ch04_1_순수_JAX로_구현하는_CNN.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yblee110/jax-flax-book/blob/main/ch04_1_%EC%88%9C%EC%88%98_JAX%EB%A1%9C_%EA%B5%AC%ED%98%84%ED%95%98%EB%8A%94_CNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dmdjluna7uld",
        "outputId": "a727baf5-6380-4712-dc60-32a72a7bda87"
      },
      "id": "Dmdjluna7uld",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 multiprocess-0.70.16 pyarrow-17.0.0 requests-2.32.3 xxhash-3.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import jax.numpy as jnp\n",
        "from datasets import load_dataset # 통일성 위해서 3.1.2.데이터 로드 가져옴\n",
        "\n",
        "\n",
        "def get_datasets():\n",
        "  datasets = load_dataset(\"mnist\")\n",
        "  datasets = datasets.with_format(\"jax\")\n",
        "  datasets = {\n",
        "    \"train\": {\n",
        "      \"image\": datasets[\"train\"][\"image\"][...,None].astype(jnp.float32)/255,\n",
        "      \"label\": datasets[\"train\"][\"label\"],\n",
        "    },\n",
        "    \"test\": {\n",
        "      \"image\": datasets[\"test\"][\"image\"][...,None].astype(jnp.float32)/255,\n",
        "      \"label\": datasets[\"test\"][\"label\"],\n",
        "    },\n",
        "  }\n",
        "  return datasets['train'], datasets['test']\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "vRYW1gra7hCR"
      },
      "id": "vRYW1gra7hCR",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds, test_ds = get_datasets()"
      ],
      "metadata": {
        "colab": {
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        "id": "oWDKGmuC79uA",
        "outputId": "c88c4e12-9016-431b-8131-fd2c2325f0cf"
      },
      "id": "oWDKGmuC79uA",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for mnist contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/mnist.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n"
          ]
        },
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8711244952714b1a99e6dd0d75783e1e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15259efa62dd47a2a646b748752a5df0"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import functools\n",
        "import operator as op\n",
        "import jax\n",
        "from jax import lax\n",
        "from jax import random\n",
        "\n",
        "from jax.nn import relu, log_softmax\n",
        "from jax.nn.initializers import glorot_normal, normal\n",
        "\n",
        "\n",
        "# 각 레이어 생성자 함수는 (init_fun, apply_fun) 쌍을 반환하며, 여기서\n",
        "# init_fun: rng 키와 입력 모양을 가져와 (output_shape, params) 쌍을 반환하고,\n",
        "# apply_fun: params, 입력, rng 키를 가져와 레이어를 적용합니다."
      ],
      "metadata": {
        "id": "Bk8AT79SUnQM"
      },
      "id": "Bk8AT79SUnQM",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Dense(out_dim, W_init=glorot_normal(), b_init=normal()):\n",
        "  \"\"\"밀도가 높은(완전히 연결된) 레이어를 위한 레이어 생성자 함수\"\"\"\n",
        "  def init_fun(rng, input_shape):\n",
        "    output_shape = input_shape[:-1] + (out_dim,)\n",
        "    k1, k2 = random.split(rng)\n",
        "    W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))\n",
        "    return output_shape, (W, b)\n",
        "  def apply_fun(params, inputs, **kwargs):\n",
        "    W, b = params\n",
        "    return jnp.dot(inputs, W) + b\n",
        "  return init_fun, apply_fun"
      ],
      "metadata": {
        "id": "TUrEtWatVJXF"
      },
      "id": "TUrEtWatVJXF",
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def GeneralConv(dimension_numbers, out_chan, filter_shape,\n",
        "                strides=None, padding='VALID', W_init=None,\n",
        "                b_init=normal(1e-6)):\n",
        "  \"\"\"일반 컨볼루션 레이어를 위한 레이어 구성 함수\"\"\"\n",
        "  lhs_spec, rhs_spec, out_spec = dimension_numbers\n",
        "  one = (1,) * len(filter_shape)\n",
        "  strides = strides or one\n",
        "  W_init = W_init or glorot_normal(rhs_spec.index('I'), rhs_spec.index('O'))\n",
        "  def init_fun(rng, input_shape):\n",
        "    filter_shape_iter = iter(filter_shape)\n",
        "    kernel_shape = [out_chan if c == 'O' else\n",
        "                    input_shape[lhs_spec.index('C')] if c == 'I' else\n",
        "                    next(filter_shape_iter) for c in rhs_spec]\n",
        "    output_shape = lax.conv_general_shape_tuple(\n",
        "        input_shape, kernel_shape, strides, padding, dimension_numbers)\n",
        "    bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n",
        "    k1, k2 = random.split(rng)\n",
        "    W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\n",
        "    return output_shape, (W, b)\n",
        "  def apply_fun(params, inputs, **kwargs):\n",
        "    W, b = params\n",
        "    return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\n",
        "                                    dimension_numbers=dimension_numbers) + b\n",
        "  return init_fun, apply_fun\n",
        "Conv = functools.partial(GeneralConv, ('NHWC', 'HWIO', 'NHWC'))"
      ],
      "metadata": {
        "id": "TUuQtgBjVE7c"
      },
      "id": "TUuQtgBjVE7c",
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def elementwise(fun, **fun_kwargs):\n",
        "  \"\"\"입력에 요소별로 스칼라 함수를 적용하는 레이어\"\"\"\n",
        "  init_fun = lambda rng, input_shape: (input_shape, ())\n",
        "  apply_fun = lambda params, inputs, **kwargs: fun(inputs, **fun_kwargs)\n",
        "  return init_fun, apply_fun\n",
        "Relu = elementwise(relu)\n",
        "LogSoftmax = elementwise(log_softmax, axis=-1)"
      ],
      "metadata": {
        "id": "Dhw4p48jVCqK"
      },
      "id": "Dhw4p48jVCqK",
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def _pooling_layer(reducer, init_val, rescaler=None):\n",
        "  def PoolingLayer(window_shape, strides=None, padding='VALID', spec=None):\n",
        "    \"\"\"풀링 레이어를 위한 레이어 구성 기능\"\"\"\n",
        "    strides = strides or (1,) * len(window_shape)\n",
        "    rescale = rescaler(window_shape, strides, padding) if rescaler else None\n",
        "\n",
        "    if spec is None:\n",
        "      non_spatial_axes = 0, len(window_shape) + 1\n",
        "    else:\n",
        "      non_spatial_axes = spec.index('N'), spec.index('C')\n",
        "\n",
        "    for i in sorted(non_spatial_axes):\n",
        "      window_shape = window_shape[:i] + (1,) + window_shape[i:]\n",
        "      strides = strides[:i] + (1,) + strides[i:]\n",
        "\n",
        "    def init_fun(rng, input_shape):\n",
        "      padding_vals = lax.padtype_to_pads(input_shape, window_shape,\n",
        "                                         strides, padding)\n",
        "      ones = (1,) * len(window_shape)\n",
        "      out_shape = lax.reduce_window_shape_tuple(input_shape, window_shape, strides, padding_vals, ones, ones)\n",
        "      return out_shape, ()\n",
        "    def apply_fun(params, inputs, **kwargs):\n",
        "      out = lax.reduce_window(inputs, -jnp.inf, lax.max, window_shape,\n",
        "                              strides, padding)\n",
        "      return out\n",
        "    return init_fun, apply_fun\n",
        "  return PoolingLayer\n",
        "MaxPool = _pooling_layer(lax.max, -jnp.inf)"
      ],
      "metadata": {
        "id": "TflEsBuyU7by"
      },
      "id": "TflEsBuyU7by",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Flatten():\n",
        "  \"\"\"주요 조명을 제외한 모든 조명을 평평하게 만드는 레이어 구성 기능\"\"\"\n",
        "  def init_fun(rng, input_shape):\n",
        "    output_shape = input_shape[0], functools.reduce(op.mul, input_shape[1:], 1)\n",
        "    return output_shape, ()\n",
        "  def apply_fun(params, inputs, **kwargs):\n",
        "    return jnp.reshape(inputs, (inputs.shape[0], -1))\n",
        "  return init_fun, apply_fun\n",
        "Flatten = Flatten()"
      ],
      "metadata": {
        "id": "4kQ_Geu-UyZ0"
      },
      "id": "4kQ_Geu-UyZ0",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def serial(*layers):\n",
        "  \"\"\"직렬로 레이어를 구성하기 위한 결합기입니다.\n",
        "\n",
        "  Args:\n",
        "    *레이어: 레이어 시퀀스, 각각 (init_fun, apply_fun) 쌍입니다.\n",
        "\n",
        "  반환값:\n",
        "    주어진 레이어 시퀀스의 직렬 구성을 나타내는 새 레이어, 즉 (init_fun, apply_fun) 쌍입니다.\n",
        "    구성을 나타내는 새 레이어입니다.\n",
        "  \"\"\"\n",
        "  nlayers = len(layers)\n",
        "  init_funs, apply_funs = zip(*layers)\n",
        "  def init_fun(rng, input_shape):\n",
        "    params = []\n",
        "    for init_fun in init_funs:\n",
        "      rng, layer_rng = random.split(rng)\n",
        "      input_shape, param = init_fun(layer_rng, input_shape)\n",
        "      params.append(param)\n",
        "    return input_shape, params\n",
        "  def apply_fun(params, inputs, **kwargs):\n",
        "    rng = kwargs.pop('rng', None)\n",
        "    rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n",
        "    for fun, param, rng in zip(apply_funs, params, rngs):\n",
        "      inputs = fun(param, inputs, rng=rng, **kwargs)\n",
        "    return inputs\n",
        "  return init_fun, apply_fun"
      ],
      "metadata": {
        "id": "CTsiLb7GUr9d"
      },
      "id": "CTsiLb7GUr9d",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import jax.numpy as jnp\n",
        "from jax import random\n",
        "\n",
        "# 네트워크 초기화 및 평가 기능 설정\n",
        "net_init, net_apply = serial(\n",
        "    Conv(32, (3, 3), padding='SAME'), Relu,\n",
        "    Conv(64, (3, 3), padding='SAME'), Relu,\n",
        "    MaxPool((2, 2)), Flatten,\n",
        "    Dense(128), Relu,\n",
        "    Dense(10), LogSoftmax,\n",
        ")\n",
        "\n",
        "# 배치 모양에 커밋하지 않고 매개변수 초기화하기\n",
        "rng = random.key(0)\n",
        "in_shape = (-1, 28, 28, 1)\n",
        "out_shape, net_params = net_init(rng, in_shape)"
      ],
      "metadata": {
        "id": "GJ1eKiJ9UeVi"
      },
      "id": "GJ1eKiJ9UeVi",
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from jax.example_libraries import optimizers\n",
        "from jax import grad\n",
        "\n",
        "# 옵티마이저 초기화 및 업데이트 기능 설정\n",
        "opt_init, opt_update, get_params = optimizers.momentum(step_size=1e-3, mass=0.9)"
      ],
      "metadata": {
        "id": "cPvgNBkAVs-q"
      },
      "id": "cPvgNBkAVs-q",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def softmax_cross_entropy(preds, labels):\n",
        "    x = labels * preds\n",
        "    return -jnp.where(labels == 0, 0, x).sum(-1)"
      ],
      "metadata": {
        "id": "zEDbIW9dvEyM"
      },
      "id": "zEDbIW9dvEyM",
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(preds, labels):\n",
        "  loss = jnp.mean(softmax_cross_entropy(preds, jax.nn.one_hot(labels, num_classes=10)))\n",
        "  accuracy = jnp.mean(jnp.argmax(preds, -1) == labels)\n",
        "  metrics = {\n",
        "    'loss': loss,\n",
        "    'accuracy': accuracy\n",
        "  }\n",
        "  return metrics"
      ],
      "metadata": {
        "id": "WVb_lgVgtpI6"
      },
      "id": "WVb_lgVgtpI6",
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@jax.jit\n",
        "def train_step(i, state, batch):\n",
        "  def loss_fn(params):\n",
        "    logits = net_apply(params, batch['image'])\n",
        "    loss = jnp.mean(softmax_cross_entropy(logits,\n",
        "                                jax.nn.one_hot(batch['label'], num_classes=10))\n",
        "    )\n",
        "    return loss, logits\n",
        "  params = get_params(state)\n",
        "  grad_fn = jax.value_and_grad(loss_fn, has_aux=True)\n",
        "  (_, logits), grads = grad_fn(params)\n",
        "  state = opt_update(i, grads, state)\n",
        "  metrics = compute_metrics(logits, batch['label'])\n",
        "  return state, metrics"
      ],
      "metadata": {
        "id": "vg4aF9VmvOWC"
      },
      "id": "vg4aF9VmvOWC",
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@jax.jit\n",
        "def eval_step(state, batch):\n",
        "  params = get_params(state)\n",
        "  logits = net_apply(params, batch['image'])\n",
        "  return compute_metrics(logits, batch['label'])"
      ],
      "metadata": {
        "id": "EzgtipmPfZb6"
      },
      "id": "EzgtipmPfZb6",
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Optimize parameters in a loop\n",
        "state = opt_init(net_params)\n",
        "\n",
        "def train_loop(state, train_ds, batch_size, epoch, rng):\n",
        "  train_ds_size = len(train_ds['image'])\n",
        "  steps_per_epoch = train_ds_size // batch_size\n",
        "\n",
        "  perms = jax.random.permutation(rng, train_ds_size)\n",
        "  perms = perms[:steps_per_epoch * batch_size]  # 완성되지 않은 배치는 생략합니다.\n",
        "  perms = perms.reshape((steps_per_epoch, batch_size))\n",
        "\n",
        "  batch_metrics = []\n",
        "  for i, perm in enumerate(perms):\n",
        "    batch = {k: v[perm, ...] for k, v in train_ds.items()}\n",
        "    state, metrics = train_step(i, state, batch)\n",
        "    batch_metrics.append(metrics)\n",
        "  training_batch_metrics = jax.device_get(batch_metrics)\n",
        "  training_epoch_metrics = {\n",
        "    k: sum([metrics[k] for metrics in training_batch_metrics])/steps_per_epoch\n",
        "    for k in training_batch_metrics[0]}\n",
        "\n",
        "  print('EPOCH: %d\\nTraining loss: %.4f, accuracy: %.2f' % (epoch, training_epoch_metrics['loss'], training_epoch_metrics['accuracy'] * 100))\n",
        "  return state"
      ],
      "metadata": {
        "id": "tMIX20W_YfsH"
      },
      "id": "tMIX20W_YfsH",
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_loop(state, test_ds, batch_size):\n",
        "  eval_ds_size = test_ds['image'].shape[0]\n",
        "  steps_per_epoch = eval_ds_size // batch_size\n",
        "  batch_metrics = []\n",
        "  for i in range(steps_per_epoch):\n",
        "    batch = {k: v[i*batch_size:(i+1)*batch_size, ...] for k, v in test_ds.items()}\n",
        "    metrics = eval_step(state, batch)\n",
        "    batch_metrics.append(metrics)\n",
        "  eval_batch_metrics = jax.device_get(batch_metrics)\n",
        "  eval_batch_metrics = {\n",
        "    k: sum([metrics[k] for metrics in eval_batch_metrics])/steps_per_epoch\n",
        "    for k in eval_batch_metrics[0]}\n",
        "\n",
        "  print('    Eval loss: %.4f, accuracy: %.2f' % (eval_batch_metrics['loss'], eval_batch_metrics['accuracy'] * 100))"
      ],
      "metadata": {
        "id": "kZHg0mPpw4v2"
      },
      "id": "kZHg0mPpw4v2",
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_epoch = 10\n",
        "batch_size = 64\n",
        "eval_batch_size = 100\n",
        "for epoch in range(train_epoch):\n",
        "  rng, key = jax.random.split(rng)\n",
        "  state = train_loop(state, train_ds, batch_size, epoch, rng)\n",
        "  eval_loop(state, test_ds, eval_batch_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-NlRqXMw9dY",
        "outputId": "38d0c35e-ef7d-4b44-a7bb-9fa29b0a5995"
      },
      "id": "w-NlRqXMw9dY",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EPOCH: 0\n",
            "Training loss: 0.4391, accuracy: 87.65\n",
            "    Eval loss: 0.1971, accuracy: 94.04\n",
            "EPOCH: 1\n",
            "Training loss: 0.1746, accuracy: 94.79\n",
            "    Eval loss: 0.1299, accuracy: 96.21\n",
            "EPOCH: 2\n",
            "Training loss: 0.1219, accuracy: 96.31\n",
            "    Eval loss: 0.0933, accuracy: 97.12\n",
            "EPOCH: 3\n",
            "Training loss: 0.0911, accuracy: 97.26\n",
            "    Eval loss: 0.0832, accuracy: 97.37\n",
            "EPOCH: 4\n",
            "Training loss: 0.0738, accuracy: 97.73\n",
            "    Eval loss: 0.0618, accuracy: 98.00\n",
            "EPOCH: 5\n",
            "Training loss: 0.0606, accuracy: 98.20\n",
            "    Eval loss: 0.0539, accuracy: 98.20\n",
            "EPOCH: 6\n",
            "Training loss: 0.0516, accuracy: 98.44\n",
            "    Eval loss: 0.0469, accuracy: 98.35\n",
            "EPOCH: 7\n",
            "Training loss: 0.0443, accuracy: 98.67\n",
            "    Eval loss: 0.0480, accuracy: 98.41\n",
            "EPOCH: 8\n",
            "Training loss: 0.0390, accuracy: 98.85\n",
            "    Eval loss: 0.0503, accuracy: 98.25\n",
            "EPOCH: 9\n",
            "Training loss: 0.0352, accuracy: 98.92\n",
            "    Eval loss: 0.0460, accuracy: 98.37\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt # 3.1.8.와 유사하게 맞춤\n",
        "\n",
        "@jax.jit\n",
        "def pred_step(state, batch):\n",
        "  params = get_params(state)\n",
        "  logits = net_apply(params, batch)\n",
        "  return logits.argmax(axis=1)\n",
        "pred = pred_step(state, test_ds['image'][:25])\n",
        "\n",
        "fig, axs = plt.subplots(5, 5, figsize=(12, 12))\n",
        "for i, ax in enumerate(axs.flatten()):\n",
        "  ax.imshow(test_ds['image'][i], cmap='gray')\n",
        "  ax.set_title(f\"label={pred[i]}\")\n",
        "  ax.axis('off')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 983
        },
        "id": "4QNjI_7C9g4M",
        "outputId": "33d54fb1-57e2-408d-eec6-a4306a3f12c4"
      },
      "id": "4QNjI_7C9g4M",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 25 Axes>"
            ],
            "image/png": 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