File size: 9,634 Bytes
fc0f7bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "j_LlXHYcmRaC"
},
"source": [
"# MLP MNIST\n",
"\n",
"[](https://colab.sandbox.google.com/github/google-deepmind/optax/blob/main/examples/mlp_mnist.ipynb)\n",
"\n",
"This notebook trains a simple Multilayer Perceptron (MLP) classifier for hand-written digit recognition (MNIST dataset)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9cu0kFNrnJj7"
},
"outputs": [],
"source": [
"from typing import Sequence\n",
"\n",
"from flax import linen as nn\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import optax\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import tensorflow_datasets as tfds"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2Adl_l_uZs1d"
},
"outputs": [],
"source": [
"# @markdown The learning rate for the optimizer:\n",
"LEARNING_RATE = 0.002 # @param{type:\"number\"}\n",
"# @markdown Number of samples in each batch:\n",
"BATCH_SIZE = 128 # @param{type:\"integer\"}\n",
"# @markdown Total number of epochs to train for:\n",
"N_EPOCHS = 1 # @param{type:\"integer\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZZej3FcOhuRE"
},
"source": [
"MNIST is a dataset of 28x28 images with 1 channel. We now load the dataset using `tensorflow_datasets`, apply min-max normalization to the images, shuffle the data in the train set and create batches of size `BATCH_SIZE`.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xPZ0paOehHWg"
},
"outputs": [],
"source": [
"(train_loader, test_loader), info = tfds.load(\n",
" \"mnist\", split=[\"train\", \"test\"], as_supervised=True, with_info=True\n",
")\n",
"\n",
"min_max_rgb = lambda image, label: (tf.cast(image, tf.float32) / 255., label)\n",
"train_loader = train_loader.map(min_max_rgb)\n",
"test_loader = test_loader.map(min_max_rgb)\n",
"\n",
"NUM_CLASSES = info.features[\"label\"].num_classes\n",
"IMG_SIZE = info.features[\"image\"].shape\n",
"\n",
"train_loader_batched = train_loader.shuffle(\n",
" buffer_size=10_000, reshuffle_each_iteration=True\n",
").batch(BATCH_SIZE, drop_remainder=True)\n",
"\n",
"test_loader_batched = test_loader.batch(BATCH_SIZE, drop_remainder=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XkLaC2MlbAqa"
},
"source": [
"The data is ready! Next let's define a model. Optax is agnostic to which (if any) neural network library is used. Here we use Flax to implement a simple MLP."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RppusWrcaXzX"
},
"outputs": [],
"source": [
"class MLP(nn.Module):\n",
" \"\"\"A simple multilayer perceptron model for image classification.\"\"\"\n",
" hidden_sizes: Sequence[int] = (1000, 1000)\n",
"\n",
" @nn.compact\n",
" def __call__(self, x):\n",
" # Flattens images in the batch.\n",
" x = x.reshape((x.shape[0], -1))\n",
" x = nn.Dense(features=self.hidden_sizes[0])(x)\n",
" x = nn.relu(x)\n",
" x = nn.Dense(features=self.hidden_sizes[1])(x)\n",
" x = nn.relu(x)\n",
" x = nn.Dense(features=NUM_CLASSES)(x)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DKOi55MgdPyp"
},
"outputs": [],
"source": [
"net = MLP()\n",
"\n",
"@jax.jit\n",
"def predict(params, inputs):\n",
" return net.apply({\"params\": params}, inputs)\n",
"\n",
"\n",
"@jax.jit\n",
"def loss_accuracy(params, data):\n",
" \"\"\"Computes loss and accuracy over a mini-batch.\n",
"\n",
" Args:\n",
" params: parameters of the model.\n",
" bn_params: state of the model.\n",
" data: tuple of (inputs, labels).\n",
" is_training: if true, uses train mode, otherwise uses eval mode.\n",
"\n",
" Returns:\n",
" loss: float\n",
" \"\"\"\n",
" inputs, labels = data\n",
" logits = predict(params, inputs)\n",
" loss = optax.softmax_cross_entropy_with_integer_labels(\n",
" logits=logits, labels=labels\n",
" ).mean()\n",
" accuracy = jnp.mean(jnp.argmax(logits, axis=-1) == labels)\n",
" return loss, {\"accuracy\": accuracy}\n",
"\n",
"@jax.jit\n",
"def update_model(state, grads):\n",
" return state.apply_gradients(grads=grads)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0eB2dhIpjTIi"
},
"source": [
"Next we need to initialize network parameters and solver state. We also define a convenience function `dataset_stats` that we'll call once per epoch to collect the loss and accuracy of our solver over the test set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PBnbq7gui34L"
},
"outputs": [],
"source": [
"solver = optax.adam(LEARNING_RATE)\n",
"rng = jax.random.PRNGKey(0)\n",
"dummy_data = jnp.ones((1,) + IMG_SIZE, dtype=jnp.float32)\n",
"\n",
"params = net.init({\"params\": rng}, dummy_data)[\"params\"]\n",
"\n",
"solver_state = solver.init(params)\n",
"\n",
"def dataset_stats(params, data_loader):\n",
" \"\"\"Computes loss and accuracy over the dataset `data_loader`.\"\"\"\n",
" all_accuracy = []\n",
" all_loss = []\n",
" for batch in data_loader.as_numpy_iterator():\n",
" batch_loss, batch_aux = loss_accuracy(params, batch)\n",
" all_loss.append(batch_loss)\n",
" all_accuracy.append(batch_aux[\"accuracy\"])\n",
" return {\"loss\": np.mean(all_loss), \"accuracy\": np.mean(all_accuracy)}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4H6GWNJf0XTY"
},
"source": [
"Finally, we do the actual training. The next cell train the model for `N_EPOCHS`. Within each epoch we iterate over the batched loader `train_loader_batched`, and once per epoch we also compute the test set accuracy and loss."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DeQr0urBjoDj"
},
"outputs": [],
"source": [
"train_accuracy = []\n",
"train_losses = []\n",
"\n",
"# Computes test set accuracy at initialization.\n",
"test_stats = dataset_stats(params, test_loader_batched)\n",
"test_accuracy = [test_stats[\"accuracy\"]]\n",
"test_losses = [test_stats[\"loss\"]]\n",
"\n",
"\n",
"@jax.jit\n",
"def train_step(params, solver_state, batch):\n",
" # Performs a one step update.\n",
" (loss, aux), grad = jax.value_and_grad(loss_accuracy, has_aux=True)(\n",
" params, batch\n",
" )\n",
" updates, solver_state = solver.update(grad, solver_state, params)\n",
" params = optax.apply_updates(params, updates)\n",
" return params, solver_state, loss, aux\n",
"\n",
"\n",
"for epoch in range(N_EPOCHS):\n",
" train_accuracy_epoch = []\n",
" train_losses_epoch = []\n",
"\n",
" for step, train_batch in enumerate(train_loader_batched.as_numpy_iterator()):\n",
" params, solver_state, train_loss, train_aux = train_step(\n",
" params, solver_state, train_batch\n",
" )\n",
" train_accuracy_epoch.append(train_aux[\"accuracy\"])\n",
" train_losses_epoch.append(train_loss)\n",
" if step % 20 == 0:\n",
" print(\n",
" f\"step {step}, train loss: {train_loss:.2e}, train accuracy:\"\n",
" f\" {train_aux['accuracy']:.2f}\"\n",
" )\n",
"\n",
" test_stats = dataset_stats(params, test_loader_batched)\n",
" test_accuracy.append(test_stats[\"accuracy\"])\n",
" test_losses.append(test_stats[\"loss\"])\n",
" train_accuracy.append(np.mean(train_accuracy_epoch))\n",
" train_losses.append(np.mean(train_losses_epoch))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yyS1oRZBtytP"
},
"outputs": [],
"source": [
"f\"Improved accuracy on test DS from {test_accuracy[0]} to {test_accuracy[-1]}\""
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|