diff --git a/README.md b/README.md
index 77a32bb..6f78859 100644
--- a/README.md
+++ b/README.md
@@ -7,11 +7,12 @@ Download the files via:
```bash
mkdir data
cd data
-curl -Lso "./data/train_img.gz" https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz
-curl -Lso "./data/train_label.gz" https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz
-curl -Lso "./data/test_img.gz" https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz
-curl -Lso "./data/test_label.gz" https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz
+curl -Lso "./train_img.gz" https://storage.googleapis.com/cvdf-datasets/mnist/train-images-idx3-ubyte.gz
+curl -Lso "./train_label.gz" https://storage.googleapis.com/cvdf-datasets/mnist/train-labels-idx1-ubyte.gz
+curl -Lso "./test_img.gz" https://storage.googleapis.com/cvdf-datasets/mnist/t10k-images-idx3-ubyte.gz
+curl -Lso "./test_label.gz" https://storage.googleapis.com/cvdf-datasets/mnist/t10k-labels-idx1-ubyte.gz
gunzip *
+for f in *; do mv "$f" "$f.idx"; done
```
in the directory of the files and rename to .idx
diff --git a/notebooks/benchmark_mnist_mlp.ipynb b/notebooks/benchmark_mnist_mlp.ipynb
new file mode 100644
index 0000000..a3e6edf
--- /dev/null
+++ b/notebooks/benchmark_mnist_mlp.ipynb
@@ -0,0 +1,5076 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# imports\n",
+ "%load_ext autoreload\n",
+ "%autoreload all\n",
+ "import sys, os\n",
+ "\n",
+ "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), os.pardir)))\n",
+ "from src.mnist_mlp import MLP, MLP_NO_OUTPUT_ACTIVATION\n",
+ "from src import *\n",
+ "\n",
+ "import gc\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import itertools, json\n",
+ "import plotly.express as px"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+ "# Plot validation loss in reference to number of params and number of hidden layers\n",
+ "parameters = {\n",
+ " \"num_hidden_layers\": range(1, 10, 2),\n",
+ " \"hidden_layer_size\": range(64, 257, 64),\n",
+ " \"activation\": [\"tanh\", \"relu\"],\n",
+ " \"lr\": np.logspace(-4, -1, 20),\n",
+ " \"model\": [MLP, MLP_NO_OUTPUT_ACTIVATION],\n",
+ "}\n",
+ "# parameters = {\n",
+ "# \"num_hidden_layers\": [1, 3],\n",
+ "# \"hidden_layer_size\": [128, 256],\n",
+ "# \"activation\": [\"tanh\", \"relu\"],\n",
+ "# \"lr\": [3e-4, 1e-3],\n",
+ "# \"model\": [MLP, MLP_NO_OUTPUT_ACTIVATION],\n",
+ "# }\n",
+ "\n",
+ "epochs = 40\n",
+ "batch_size = 128"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_302022/2032211116.py:13: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n",
+ " train_labels = torch.from_numpy(train_labels)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# load MNIST data\n",
+ "train_img_path = \"../data/train_img.idx\"\n",
+ "train_label_path = \"../data/train_label.idx\"\n",
+ "test_img_path = \"../data/test_img.idx\"\n",
+ "test_label_path = \"../data/test_label.idx\"\n",
+ "\n",
+ "train_samples, train_labels, test_samples, test_labels = normalize_mnist_data(\n",
+ " train_img_path, train_label_path, test_img_path, test_label_path\n",
+ ")\n",
+ "\n",
+ "# use tensors\n",
+ "train_samples = torch.from_numpy(train_samples).float()\n",
+ "train_labels = torch.from_numpy(train_labels)\n",
+ "test_samples = torch.from_numpy(test_samples).float()\n",
+ "test_labels = torch.from_numpy(test_labels)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def benchmark_parameters(\n",
+ " model: nn.Module,\n",
+ " train_samples: torch.Tensor,\n",
+ " train_labels: torch.Tensor,\n",
+ " test_samples: torch.Tensor,\n",
+ " test_labels: torch.Tensor,\n",
+ " num_hidden: int,\n",
+ " size_hidden: int,\n",
+ " activation: str,\n",
+ " epochs: int,\n",
+ " batch_size: int,\n",
+ " lr: float,\n",
+ " optim: str = \"adam\",\n",
+ ") -> tuple[float, float, int]:\n",
+ " # setup network\n",
+ " mlp = model(784, 10, np.array(num_hidden * [size_hidden]), activation).to(device)\n",
+ " if optim == \"adam\":\n",
+ " optimizer = torch.optim.AdamW(mlp.parameters(), lr)\n",
+ " else:\n",
+ " optimizer = torch.optim.SGD(mlp.parameters(), lr)\n",
+ " \n",
+ " train_samples = train_samples.to(device=device)\n",
+ " train_labels = train_labels.to(device=device)\n",
+ " test_samples = test_samples.to(device=device)\n",
+ " test_labels = test_labels.to(device=device)\n",
+ " validation_accuracies = torch.Tensor().to(device)\n",
+ " validation_losses = torch.Tensor().to(device)\n",
+ " training_accuracies = torch.Tensor().to(device)\n",
+ " training_losses = torch.Tensor().to(device)\n",
+ "\n",
+ " # train model\n",
+ " for epoch in range(epochs):\n",
+ " # randomize order of training samples and labels\n",
+ " idx = torch.randperm(len(train_samples))\n",
+ " train_samples = train_samples[idx]\n",
+ " train_labels = train_labels[idx]\n",
+ " for i in range(0, len(train_samples), batch_size):\n",
+ " batch_samples = train_samples[i : i + batch_size].flatten(1)\n",
+ " batch_labels = train_labels[i : i + batch_size].flatten()\n",
+ " # forward pass\n",
+ " optimizer.zero_grad()\n",
+ " y_hat = mlp(batch_samples)\n",
+ " loss = F.cross_entropy(y_hat, batch_labels)\n",
+ " # backward pass\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " # test the model\n",
+ " # TODO: keep in arrays -> .detach() instead of .item()\n",
+ " validation_accuracies = torch.cat((validation_accuracies, get_accuracy(mlp, test_samples, test_labels).unsqueeze(0)))\n",
+ " validation_losses = torch.cat((validation_losses, F.cross_entropy(\n",
+ " mlp(test_samples.flatten(1)), test_labels.flatten()\n",
+ " ).unsqueeze(0)))\n",
+ " training_accuracies = torch.cat((training_accuracies, get_accuracy(mlp, train_samples, train_labels).unsqueeze(0)))\n",
+ " training_losses = torch.cat((training_losses, F.cross_entropy(\n",
+ " mlp(train_samples.flatten(1)), train_labels.flatten()\n",
+ " ).unsqueeze(0)))\n",
+ "\n",
+ " return (\n",
+ " validation_accuracies,\n",
+ " validation_losses,\n",
+ " training_accuracies,\n",
+ " training_losses,\n",
+ " mlp.param_count(),\n",
+ " mlp._get_name(),\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "current: {'num_hidden_layers': 1, 'hidden_layer_size': 64, 'activation': 'tanh', 'learning_rate': np.float64(0.0001), 'validation_accuracies': [0.852400004863739, 0.8847999572753906, 0.9000999927520752, 0.9059999585151672, 0.9110999703407288, 0.9142999649047852, 0.91839998960495, 0.9214999675750732, 0.9231999516487122, 0.9261999726295471, 0.9283999800682068, 0.9301999807357788, 0.9334999918937683, 0.9351999759674072, 0.9358999729156494, 0.9377999901771545, 0.9383999705314636, 0.9399999976158142, 0.9404999613761902, 0.9414999485015869, 0.9429000020027161, 0.9440999627113342, 0.9454999566078186, 0.9458999633789062, 0.9475999474525452, 0.9476999640464783, 0.9479999542236328, 0.9491999745368958, 0.9503999948501587, 0.9512999653816223, 0.9519000053405762, 0.9530999660491943, 0.953499972820282, 0.9533999562263489, 0.9542999863624573, 0.9544999599456787, 0.955299973487854, 0.9558999538421631, 0.9558999538421631, 0.9562000036239624], 'validation_losses': [1.3732492923736572, 1.1927753686904907, 1.108560562133789, 1.061122179031372, 1.0313916206359863, 1.0098581314086914, 0.9943252801895142, 0.9817313551902771, 0.9720281362533569, 0.963628888130188, 0.9564316272735596, 0.9503799080848694, 0.9447430372238159, 0.9400917887687683, 0.9354061484336853, 0.9315134882926941, 0.9279078245162964, 0.9242729544639587, 0.9213963747024536, 0.9186037182807922, 0.915760338306427, 0.9137641787528992, 0.9111043214797974, 0.908924400806427, 0.9069396257400513, 0.9052165150642395, 0.9033767580986023, 0.9017788171768188, 0.9002466797828674, 0.8986172080039978, 0.8974626660346985, 0.8959345817565918, 0.8941765427589417, 0.8931265473365784, 0.8920466899871826, 0.8908295035362244, 0.8898552656173706, 0.888772189617157, 0.8878747224807739, 0.8865652084350586], 'training_accuracies': [0.8439333438873291, 0.8744666576385498, 0.8924999833106995, 0.901116669178009, 0.9068000316619873, 0.9122499823570251, 0.9161999821662903, 0.9200000166893005, 0.9227499961853027, 0.9261333346366882, 0.9290500283241272, 0.9312666654586792, 0.9337666630744934, 0.935366690158844, 0.9372666478157043, 0.9390333294868469, 0.9405166506767273, 0.9421499967575073, 0.9434999823570251, 0.9451833367347717, 0.9461333155632019, 0.9470666646957397, 0.9485166668891907, 0.9493833184242249, 0.9507499933242798, 0.951533317565918, 0.9524500370025635, 0.9532999992370605, 0.9540500044822693, 0.9554166793823242, 0.956250011920929, 0.9567333459854126, 0.9575166702270508, 0.9582499861717224, 0.9593999981880188, 0.959933340549469, 0.9604666829109192, 0.9611333608627319, 0.9616166949272156, 0.9625666737556458], 'training_losses': [1.3828206062316895, 1.2027530670166016, 1.1181684732437134, 1.0698297023773193, 1.0388734340667725, 1.0163276195526123, 0.9997100830078125, 0.9861429333686829, 0.9757066369056702, 0.9658873677253723, 0.9578880667686462, 0.9509624242782593, 0.944877564907074, 0.939334511756897, 0.9343169927597046, 0.9298774003982544, 0.9256021976470947, 0.9218952655792236, 0.9184777140617371, 0.9151583909988403, 0.9119994044303894, 0.9092782735824585, 0.9065394997596741, 0.9039962887763977, 0.9017112255096436, 0.8994261026382446, 0.8971740007400513, 0.8951640725135803, 0.8933466672897339, 0.8912511467933655, 0.8896181583404541, 0.8878781199455261, 0.8861650824546814, 0.8844878673553467, 0.8830053806304932, 0.8816813230514526, 0.8801321983337402, 0.8786733150482178, 0.8775424361228943, 0.8760489225387573], 'num_params': 50890, 'model': 'MLP'}\n",
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+ ]
+ },
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[5], line 19\u001b[0m\n\u001b[1;32m 3\u001b[0m parameters_used \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hidden_num, hidden_size, activation, lr, model \u001b[38;5;129;01min\u001b[39;00m itertools\u001b[38;5;241m.\u001b[39mproduct(\n\u001b[1;32m 6\u001b[0m parameters[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_hidden_layers\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 7\u001b[0m parameters[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhidden_layer_size\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 10\u001b[0m parameters[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 11\u001b[0m ):\n\u001b[1;32m 12\u001b[0m (\n\u001b[1;32m 13\u001b[0m validation_accuracies,\n\u001b[1;32m 14\u001b[0m validation_losses,\n\u001b[1;32m 15\u001b[0m training_accuracies,\n\u001b[1;32m 16\u001b[0m training_losses,\n\u001b[1;32m 17\u001b[0m parameter_count,\n\u001b[1;32m 18\u001b[0m model_name,\n\u001b[0;32m---> 19\u001b[0m ) \u001b[38;5;241m=\u001b[39m \u001b[43mbenchmark_parameters\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 23\u001b[0m \u001b[43m \u001b[49m\u001b[43mtest_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 24\u001b[0m \u001b[43m \u001b[49m\u001b[43mtest_labels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 25\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_num\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 27\u001b[0m \u001b[43m \u001b[49m\u001b[43mactivation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 29\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 30\u001b[0m \u001b[43m \u001b[49m\u001b[43mlr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 31\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43madam\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 32\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 33\u001b[0m params \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 34\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_hidden_layers\u001b[39m\u001b[38;5;124m\"\u001b[39m: hidden_num,\n\u001b[1;32m 35\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhidden_layer_size\u001b[39m\u001b[38;5;124m\"\u001b[39m: hidden_size,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m: model_name,\n\u001b[1;32m 44\u001b[0m }\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcurrent: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparams\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+ "Cell \u001b[0;32mIn[4], line 54\u001b[0m, in \u001b[0;36mbenchmark_parameters\u001b[0;34m(model, train_samples, train_labels, test_samples, test_labels, num_hidden, size_hidden, activation, epochs, batch_size, lr, optim)\u001b[0m\n\u001b[1;32m 50\u001b[0m validation_accuracies \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat((validation_accuracies, get_accuracy(mlp, test_samples, test_labels)\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)))\n\u001b[1;32m 51\u001b[0m validation_losses \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat((validation_losses, F\u001b[38;5;241m.\u001b[39mcross_entropy(\n\u001b[1;32m 52\u001b[0m mlp(test_samples\u001b[38;5;241m.\u001b[39mflatten(\u001b[38;5;241m1\u001b[39m)), test_labels\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[1;32m 53\u001b[0m )\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)))\n\u001b[0;32m---> 54\u001b[0m training_accuracies \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat((training_accuracies, \u001b[43mget_accuracy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmlp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_samples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_labels\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)))\n\u001b[1;32m 55\u001b[0m training_losses \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat((training_losses, F\u001b[38;5;241m.\u001b[39mcross_entropy(\n\u001b[1;32m 56\u001b[0m mlp(train_samples\u001b[38;5;241m.\u001b[39mflatten(\u001b[38;5;241m1\u001b[39m)), train_labels\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[1;32m 57\u001b[0m )\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)))\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[1;32m 60\u001b[0m validation_accuracies,\n\u001b[1;32m 61\u001b[0m validation_losses,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 65\u001b[0m mlp\u001b[38;5;241m.\u001b[39m_get_name(),\n\u001b[1;32m 66\u001b[0m )\n",
+ "File \u001b[0;32m~/projects/diy-mnist-nn/src/__init__.py:190\u001b[0m, in \u001b[0;36mget_accuracy\u001b[0;34m(model, samples, labels)\u001b[0m\n\u001b[1;32m 188\u001b[0m labels \u001b[38;5;241m=\u001b[39m labels\u001b[38;5;241m.\u001b[39mflatten()\n\u001b[1;32m 189\u001b[0m y_hat \u001b[38;5;241m=\u001b[39m model(samples\u001b[38;5;241m.\u001b[39mflatten(\u001b[38;5;241m1\u001b[39m))\n\u001b[0;32m--> 190\u001b[0m correct \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msum\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_hat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (correct \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mlen\u001b[39m(samples))\n",
+ "File \u001b[0;32m~/.cache/pypoetry/virtualenvs/diy-mnist-nn-QMraT3eV-py3.12/lib/python3.12/site-packages/torch/_tensor.py:1119\u001b[0m, in \u001b[0;36mTensor.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1110\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_get_tracing_state():\n\u001b[1;32m 1111\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 1112\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIterating over a tensor might cause the trace to be incorrect. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1113\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing a tensor of different shape won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt change the number of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1117\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[1;32m 1118\u001b[0m )\n\u001b[0;32m-> 1119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28miter\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munbind\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m)\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "best_accuracy = 0.0\n",
+ "best_params = {}\n",
+ "parameters_used = []\n",
+ "\n",
+ "for hidden_num, hidden_size, activation, lr, model in itertools.product(\n",
+ " parameters[\"num_hidden_layers\"],\n",
+ " parameters[\"hidden_layer_size\"],\n",
+ " parameters[\"activation\"],\n",
+ " parameters[\"lr\"],\n",
+ " parameters[\"model\"],\n",
+ "):\n",
+ " (\n",
+ " validation_accuracies,\n",
+ " validation_losses,\n",
+ " training_accuracies,\n",
+ " training_losses,\n",
+ " parameter_count,\n",
+ " model_name,\n",
+ " ) = benchmark_parameters(\n",
+ " model,\n",
+ " train_samples,\n",
+ " train_labels,\n",
+ " test_samples,\n",
+ " test_labels,\n",
+ " hidden_num,\n",
+ " hidden_size,\n",
+ " activation,\n",
+ " epochs,\n",
+ " batch_size,\n",
+ " lr,\n",
+ " \"adam\",\n",
+ " )\n",
+ " params = {\n",
+ " \"num_hidden_layers\": hidden_num,\n",
+ " \"hidden_layer_size\": hidden_size,\n",
+ " \"activation\": activation,\n",
+ " \"learning_rate\": lr,\n",
+ " \"validation_accuracies\": validation_accuracies.detach().cpu().numpy().tolist(),\n",
+ " \"validation_losses\": validation_losses.detach().cpu().numpy().tolist(),\n",
+ " \"training_accuracies\": training_accuracies.detach().cpu().numpy().tolist(),\n",
+ " \"training_losses\": training_losses.detach().cpu().numpy().tolist(),\n",
+ " \"num_params\": parameter_count,\n",
+ " \"model\": model_name,\n",
+ " }\n",
+ "\n",
+ " print(f\"current: {params}\")\n",
+ " with open(\"../data/parameters_used.jsonl\", \"a\") as file:\n",
+ " json.dump(params, file, indent=None)\n",
+ " file.write(\"\\n\")\n",
+ "\n",
+ "gc.collect()\n",
+ "torch.cuda.empty_cache()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " num_hidden_layers hidden_layer_size activation learning_rate \\\n",
+ "0 1 64 tanh 0.000100 \n",
+ "1 1 64 tanh 0.000100 \n",
+ "2 1 64 tanh 0.000144 \n",
+ "3 1 64 tanh 0.000144 \n",
+ "4 1 64 tanh 0.000207 \n",
+ "5 1 64 tanh 0.000207 \n",
+ "6 1 64 tanh 0.000298 \n",
+ "7 1 64 tanh 0.000298 \n",
+ "8 1 64 tanh 0.000428 \n",
+ "\n",
+ " validation_accuracies \\\n",
+ "0 [0.852400004863739, 0.8847999572753901, 0.9000... \n",
+ "1 [0.8513000011444091, 0.8908999562263481, 0.903... \n",
+ "2 [0.874499976634979, 0.8976999521255491, 0.9073... \n",
+ "3 [0.8686999678611751, 0.8978999853134151, 0.910... \n",
+ "4 [0.88809996843338, 0.907499969005584, 0.917199... \n",
+ "5 [0.8859999775886531, 0.9091999530792231, 0.918... \n",
+ "6 [0.8980999588966371, 0.9135999679565431, 0.922... \n",
+ "7 [0.9028999805450441, 0.91839998960495, 0.92839... \n",
+ "8 [0.9071999788284301, 0.9194999933242791, 0.929... \n",
+ "\n",
+ " validation_losses \\\n",
+ "0 [1.373249292373657, 1.19277536869049, 1.108560... \n",
+ "1 [0.8249649405479431, 0.5107517838478081, 0.401... \n",
+ "2 [1.269325137138366, 1.115129113197326, 1.05091... \n",
+ "3 [0.6481606960296631, 0.41510558128356906, 0.33... \n",
+ "4 [1.181039810180664, 1.057238578796386, 1.00748... \n",
+ "5 [0.509513437747955, 0.34663820266723605, 0.291... \n",
+ "6 [1.110373735427856, 1.013886094093322, 0.97583... \n",
+ "7 [0.400189399719238, 0.295490562915802, 0.25331... \n",
+ "8 [1.053267240524292, 0.9827032089233391, 0.9538... \n",
+ "\n",
+ " training_accuracies \\\n",
+ "0 [0.8439333438873291, 0.874466657638549, 0.8924... \n",
+ "1 [0.841250002384185, 0.8799833655357361, 0.8963... \n",
+ "2 [0.865533351898193, 0.891799986362457, 0.90241... \n",
+ "3 [0.8619499802589411, 0.894233345985412, 0.9072... \n",
+ "4 [0.8794000148773191, 0.900849997997283, 0.9137... \n",
+ "5 [0.8797000050544731, 0.905483365058898, 0.9164... \n",
+ "6 [0.8913166522979731, 0.9107333421707151, 0.921... \n",
+ "7 [0.8951166868209831, 0.917083323001861, 0.9276... \n",
+ "8 [0.9021000266075131, 0.9195166826248161, 0.929... \n",
+ "\n",
+ " training_losses num_params \\\n",
+ "0 [1.382820606231689, 1.2027530670166011, 1.1181... 50890 \n",
+ "1 [0.840439558029174, 0.524856865406036, 0.41254... 50890 \n",
+ "2 [1.277857542037963, 1.123657703399658, 1.05907... 50890 \n",
+ "3 [0.6638996005058281, 0.42745333909988403, 0.34... 50890 \n",
+ "4 [1.191100478172302, 1.065655708312988, 1.01372... 50890 \n",
+ "5 [0.522558212280273, 0.35676458477973905, 0.299... 50890 \n",
+ "6 [1.1200891733169551, 1.020868182182312, 0.9807... 50890 \n",
+ "7 [0.41396418213844305, 0.300841629505157, 0.255... 50890 \n",
+ "8 [1.062315106391906, 0.986643135547637, 0.95454... 50890 \n",
+ "\n",
+ " model \n",
+ "0 MLP \n",
+ "1 MLP_NO_OUTPUT_ACTIVATION \n",
+ "2 MLP \n",
+ "3 MLP_NO_OUTPUT_ACTIVATION \n",
+ "4 MLP \n",
+ "5 MLP_NO_OUTPUT_ACTIVATION \n",
+ "6 MLP \n",
+ "7 MLP_NO_OUTPUT_ACTIVATION \n",
+ "8 MLP \n"
+ ]
+ }
+ ],
+ "source": [
+ "# with open(\"../data/parameters_used_test.json\", \"r\") as file:\n",
+ "# parameters_used = json.load(file)\n",
+ "\n",
+ "# plot the results\n",
+ "# num_hidden_layers = [result[\"num_hidden_layers\"] for result in parameters_used]\n",
+ "# hidden_layer_size = [result[\"hidden_layer_size\"] for result in parameters_used]\n",
+ "# num_params = [result[\"num_params\"] for result in parameters_used]\n",
+ "# validation_accuracy = [result[\"validation_accuracy\"] for result in parameters_used]\n",
+ "# validation_loss = [result[\"validation_loss\"] for result in parameters_used]\n",
+ "# training_accuracy = [result[\"training_accuracy\"] for result in parameters_used]\n",
+ "# training_loss = [result[\"training_loss\"] for result in parameters_used]\n",
+ "# learning_rate = [result[\"learning_rate\"] for result in parameters_used]\n",
+ "# activation = [result[\"activation\"] for result in parameters_used]\n",
+ "# model = [result[\"model\"] for result in parameters_used]\n",
+ "\n",
+ "# df = pd.DataFrame(\n",
+ "# {\n",
+ "# \"num_hidden_layers\": num_hidden_layers,\n",
+ "# \"hidden_layer_size\": hidden_layer_size,\n",
+ "# \"num_params\": num_params,\n",
+ "# \"learning_rate\": learning_rate,\n",
+ "# \"validation_accuracy\": validation_accuracy,\n",
+ "# \"validation_loss\": validation_loss,\n",
+ "# \"training_accuracy\": training_accuracy,\n",
+ "# \"training_loss\": training_loss,\n",
+ "# \"activation\": activation,\n",
+ "# \"model\": model,\n",
+ "# }\n",
+ "# )\n",
+ "\n",
+ "df = pd.read_json(\"../data/parameters_used.jsonl\", lines=True)\n",
+ "\n",
+ "df_tanh = df[df[\"activation\"] == \"tanh\"]\n",
+ "\n",
+ "df_relu = df[df[\"activation\"] == \"relu\"]\n",
+ "\n",
+ "print(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ " "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
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model=MLP
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+ "color": "#636efa",
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+ "marker": {
+ "symbol": "circle"
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+ "mode": "lines+markers",
+ "name": "tanh",
+ "orientation": "v",
+ "showlegend": true,
+ "type": "scatter",
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+ }
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+ "source": [
+ "fig = px.line(\n",
+ " df_relu,\n",
+ " x=\"hidden_layer_size\",\n",
+ " y=[\"validation_accuracy\", \"training_accuracy\"],\n",
+ " color=\"num_hidden_layers\",\n",
+ " text=\"activation\",\n",
+ " facet_row=\"learning_rate\",\n",
+ " facet_col=\"model\",\n",
+ ")\n",
+ "fig.add_traces(\n",
+ " px.line(\n",
+ " df_tanh,\n",
+ " x=\"hidden_layer_size\",\n",
+ " y=[\"validation_accuracy\", \"training_accuracy\"],\n",
+ " color=\"num_hidden_layers\",\n",
+ " text=\"activation\",\n",
+ " facet_row=\"learning_rate\",\n",
+ " facet_col=\"model\",\n",
+ " ).data\n",
+ ")\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# TODO:\n",
+ "- \n",
+ "- "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "diy-mnist-nn-QMraT3eV-py3.12",
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+ "name": "python3"
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+ "nbconvert_exporter": "python",
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+}
diff --git a/notebooks/pytorch_mnist.ipynb b/notebooks/pytorch_mnist.ipynb
new file mode 100644
index 0000000..ab08389
--- /dev/null
+++ b/notebooks/pytorch_mnist.ipynb
@@ -0,0 +1,416 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys, os\n",
+ "\n",
+ "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), os.pardir)))\n",
+ "from src import *\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "import time\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import plotly.express as px"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load MNIST data\n",
+ "train_img_path = \"../data/train_img.idx\"\n",
+ "train_label_path = \"../data/train_label.idx\"\n",
+ "test_img_path = \"../data/test_img.idx\"\n",
+ "test_label_path = \"../data/test_label.idx\"\n",
+ "\n",
+ "train_samples, train_labels, test_samples, test_labels = normalize_mnist_data(\n",
+ " train_img_path, train_label_path, test_img_path, test_label_path\n",
+ ")\n",
+ "\n",
+ "# use tensors\n",
+ "train_samples = torch.from_numpy(train_samples).float()\n",
+ "train_labels = torch.from_numpy(train_labels)\n",
+ "test_samples = torch.from_numpy(test_samples).float()\n",
+ "test_labels = torch.from_numpy(test_labels)\n",
+ "\n",
+ "# hyperparameters\n",
+ "epochs = 40\n",
+ "batch_size = 128\n",
+ "lr = 1e-3\n",
+ "\n",
+ "nin = 784\n",
+ "nhidden = 600\n",
+ "nout = 10\n",
+ "\n",
+ "# initialize model and optimizer\n",
+ "m = nn.Sequential(\n",
+ " nn.Linear(nin, 2 * nhidden),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(2 * nhidden, nhidden),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(nhidden, nhidden),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(nhidden, nhidden // 2),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(nhidden // 2, nout),\n",
+ ")\n",
+ "\n",
+ "optimizer = torch.optim.AdamW(m.parameters(), lr=lr)\n",
+ "\n",
+ "# plot parameters\n",
+ "\n",
+ "train_loss = []\n",
+ "validation_loss = []\n",
+ "train_accuracy = []\n",
+ "test_accuracy = []\n",
+ "times = []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # load model parameters\n",
+ "# m.load_state_dict(torch.load(\"../data/mnist_model.pth\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Train Loop"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch: 1, mean batch loss: 0.2659 +- 0.3005\n",
+ "tensor([5842, 6742, 5923, ..., 6265, 5923, 5918])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 2, mean batch loss: 0.0949 +- 0.0444\n",
+ "tensor([5949, 6742, 6131, ..., 6131, 6742, 6742])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 3, mean batch loss: 0.0642 +- 0.0417\n",
+ "tensor([6742, 6265, 6131, ..., 5923, 5918, 5842])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 4, mean batch loss: 0.0515 +- 0.0356\n",
+ "tensor([5949, 5923, 5842, ..., 6265, 6742, 6131])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 5, mean batch loss: 0.0393 +- 0.0292\n",
+ "tensor([5949, 5918, 5949, ..., 6265, 6265, 5958])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 6, mean batch loss: 0.0338 +- 0.0312\n",
+ "tensor([6265, 5923, 5421, ..., 5851, 6742, 6265])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 7, mean batch loss: 0.0301 +- 0.0281\n",
+ "tensor([5958, 5842, 5923, ..., 5958, 5958, 6131])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 8, mean batch loss: 0.0258 +- 0.0270\n",
+ "tensor([5918, 6265, 5851, ..., 5923, 5958, 6265])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 9, mean batch loss: 0.0232 +- 0.0229\n",
+ "tensor([5851, 5842, 5923, ..., 5958, 5918, 6742])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 10, mean batch loss: 0.0220 +- 0.0236\n",
+ "tensor([5851, 5923, 5842, ..., 5949, 5918, 5958])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 11, mean batch loss: 0.0183 +- 0.0230\n",
+ "tensor([6742, 5851, 5949, ..., 5958, 5851, 5949])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 12, mean batch loss: 0.0162 +- 0.0218\n",
+ "tensor([6131, 5918, 6742, ..., 5918, 5949, 6742])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 13, mean batch loss: 0.0196 +- 0.0269\n",
+ "tensor([6131, 5851, 5949, ..., 6131, 5421, 5958])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 14, mean batch loss: 0.0134 +- 0.0184\n",
+ "tensor([5842, 5958, 5923, ..., 6265, 5958, 5842])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 15, mean batch loss: 0.0149 +- 0.0236\n",
+ "tensor([6742, 5958, 6131, ..., 5918, 5421, 5421])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 16, mean batch loss: 0.0132 +- 0.0223\n",
+ "tensor([5918, 5842, 5918, ..., 5958, 5923, 5958])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 17, mean batch loss: 0.0141 +- 0.0233\n",
+ "tensor([5421, 6131, 5851, ..., 5958, 5918, 6265])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 18, mean batch loss: 0.0134 +- 0.0221\n",
+ "tensor([5842, 5421, 6742, ..., 5918, 6265, 5958])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 19, mean batch loss: 0.0118 +- 0.0191\n",
+ "tensor([5923, 6131, 5958, ..., 6265, 5842, 5918])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 20, mean batch loss: 0.0110 +- 0.0189\n",
+ "tensor([6265, 6131, 5958, ..., 5958, 5949, 6265])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 21, mean batch loss: 0.0109 +- 0.0204\n",
+ "tensor([5949, 6742, 5842, ..., 6742, 5923, 5851])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 22, mean batch loss: 0.0131 +- 0.0207\n",
+ "tensor([5842, 5851, 5949, ..., 6265, 6265, 6131])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 23, mean batch loss: 0.0086 +- 0.0160\n",
+ "tensor([5923, 5958, 5923, ..., 5851, 6742, 6265])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 24, mean batch loss: 0.0084 +- 0.0178\n",
+ "tensor([5851, 5421, 5421, ..., 6265, 5958, 5851])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 25, mean batch loss: 0.0120 +- 0.0220\n",
+ "tensor([6742, 6742, 5918, ..., 5923, 6265, 6742])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 26, mean batch loss: 0.0082 +- 0.0171\n",
+ "tensor([6265, 5918, 5958, ..., 5949, 5958, 5958])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 27, mean batch loss: 0.0081 +- 0.0209\n",
+ "tensor([5923, 6131, 5949, ..., 5923, 5949, 5923])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 28, mean batch loss: 0.0093 +- 0.0185\n",
+ "tensor([6265, 5851, 5958, ..., 6742, 5958, 6742])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 29, mean batch loss: 0.0087 +- 0.0195\n",
+ "tensor([5949, 5421, 6742, ..., 5842, 5842, 6131])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 30, mean batch loss: 0.0075 +- 0.0162\n",
+ "tensor([5421, 6742, 5918, ..., 5949, 5421, 5918])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 31, mean batch loss: 0.0091 +- 0.0182\n",
+ "tensor([6742, 5421, 5958, ..., 6742, 5923, 5918])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 32, mean batch loss: 0.0063 +- 0.0170\n",
+ "tensor([5923, 5918, 5923, ..., 6742, 5949, 5851])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 33, mean batch loss: 0.0058 +- 0.0160\n",
+ "tensor([5851, 6742, 6265, ..., 5851, 5842, 5949])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 34, mean batch loss: 0.0101 +- 0.0247\n",
+ "tensor([5949, 6742, 5949, ..., 5958, 6131, 5851])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 35, mean batch loss: 0.0112 +- 0.0266\n",
+ "tensor([5918, 5918, 6131, ..., 6742, 6742, 6742])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 36, mean batch loss: 0.0105 +- 0.0227\n",
+ "tensor([5923, 5923, 5851, ..., 6131, 5421, 5949])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 37, mean batch loss: 0.0084 +- 0.0170\n",
+ "tensor([6742, 5958, 5851, ..., 5842, 6131, 5958])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 38, mean batch loss: 0.0045 +- 0.0157\n",
+ "tensor([5918, 6742, 5421, ..., 5851, 5918, 6131])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 39, mean batch loss: 0.0048 +- 0.0174\n",
+ "tensor([5923, 5421, 5923, ..., 5918, 5421, 5851])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n",
+ "Epoch: 40, mean batch loss: 0.0066 +- 0.0133\n",
+ "tensor([6742, 5421, 5842, ..., 5842, 5851, 6265])\n",
+ "tensor([1028, 1032, 1135, ..., 982, 892, 958])\n"
+ ]
+ }
+ ],
+ "source": [
+ "# train model\n",
+ "for epoch in range(epochs):\n",
+ " start_time = time.process_time()\n",
+ " # randomize order of training samples and labels\n",
+ " idx = torch.randperm(len(train_samples))\n",
+ " train_samples = train_samples[idx]\n",
+ " train_labels = train_labels[idx]\n",
+ " batch_losses = []\n",
+ " for i in range(0, len(train_samples), batch_size):\n",
+ " # forward pass\n",
+ " y_hat = m(train_samples[i : i + batch_size].flatten(1))\n",
+ " loss = F.cross_entropy(y_hat, train_labels[i : i + batch_size].flatten())\n",
+ " # backward pass\n",
+ " optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " batch_losses.append(loss.item()) # TODO: batch loss not additive\n",
+ " mean_batch_loss = torch.mean(torch.Tensor(batch_losses)).item()\n",
+ " std_batch_loss = torch.std(torch.Tensor(batch_losses)).item()\n",
+ " print(\n",
+ " f\"Epoch: {epoch + 1}, mean batch loss: {mean_batch_loss:.4f} +- {std_batch_loss:.4f}\"\n",
+ " )\n",
+ " end_time = time.process_time()\n",
+ " # epoch loss with training and validation dataset\n",
+ " train_loss = F.cross_entropy(m(train_samples.flatten(1)), train_labels.flatten())\n",
+ " validation_loss = F.cross_entropy(\n",
+ " m(train_samples.flatten(1)), train_labels.flatten()\n",
+ " )\n",
+ " train_accuracy.append(get_accuracy(m, train_samples, train_labels))\n",
+ " test_accuracy.append(get_accuracy(m, test_samples, test_labels))\n",
+ " times.append(end_time - start_time)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([7, 2, 1, 0, 4, 1, 4, 9, 5, 9]) \n",
+ " tensor([[7],\n",
+ " [2],\n",
+ " [1],\n",
+ " [0],\n",
+ " [4],\n",
+ " [1],\n",
+ " [4],\n",
+ " [9],\n",
+ " [5],\n",
+ " [9]], dtype=torch.uint8)\n"
+ ]
+ }
+ ],
+ "source": [
+ "sampl = test_samples[:10]\n",
+ "labl = test_labels[:10]\n",
+ "\n",
+ "ypred = m(sampl.flatten(1))\n",
+ "\n",
+ "print(torch.argmax(ypred, dim=1), \"\\n\", labl)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([1, 1, 2, 1, 2, 2, 2, 2, 1, 2])\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "tensor([0.1000, 0.1000, 0.2000, 0.1000, 0.2000, 0.2000, 0.2000, 0.2000, 0.1000,\n",
+ " 0.2000])"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "get_accuracy(m, sampl, labl)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # save model parameters\n",
+ "# torch.save(m.state_dict(), \"../data/mnist_model.pth\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Validation Accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# test model accuracy\n",
+ "test_acc = get_accuracy(m, test_samples, test_labels)\n",
+ "\n",
+ "print(f\"accuracy: {test_acc:.2%}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# plot the values\n",
+ "\n",
+ "df = pd.DataFrame(\n",
+ " {\n",
+ " \"epoch\": range(1, epochs + 1),\n",
+ " \"train_loss\": train_loss,\n",
+ " \"validation_loss\": validation_loss,\n",
+ " \"train_accuracy\": train_accuracy,\n",
+ " \"test_accuracy\": test_accuracy,\n",
+ " \"times\": times,\n",
+ " }\n",
+ ")\n",
+ "\n",
+ "fig = px.line(\n",
+ " df,\n",
+ " x=\"epoch\",\n",
+ " y=[\"train_loss\", \"validation_loss\", \"train_accuracy\", \"test_accuracy\"],\n",
+ " log_y=True,\n",
+ ")\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = px.line(df, x=\"epoch\", y=\"times\")\n",
+ "fig.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "diy-mnist-nn-QMraT3eV-py3.12",
+ "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": "3.12.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/poetry.lock b/poetry.lock
index c6a00a9..8e45745 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,25 +1,25 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
[[package]]
name = "anyio"
-version = "4.6.2.post1"
+version = "4.8.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.9"
files = [
- {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
- {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
+ {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"},
+ {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"},
]
[package.dependencies]
exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
idna = ">=2.8"
sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
[package.extras]
-doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
trio = ["trio (>=0.26.1)"]
[[package]]
@@ -111,21 +111,18 @@ test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock
[[package]]
name = "asttokens"
-version = "2.4.1"
+version = "3.0.0"
description = "Annotate AST trees with source code positions"
optional = false
-python-versions = "*"
+python-versions = ">=3.8"
files = [
- {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
- {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
+ {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"},
+ {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"},
]
-[package.dependencies]
-six = ">=1.12.0"
-
[package.extras]
-astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
-test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+astroid = ["astroid (>=2,<4)"]
+test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"]
[[package]]
name = "async-lru"
@@ -143,19 +140,19 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
[[package]]
name = "attrs"
-version = "24.2.0"
+version = "24.3.0"
description = "Classes Without Boilerplate"
optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
files = [
- {file = "attrs-24.2.0-py3-none-any.whl", hash = "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"},
- {file = "attrs-24.2.0.tar.gz", hash = "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346"},
+ {file = "attrs-24.3.0-py3-none-any.whl", hash = "sha256:ac96cd038792094f438ad1f6ff80837353805ac950cd2aa0e0625ef19850c308"},
+ {file = "attrs-24.3.0.tar.gz", hash = "sha256:8f5c07333d543103541ba7be0e2ce16eeee8130cb0b3f9238ab904ce1e85baff"},
]
[package.extras]
benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
-dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
+dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
@@ -245,31 +242,31 @@ uvloop = ["uvloop (>=0.15.2)"]
[[package]]
name = "bleach"
-version = "6.1.0"
+version = "6.2.0"
description = "An easy safelist-based HTML-sanitizing tool."
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"},
- {file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"},
+ {file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"},
+ {file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"},
]
[package.dependencies]
-six = ">=1.9.0"
+tinycss2 = {version = ">=1.1.0,<1.5", optional = true, markers = "extra == \"css\""}
webencodings = "*"
[package.extras]
-css = ["tinycss2 (>=1.1.0,<1.3)"]
+css = ["tinycss2 (>=1.1.0,<1.5)"]
[[package]]
name = "certifi"
-version = "2024.8.30"
+version = "2024.12.14"
description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
files = [
- {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
- {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
+ {file = "certifi-2024.12.14-py3-none-any.whl", hash = "sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56"},
+ {file = "certifi-2024.12.14.tar.gz", hash = "sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db"},
]
[[package]]
@@ -353,127 +350,114 @@ pycparser = "*"
[[package]]
name = "charset-normalizer"
-version = "3.4.0"
+version = "3.4.1"
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
optional = false
-python-versions = ">=3.7.0"
-files = [
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certifi = "*"
httpcore = "==1.*"
idna = "*"
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brotli = ["brotli", "brotlicffi"]
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[[package]]
name = "ipython"
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description = "IPython: Productive Interactive Computing"
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python-versions = ">=3.10"
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pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
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pygments = ">=2.4.0"
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traitlets = ">=5.13.0"
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kernel = ["ipykernel"]
matplotlib = ["matplotlib"]
nbconvert = ["nbconvert"]
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python-versions = ">=3.6"
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[[package]]
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[[package]]
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files = [
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- {file = "types_python_dateutil-2.9.0.20241003-py3-none-any.whl", hash = "sha256:250e1d8e80e7bbc3a6c99b907762711d1a1cdd00e978ad39cb5940f6f0a87f3d"},
+ {file = "types_python_dateutil-2.9.0.20241206-py3-none-any.whl", hash = "sha256:e248a4bc70a486d3e3ec84d0dc30eec3a5f979d6e7ee4123ae043eedbb987f53"},
+ {file = "types_python_dateutil-2.9.0.20241206.tar.gz", hash = "sha256:18f493414c26ffba692a72369fea7a154c502646301ebfe3d56a04b3767284cb"},
]
[[package]]
@@ -3378,13 +3349,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
[[package]]
name = "urllib3"
-version = "2.2.3"
+version = "2.3.0"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "urllib3-2.2.3-py3-none-any.whl", hash = "sha256:ca899ca043dcb1bafa3e262d73aa25c465bfb49e0bd9dd5d59f1d0acba2f8fac"},
- {file = "urllib3-2.2.3.tar.gz", hash = "sha256:e7d814a81dad81e6caf2ec9fdedb284ecc9c73076b62654547cc64ccdcae26e9"},
+ {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
+ {file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
]
[package.extras]
@@ -3406,19 +3377,15 @@ files = [
[[package]]
name = "webcolors"
-version = "24.8.0"
+version = "24.11.1"
description = "A library for working with the color formats defined by HTML and CSS."
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "webcolors-24.8.0-py3-none-any.whl", hash = "sha256:fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a"},
- {file = "webcolors-24.8.0.tar.gz", hash = "sha256:08b07af286a01bcd30d583a7acadf629583d1f79bfef27dd2c2c5c263817277d"},
+ {file = "webcolors-24.11.1-py3-none-any.whl", hash = "sha256:515291393b4cdf0eb19c155749a096f779f7d909f7cceea072791cb9095b92e9"},
+ {file = "webcolors-24.11.1.tar.gz", hash = "sha256:ecb3d768f32202af770477b8b65f318fa4f566c22948673a977b00d589dd80f6"},
]
-[package.extras]
-docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
-tests = ["coverage[toml]"]
-
[[package]]
name = "webencodings"
version = "0.5.1"
@@ -3460,4 +3427,4 @@ files = [
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
-content-hash = "f3cfcd12343c337c645a355726f1be9d65276169daf77f3e082f733d730f501a"
+content-hash = "fb09ea0c30b12d3f818aa05c106c69869b77d03827cf22aacb87fb5be6b56049"
diff --git a/pyproject.toml b/pyproject.toml
index 39e287b..336d39f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,13 +8,13 @@ packages = [{ include = "src" }, ]
[tool.poetry.dependencies]
python = "^3.10"
+torch = "^2.5.1"
[tool.poetry.group.dev.dependencies]
numpy = "^2.1.2"
matplotlib = "^3.9.2"
pytest = "^8.3.3"
black = {extras = ["jupyter"], version = "^24.10.0"}
-torch = "^2.5.0"
plotly = "^5.24.1"
ipykernel = "^6.29.5"
nbconvert = "^7.16.4"
diff --git a/src/__init__.py b/src/__init__.py
index b338882..db6db39 100644
--- a/src/__init__.py
+++ b/src/__init__.py
@@ -1,6 +1,8 @@
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
+import torch
+import torch.nn as nn
def get_number_of_samples(
@@ -101,6 +103,26 @@ def binary_parse_mnist_data(
)
+def normalize_mnist_data(
+ idx_file_training_samples: str,
+ idx_file_training_labels: str,
+ idx_file_test_samples: str,
+ idx_file_test_labels: str,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+ """
+ Returns the MNIST dataset as normalized arrays
+ """
+ training_samples, training_labels, test_samples, test_labels = parse_mnist_data(
+ idx_file_training_samples,
+ idx_file_training_labels,
+ idx_file_test_samples,
+ idx_file_test_labels,
+ )
+ training_samples = training_samples / 255
+ test_samples = test_samples / 255
+ return training_samples, training_labels, test_samples, test_labels
+
+
# from https://yann.lecun.com/exdb/mnist/
def parse_mnist_data(
idx_file_training_samples: str,
@@ -159,6 +181,17 @@ def parse_mnist_labels(idx_file_path: str) -> np.ndarray:
return out
+def get_accuracy(
+ model: nn.Module, samples: torch.Tensor, labels: torch.Tensor
+) -> float:
+ with torch.no_grad():
+ labels = labels.flatten()
+ y_hat = model(samples.flatten(1))
+ correct = sum(torch.argmax(y_hat, dim=1) == labels)
+
+ return (correct / len(samples))
+
+
def plot_image(img: np.ndarray) -> plt.Figure:
assert len(img.shape) == 2, "input must be 2-dimensional (single image)"
diff --git a/src/mnist_mlp.py b/src/mnist_mlp.py
new file mode 100644
index 0000000..d7aef97
--- /dev/null
+++ b/src/mnist_mlp.py
@@ -0,0 +1,64 @@
+import torch
+import torch.nn as nn
+
+
+class MLP(nn.Module):
+
+ def __init__(
+ self, fan_in: int, fan_out: int, dim_hidden_layers: list[int], activation: str
+ ) -> None:
+ super(MLP, self).__init__()
+ self.l_in = nn.Linear(fan_in, dim_hidden_layers[0])
+ self.ls_hidden = nn.ModuleList(
+ [
+ nn.Linear(dim_hidden_layers[i], dim_hidden_layers[i + 1])
+ for i in range(len(dim_hidden_layers) - 1)
+ ]
+ )
+ self.l_out = nn.Linear(dim_hidden_layers[-1], fan_out)
+ if activation == "tanh":
+ self.activation = nn.Tanh()
+ else:
+ self.activation = nn.ReLU()
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ x = self.activation(self.l_in(x))
+ for _, l_hidden in enumerate(self.ls_hidden):
+ x = self.activation(l_hidden(x))
+ out = self.activation(self.l_out(x))
+ return out
+
+ def param_count(self) -> int:
+ count = sum(p.numel() for p in self.parameters())
+ return count
+
+
+class MLP_NO_OUTPUT_ACTIVATION(nn.Module):
+ # try leakyReLU if no improvement
+ def __init__(
+ self, fan_in: int, fan_out: int, dim_hidden_layers: list[int], activation: str
+ ) -> None:
+ super(MLP_NO_OUTPUT_ACTIVATION, self).__init__()
+ self.l_in = nn.Linear(fan_in, dim_hidden_layers[0])
+ self.ls_hidden = nn.ModuleList(
+ [
+ nn.Linear(dim_hidden_layers[i], dim_hidden_layers[i + 1])
+ for i in range(len(dim_hidden_layers) - 1)
+ ]
+ )
+ self.l_out = nn.Linear(dim_hidden_layers[-1], fan_out)
+ if activation == "tanh":
+ self.activation = nn.Tanh()
+ else:
+ self.activation = nn.ReLU()
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ x = self.activation(self.l_in(x))
+ for _, l_hidden in enumerate(self.ls_hidden):
+ x = self.activation(l_hidden(x))
+ out = self.l_out(x)
+ return out
+
+ def param_count(self) -> int:
+ count = sum(p.numel() for p in self.parameters())
+ return count