |
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| { | |
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| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "plt.title('Loss vs number of batches')\n", | |
| "plt.plot(np.arange(len(losses)), losses, color='blue')\n", | |
| "plt.plot(np.arange(0, len(losses), len(train_dl)), valid_losses, color='orange')\n", | |
| "plt.legend(('Training loss', 'Validation loss'))\n", | |
| "plt.show()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 452 | |
| }, | |
| "id": "q7wjKdRBdtc8", | |
| "outputId": "c29397b9-e071-4e63-b90a-e0470412e3fd" | |
| }, | |
| "execution_count": 211, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "N = len(X_test)\n", | |
| "correct = 0\n", | |
| "confusion_matrix = np.zeros((10, 10))\n", | |
| "for X, y in DataLoader(X_test, y_test, num_classes=10, batch_size=len(X_test)):\n", | |
| " outputs = model.forward(X)\n", | |
| " y_pred_logits, y_pred = outputs[-1]\n", | |
| " pred_class = np.argmax(y_pred, axis=-1)\n", | |
| " true_class = np.argmax(y, axis=-1)\n", | |
| " correct += np.sum(pred_class == true_class)\n", | |
| " for i in range(len(pred_class)):\n", | |
| " confusion_matrix[true_class[i]][pred_class[i]] += 1\n", | |
| "\n", | |
| "print(f'Accuracy: {correct}/{N} = {correct/N}')\n", | |
| "print(f'Confusion matrix:\\n{confusion_matrix}')" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "VWSNW0LCvJk1", | |
| "outputId": "554250d1-aa06-4707-8aeb-2cbd26f40bc3" | |
| }, | |
| "execution_count": 212, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Accuracy: 262/270 = 0.9703703703703703\n", | |
| "Confusion matrix:\n", | |
| "[[19. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", | |
| " [ 0. 31. 1. 0. 0. 0. 0. 0. 0. 0.]\n", | |
| " [ 0. 0. 26. 1. 0. 0. 0. 0. 0. 0.]\n", | |
| " [ 0. 0. 0. 28. 0. 0. 0. 0. 0. 0.]\n", | |
| " [ 0. 1. 0. 0. 38. 0. 0. 0. 0. 0.]\n", | |
| " [ 0. 0. 0. 1. 0. 29. 0. 0. 0. 1.]\n", | |
| " [ 0. 0. 0. 0. 0. 0. 17. 0. 0. 0.]\n", | |
| " [ 0. 0. 0. 0. 0. 0. 0. 21. 1. 0.]\n", | |
| " [ 0. 0. 0. 0. 0. 0. 0. 0. 32. 0.]\n", | |
| " [ 0. 1. 0. 0. 0. 0. 0. 0. 0. 21.]]\n" | |
| ] | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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