4.4.1-IO-API-Image-Classification.ipynb

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   "source": [
    "!pip install -r https://raw.githubusercontent.com/datamllab/automl-in-action-notebooks/master/requirements.txt\n"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "### Load MNIST dataset\n"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "print(\"Training image shape:\", x_train.shape)  # (60000, 28, 28)\n",
    "print(\"Training label shape:\", y_train.shape)  # (60000,)\n",
    "print(\"First five training labels:\", y_train[:5])  # array([5 0 4 1 9], dtype=uint8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### IO API for image classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "import autokeras as ak\n",
    "\n",
    "# Initialize the IO API.\n",
    "io_model = ak.AutoModel(\n",
    "    inputs=ak.ImageInput(),\n",
    "    outputs=ak.ClassificationHead(\n",
    "        loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]\n",
    "    ),\n",
    "    objective=\"val_loss\",\n",
    "    tuner=\"random\",\n",
    "    max_trials=3,\n",
    "    overwrite=True,\n",
    ")\n",
    "\n",
    "# Fit the model with prepared data.\n",
    "# Use the first 100 training samples for 1 epoch as a quick demo.\n",
    "# You may run with the full dataset with 10 epochs, but expect a longer training time.\n",
    "io_model.fit(x_train[:100], y_train[:100], epochs=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Get the summarized results during the tuning process (return the best 10 models if\n",
    "existed)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "io_model.tuner.results_summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Retrieve best model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "best_model = io_model.export_model()\n",
    "best_model.summary()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Predict with the best model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "predicted_y = io_model.predict(x_test[:100])\n",
    "print(predicted_y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Evaluate the best model on the test data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "test_loss, test_acc = io_model.evaluate(x_test[:100], y_test[:100])\n",
    "print(\"Test accuracy: \", test_acc)\n"
   ]
  }
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