Day67-Keras_Dataset_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作業目標:\n",
    "\n",
    "    使用CIFAR100, 數據集變大的影響\n",
    "    \n",
    "    \n",
    "# 作業重點:¶\n",
    "\n",
    "   了解 CIFAR100 跟 CIFAR10 數據及差異\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "from keras.datasets import cifar100\n",
    "import keras\n",
    "import numpy as np\n",
    "np.random.seed(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 資料準備"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image normalize "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 轉換label 為OneHot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [1. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "(x_img_train,y_label_train), (x_img_test, y_label_test)=cifar100.load_data()\n",
    "\n",
    "y_label_train = keras.utils.to_categorical(y_label_train, num_classes=None, dtype='float32')\n",
    "y_label_test = keras.utils.to_categorical(y_label_test, num_classes=None, dtype='float32')\n",
    "print(y_label_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_img_train_normalize = x_img_train.astype('float32') / 255.0\n",
    "x_img_test_normalize = x_img_test.astype('float32') / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "   ...\n",
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      "\n",
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      "   [0.99607843 0.99607843 0.99607843]\n",
      "   ...\n",
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      "   ...\n",
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      "\n",
      "  ...\n",
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      "   ...\n",
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      "   ...\n",
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      "   ...\n",
      "   [0.98039216 0.98039216 0.9647059 ]\n",
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      "\n",
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      "   [0.96862745 0.96862745 0.91764706]\n",
      "   ...\n",
      "   [0.9843137  0.9843137  0.9490196 ]\n",
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      "\n",
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      "   [0.98039216 0.972549   0.92941177]\n",
      "   [0.98039216 0.9607843  0.9137255 ]\n",
      "   ...\n",
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      "\n",
      "  ...\n",
      "\n",
      "  [[0.8666667  0.8352941  0.7490196 ]\n",
      "   [0.8666667  0.80784315 0.6901961 ]\n",
      "   [0.88235295 0.8117647  0.70980394]\n",
      "   ...\n",
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      "   ...\n",
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      "   ...\n",
      "   [0.85882354 0.81960785 0.7607843 ]\n",
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      "   [0.9098039  0.9019608  0.89411765]]]\n",
      "\n",
      "\n",
      " ...\n",
      "\n",
      "\n",
      " [[[0.972549   0.95686275 0.9490196 ]\n",
      "   [0.9411765  0.9098039  0.8745098 ]\n",
      "   [0.9254902  0.9098039  0.8745098 ]\n",
      "   ...\n",
      "   [0.9137255  0.8980392  0.87058824]\n",
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      "\n",
      "  [[0.88235295 0.8352941  0.8       ]\n",
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      "   ...\n",
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      "   ...\n",
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      "\n",
      "  ...\n",
      "\n",
      "  [[0.6313726  0.62352943 0.6       ]\n",
      "   [0.15294118 0.13333334 0.10980392]\n",
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      "   ...\n",
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      "\n",
      "  [[0.70980394 0.7019608  0.6745098 ]\n",
      "   [0.3372549  0.3254902  0.3019608 ]\n",
      "   [0.2784314  0.26666668 0.24313726]\n",
      "   ...\n",
      "   [0.47843137 0.40392157 0.34901962]\n",
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      "\n",
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      "   [0.7058824  0.7058824  0.6862745 ]\n",
      "   [0.6784314  0.6745098  0.654902  ]\n",
      "   ...\n",
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      "   [0.8        0.8039216  0.8039216 ]]]\n",
      "\n",
      "\n",
      " [[[0.6117647  0.6039216  0.5372549 ]\n",
      "   [0.5921569  0.57254905 0.48235294]\n",
      "   [0.5921569  0.5647059  0.49019608]\n",
      "   ...\n",
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      "\n",
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      "   ...\n",
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      "   [0.5254902  0.5058824  0.36078432]]\n",
      "\n",
      "  [[0.45490196 0.4392157  0.32156864]\n",
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      "   ...\n",
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      "\n",
      "  ...\n",
      "\n",
      "  [[0.42745098 0.39607844 0.2901961 ]\n",
      "   [0.4392157  0.39215687 0.21176471]\n",
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      "   ...\n",
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      "   [0.5411765  0.4862745  0.34901962]]\n",
      "\n",
      "  [[0.38431373 0.36078432 0.24705882]\n",
      "   [0.3647059  0.32156864 0.13725491]\n",
      "   [0.3764706  0.3254902  0.14901961]\n",
      "   ...\n",
      "   [0.4392157  0.3764706  0.18431373]\n",
      "   [0.42745098 0.36078432 0.1764706 ]\n",
      "   [0.49803922 0.44313726 0.3137255 ]]\n",
      "\n",
      "  [[0.6666667  0.654902   0.5686275 ]\n",
      "   [0.627451   0.6        0.4627451 ]\n",
      "   [0.6392157  0.59607846 0.46666667]\n",
      "   ...\n",
      "   [0.6313726  0.5921569  0.44705883]\n",
      "   [0.6117647  0.5647059  0.41960785]\n",
      "   [0.6392157  0.6039216  0.49411765]]]\n",
      "\n",
      "\n",
      " [[[0.12156863 0.2627451  0.47843137]\n",
      "   [0.11764706 0.26666668 0.4862745 ]\n",
      "   [0.12156863 0.27058825 0.49411765]\n",
      "   ...\n",
      "   [0.1254902  0.27450982 0.5058824 ]\n",
      "   [0.1254902  0.27450982 0.49019608]\n",
      "   [0.1254902  0.27058825 0.47843137]]\n",
      "\n",
      "  [[0.11372549 0.26666668 0.49411765]\n",
      "   [0.10980392 0.27058825 0.5019608 ]\n",
      "   [0.11764706 0.27058825 0.50980395]\n",
      "   ...\n",
      "   [0.1254902  0.27450982 0.5137255 ]\n",
      "   [0.1254902  0.27058825 0.49803922]\n",
      "   [0.12156863 0.27058825 0.4862745 ]]\n",
      "\n",
      "  [[0.11764706 0.2627451  0.49411765]\n",
      "   [0.11372549 0.26666668 0.5019608 ]\n",
      "   [0.11764706 0.27058825 0.50980395]\n",
      "   ...\n",
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      "   [0.12156863 0.27450982 0.50980395]\n",
      "   [0.11764706 0.27058825 0.49803922]]\n",
      "\n",
      "  ...\n",
      "\n",
      "  [[0.15294118 0.16078432 0.29803923]\n",
      "   [0.14901961 0.16470589 0.29803923]\n",
      "   [0.14901961 0.17254902 0.30588236]\n",
      "   ...\n",
      "   [0.15294118 0.17254902 0.30980393]\n",
      "   [0.14901961 0.16470589 0.3019608 ]\n",
      "   [0.15294118 0.16078432 0.29803923]]\n",
      "\n",
      "  [[0.15686275 0.15294118 0.28627452]\n",
      "   [0.15294118 0.15686275 0.2901961 ]\n",
      "   [0.15294118 0.16078432 0.29803923]\n",
      "   ...\n",
      "   [0.15294118 0.16078432 0.29803923]\n",
      "   [0.15686275 0.16078432 0.2901961 ]\n",
      "   [0.15686275 0.15294118 0.28627452]]\n",
      "\n",
      "  [[0.15686275 0.15294118 0.27450982]\n",
      "   [0.15686275 0.15294118 0.2784314 ]\n",
      "   [0.15686275 0.15294118 0.28235295]\n",
      "   ...\n",
      "   [0.16078432 0.14901961 0.28235295]\n",
      "   [0.15294118 0.14901961 0.27058825]\n",
      "   [0.15686275 0.14509805 0.2627451 ]]]]\n"
     ]
    }
   ],
   "source": [
    "print(x_img_train_normalize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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