keypoint_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "RANDOM_SEED = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 各パス指定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = 'model/keypoint_classifier/keypoint.csv'\n",
    "model_save_path = 'model/keypoint_classifier/keypoint_classifier.hdf5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分類数設定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_CLASSES = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学習データ読み込み"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_dataset = np.loadtxt(dataset, delimiter=',', dtype='float32', usecols=list(range(1, (21 * 2) + 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_dataset = np.loadtxt(dataset, delimiter=',', dtype='int32', usecols=(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_dataset, y_dataset, train_size=0.75, random_state=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# モデル構築"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Input((21 * 2, )),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Dense(20, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.4),\n",
    "    tf.keras.layers.Dense(10, activation='relu'),\n",
    "    tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dropout (Dropout)           (None, 42)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 20)                860       \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 20)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                210       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 3)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1103 (4.31 KB)\n",
      "Trainable params: 1103 (4.31 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()  # tf.keras.utils.plot_model(model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# モデルチェックポイントのコールバック\n",
    "cp_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    model_save_path, verbose=1, save_weights_only=False)\n",
    "# 早期打ち切り用コールバック\n",
    "es_callback = tf.keras.callbacks.EarlyStopping(patience=20, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# モデルコンパイル\n",
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# モデル訓練"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-04 16:43:38.773041: W tensorflow/core/framework/op_kernel.cc:1828] OP_REQUIRES failed at sparse_xent_op.cc:103 : INVALID_ARGUMENT: Received a label value of 5 which is outside the valid range of [0, 3).  Label values: 5 5 0 0 2 2 0 2 1 4 3 1 0 2 1 1 2 1 3 4 5 1 1 1 0 1 2 2 0 1 0 1 0 4 1 1 0 1 1 2 0 1 0 0 0 0 1 1 2 0 0 0 1 1 3 0 2 2 1 0 3 2 0 2 0 5 2 5 3 2 2 3 1 0 1 2 1 1 0 2 0 2 1 3 2 0 2 0 0 4 0 2 1 0 1 2 2 2 0 3 0 1 2 5 0 0 1 1 2 0 2 0 3 4 0 0 0 2 1 1 4 3 1 0 4 2 3 2\n"
     ]
    },
    {
     "ename": "InvalidArgumentError",
     "evalue": "Graph execution error:\n\nDetected at node 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits' defined at (most recent call last):\n    File \"<frozen runpy>\", line 198, in _run_module_as_main\n    File \"<frozen runpy>\", line 88, in _run_code\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel_launcher.py\", line 17, in <module>\n      app.launch_new_instance()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/traitlets/config/application.py\", line 1043, in launch_instance\n      app.start()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/kernelapp.py\", line 728, in start\n      self.io_loop.start()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tornado/platform/asyncio.py\", line 195, in start\n      self.asyncio_loop.run_forever()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py\", line 607, in run_forever\n      self._run_once()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py\", line 1919, in _run_once\n      handle._run()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/events.py\", line 80, in _run\n      self._context.run(self._callback, *self._args)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 516, in dispatch_queue\n      await self.process_one()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 505, in process_one\n      await dispatch(*args)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 412, in dispatch_shell\n      await result\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 740, in execute_request\n      reply_content = await reply_content\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/ipkernel.py\", line 422, in do_execute\n      res = shell.run_cell(\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/zmqshell.py\", line 540, in run_cell\n      return super().run_cell(*args, **kwargs)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3009, in run_cell\n      result = self._run_cell(\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3064, in _run_cell\n      result = runner(coro)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n      coro.send(None)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3269, in run_cell_async\n      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3448, in run_ast_nodes\n      if await self.run_code(code, result, async_=asy):\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3508, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"/var/folders/xt/ncfs5rvj6cq_5l03rdjg1sk80000gn/T/ipykernel_7951/2889966517.py\", line 1, in <module>\n      model.fit(\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1742, in fit\n      tmp_logs = self.train_function(iterator)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1338, in train_function\n      return step_function(self, iterator)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1322, in step_function\n      outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1303, in run_step\n      outputs = model.train_step(data)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1081, in train_step\n      loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1139, in compute_loss\n      return self.compiled_loss(\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/compile_utils.py\", line 265, in __call__\n      loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/losses.py\", line 142, in __call__\n      losses = call_fn(y_true, y_pred)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/losses.py\", line 268, in call\n      return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/losses.py\", line 2354, in sparse_categorical_crossentropy\n      return backend.sparse_categorical_crossentropy(\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/backend.py\", line 5762, in sparse_categorical_crossentropy\n      res = tf.nn.sparse_softmax_cross_entropy_with_logits(\nNode: 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits'\nReceived a label value of 5 which is outside the valid range of [0, 3).  Label values: 5 5 0 0 2 2 0 2 1 4 3 1 0 2 1 1 2 1 3 4 5 1 1 1 0 1 2 2 0 1 0 1 0 4 1 1 0 1 1 2 0 1 0 0 0 0 1 1 2 0 0 0 1 1 3 0 2 2 1 0 3 2 0 2 0 5 2 5 3 2 2 3 1 0 1 2 1 1 0 2 0 2 1 3 2 0 2 0 0 4 0 2 1 0 1 2 2 2 0 3 0 1 2 5 0 0 1 1 2 0 2 0 3 4 0 0 0 2 1 1 4 3 1 0 4 2 3 2\n\t [[{{node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}}]] [Op:__inference_train_function_746]",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[11], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m128\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcp_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mes_callback\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/eager/execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[1;32m     54\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits' defined at (most recent call last):\n    File \"<frozen runpy>\", line 198, in _run_module_as_main\n    File \"<frozen runpy>\", line 88, in _run_code\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel_launcher.py\", line 17, in <module>\n      app.launch_new_instance()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/traitlets/config/application.py\", line 1043, in launch_instance\n      app.start()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/kernelapp.py\", line 728, in start\n      self.io_loop.start()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tornado/platform/asyncio.py\", line 195, in start\n      self.asyncio_loop.run_forever()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py\", line 607, in run_forever\n      self._run_once()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/base_events.py\", line 1919, in _run_once\n      handle._run()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/asyncio/events.py\", line 80, in _run\n      self._context.run(self._callback, *self._args)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 516, in dispatch_queue\n      await self.process_one()\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 505, in process_one\n      await dispatch(*args)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 412, in dispatch_shell\n      await result\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 740, in execute_request\n      reply_content = await reply_content\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/ipkernel.py\", line 422, in do_execute\n      res = shell.run_cell(\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/ipykernel/zmqshell.py\", line 540, in run_cell\n      return super().run_cell(*args, **kwargs)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3009, in run_cell\n      result = self._run_cell(\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3064, in _run_cell\n      result = runner(coro)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n      coro.send(None)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3269, in run_cell_async\n      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3448, in run_ast_nodes\n      if await self.run_code(code, result, async_=asy):\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3508, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"/var/folders/xt/ncfs5rvj6cq_5l03rdjg1sk80000gn/T/ipykernel_7951/2889966517.py\", line 1, in <module>\n      model.fit(\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1742, in fit\n      tmp_logs = self.train_function(iterator)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1338, in train_function\n      return step_function(self, iterator)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1322, in step_function\n      outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1303, in run_step\n      outputs = model.train_step(data)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1081, in train_step\n      loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py\", line 1139, in compute_loss\n      return self.compiled_loss(\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/compile_utils.py\", line 265, in __call__\n      loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/losses.py\", line 142, in __call__\n      losses = call_fn(y_true, y_pred)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/losses.py\", line 268, in call\n      return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/losses.py\", line 2354, in sparse_categorical_crossentropy\n      return backend.sparse_categorical_crossentropy(\n    File \"/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/backend.py\", line 5762, in sparse_categorical_crossentropy\n      res = tf.nn.sparse_softmax_cross_entropy_with_logits(\nNode: 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits'\nReceived a label value of 5 which is outside the valid range of [0, 3).  Label values: 5 5 0 0 2 2 0 2 1 4 3 1 0 2 1 1 2 1 3 4 5 1 1 1 0 1 2 2 0 1 0 1 0 4 1 1 0 1 1 2 0 1 0 0 0 0 1 1 2 0 0 0 1 1 3 0 2 2 1 0 3 2 0 2 0 5 2 5 3 2 2 3 1 0 1 2 1 1 0 2 0 2 1 3 2 0 2 0 0 4 0 2 1 0 1 2 2 2 0 3 0 1 2 5 0 0 1 1 2 0 2 0 3 4 0 0 0 2 1 1 4 3 1 0 4 2 3 2\n\t [[{{node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}}]] [Op:__inference_train_function_746]"
     ]
    }
   ],
   "source": [
    "model.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    epochs=1000,\n",
    "    batch_size=128,\n",
    "    validation_data=(X_test, y_test),\n",
    "    callbacks=[cp_callback, es_callback]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# モデル評価\n",
    "val_loss, val_acc = model.evaluate(X_test, y_test, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存したモデルのロード\n",
    "model = tf.keras.models.load_model(model_save_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 推論テスト\n",
    "predict_result = model.predict(np.array([X_test[0]]))\n",
    "print(np.squeeze(predict_result))\n",
    "print(np.argmax(np.squeeze(predict_result)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 混同行列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "def print_confusion_matrix(y_true, y_pred, report=True):\n",
    "    labels = sorted(list(set(y_true)))\n",
    "    cmx_data = confusion_matrix(y_true, y_pred, labels=labels)\n",
    "    \n",
    "    df_cmx = pd.DataFrame(cmx_data, index=labels, columns=labels)\n",
    " \n",
    "    fig, ax = plt.subplots(figsize=(7, 6))\n",
    "    sns.heatmap(df_cmx, annot=True, fmt='g' ,square=False)\n",
    "    ax.set_ylim(len(set(y_true)), 0)\n",
    "    plt.show()\n",
    "    \n",
    "    if report:\n",
    "        print('Classification Report')\n",
    "        print(classification_report(y_test, y_pred))\n",
    "\n",
    "Y_pred = model.predict(X_test)\n",
    "y_pred = np.argmax(Y_pred, axis=1)\n",
    "\n",
    "print_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow-Lite用のモデルへ変換"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 推論専用のモデルとして保存\n",
    "model.save(model_save_path, include_optimizer=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# モデルを変換(量子化)\n",
    "tflite_save_path = 'model/keypoint_classifier/keypoint_classifier.tflite'\n",
    "\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
    "tflite_quantized_model = converter.convert()\n",
    "\n",
    "open(tflite_save_path, 'wb').write(tflite_quantized_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 推論テスト"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpreter = tf.lite.Interpreter(model_path=tflite_save_path)\n",
    "interpreter.allocate_tensors()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 入出力テンソルを取得\n",
    "input_details = interpreter.get_input_details()\n",
    "output_details = interpreter.get_output_details()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpreter.set_tensor(input_details[0]['index'], np.array([X_test[0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# 推論実施\n",
    "interpreter.invoke()\n",
    "tflite_results = interpreter.get_tensor(output_details[0]['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.squeeze(tflite_results))\n",
    "print(np.argmax(np.squeeze(tflite_results)))"
   ]
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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