{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "049a3037-fef0-4db3-8bd2-56b4be59facc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers, models\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf605e76-2d15-43e8-b287-5c7bc12c7a84",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc616b30-e38e-4739-813a-543b126fe919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7a5885dd-8197-4e5d-92f0-7fa7e487f708",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(-1, 32, 32, 3).astype('float32') / 255.0\n",
    "X_test = X_test.reshape(-1, 32, 32, 3).astype('float32') / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "836fe315-c940-480b-bb70-666a414e9683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e1aa4ef-f5ca-4b7b-b18f-3e03ac42218b",
   "metadata": {},
   "source": [
    "# CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d2c41fb8-3be3-4d5f-9683-193cf0dd60ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = models.Sequential([\n",
    "    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "\n",
    "    layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "\n",
    "    layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(128, activation='relu'),\n",
    "    layers.Dropout(0.3),\n",
    "    layers.Dense(10, activation='softmax')  # 10 类分类\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ec488217-1aee-4ec9-8242-e4e35791aabe",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a58f6a37-0ea2-4741-96ea-0304fdc418a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 39ms/step - accuracy: 0.2861 - loss: 1.9071 - val_accuracy: 0.4836 - val_loss: 1.4225\n",
      "Epoch 2/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 40ms/step - accuracy: 0.5055 - loss: 1.3577 - val_accuracy: 0.5840 - val_loss: 1.1788\n",
      "Epoch 3/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 40ms/step - accuracy: 0.5765 - loss: 1.1928 - val_accuracy: 0.6082 - val_loss: 1.1211\n",
      "Epoch 4/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 40ms/step - accuracy: 0.6099 - loss: 1.1059 - val_accuracy: 0.6486 - val_loss: 1.0074\n",
      "Epoch 5/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 40ms/step - accuracy: 0.6395 - loss: 1.0266 - val_accuracy: 0.6382 - val_loss: 1.0576\n",
      "Epoch 6/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 40ms/step - accuracy: 0.6620 - loss: 0.9606 - val_accuracy: 0.6582 - val_loss: 0.9763\n",
      "Epoch 7/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 40ms/step - accuracy: 0.6778 - loss: 0.9101 - val_accuracy: 0.6946 - val_loss: 0.8998\n",
      "Epoch 8/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 40ms/step - accuracy: 0.7006 - loss: 0.8531 - val_accuracy: 0.6958 - val_loss: 0.8916\n",
      "Epoch 9/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 38ms/step - accuracy: 0.7155 - loss: 0.8105 - val_accuracy: 0.7076 - val_loss: 0.8398\n",
      "Epoch 10/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 38ms/step - accuracy: 0.7257 - loss: 0.7800 - val_accuracy: 0.6912 - val_loss: 0.9219\n",
      "Epoch 11/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 40ms/step - accuracy: 0.7366 - loss: 0.7499 - val_accuracy: 0.7210 - val_loss: 0.8266\n",
      "Epoch 12/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 40ms/step - accuracy: 0.7481 - loss: 0.7120 - val_accuracy: 0.7236 - val_loss: 0.8117\n",
      "Epoch 13/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 40ms/step - accuracy: 0.7593 - loss: 0.6885 - val_accuracy: 0.7232 - val_loss: 0.8189\n",
      "Epoch 14/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 39ms/step - accuracy: 0.7723 - loss: 0.6490 - val_accuracy: 0.7256 - val_loss: 0.8220\n",
      "Epoch 15/15\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 40ms/step - accuracy: 0.7750 - loss: 0.6312 - val_accuracy: 0.7392 - val_loss: 0.7815\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x214a2668350>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=15, batch_size=64, validation_split=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2a1aef13-e0a0-4e89-9da2-ed89f1cdc806",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - accuracy: 0.7292 - loss: 0.8151\n",
      "测试集准确率： 0.7236999869346619\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
    "print(\"测试集准确率：\", test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "677dc9bd-3c46-4fd0-8143-b0f77878a819",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred_probs = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e6e68be7-ced5-4b8e-a49a-6707cb8d047f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b51bb2a8-0435-4f6d-b547-2a48a668be73",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5035b004-b271-4a75-8e0f-a078f2ab7f74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert probability predictions to class predictions first\n",
    "y_pred = np.argmax(y_pred_probs, axis=1)  # This converts probabilities to class indices\n",
    "\n",
    "# Now create the confusion matrix with discrete class predictions\n",
    "knn_cm = metrics.confusion_matrix(y_test, y_pred)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(knn_cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Cifar10 image Confusion Matrix for CNN')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "003f9c83-e26f-4da5-9a7c-c285595ffa04",
   "metadata": {},
   "source": [
    "# Multi-Layer-Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "63fffc21-f1c1-40f5-ba6e-600b5ff42cd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier, MLPRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ff0cb1ca-719b-45a1-9ac4-17307be75696",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.flatten()\n",
    "y_test = y_test.flatten()\n",
    "\n",
    "X_train = X_train.reshape((X_train.shape[0], -1)) \n",
    "X_test = X_test.reshape((X_test.shape[0], -1))     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e587f03e-6e4c-4e6b-b161-3e9bce04aab8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:697: UserWarning: Training interrupted by user.\n",
      "  warnings.warn(\"Training interrupted by user.\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(alpha=0.01, hidden_layer_sizes=(32, 64, 128),\n",
       "              learning_rate_init=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MLPClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neural_network.MLPClassifier.html\">?<span>Documentation for MLPClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MLPClassifier(alpha=0.01, hidden_layer_sizes=(32, 64, 128),\n",
       "              learning_rate_init=0.01)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MLPClassifier(alpha=0.01, hidden_layer_sizes=(32, 64, 128),\n",
       "              learning_rate_init=0.01)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a model for Support Vector (Machine) Classifier using default parameters\n",
    "mlp_clf_opt = MLPClassifier(alpha=0.01, hidden_layer_sizes=(32, 64, 128), learning_rate_init=0.01)\n",
    "# fit the data to the model, both features and labels to train it\n",
    "mlp_clf_opt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bda06269-339d-4ed5-a7da-bd3ef3950e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = mlp_clf_opt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2692f77e-1267-4679-8e6f-89e1cddb1602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 8, 8, ..., 5, 6, 7], dtype=uint8)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7e615c8f-c755-413d-95cb-e8151ab87011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the shape of y_pred first\n",
    "if len(y_pred.shape) > 1 and y_pred.shape[1] > 1:\n",
    "    # If y_pred contains probabilities (2D array), convert to class predictions\n",
    "    y_pred = np.argmax(y_pred, axis=1)\n",
    "# else: y_pred is already class predictions, no need to convert\n",
    "\n",
    "# Now create the confusion matrix with discrete class predictions\n",
    "knn_cm = metrics.confusion_matrix(y_test, y_pred)\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.heatmap(knn_cm, annot=True, fmt='d', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.title('Cifar10 image Confusion Matrix for ANN')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "611167d4-ce00-4830-9d25-b78a6a7a65de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}