TF_Keras_CNN_CIFAR_10.ipynb

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      "pygments_lexer": "ipython3",
      "version": "3.7.4"
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    "colab": {
      "name": "TF Keras CNN-CIFAR-10.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/D-Idan/TF-KerasCNN-CIFAR-10/blob/main/TF_Keras_CNN_CIFAR_10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jzN8SQ3Ck1fx"
      },
      "source": [
        "# CIFAR-10 Multiple Classes\n",
        "\n",
        "Let's go over another example of using Keras and building out CNNs. This time will use another famous data set, the CIFAR-10 dataset which consists of 10 different image types."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZekxeYPck1f2"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pyRkAA1Fk1f4"
      },
      "source": [
        "-----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5w1AW9qxk1f4"
      },
      "source": [
        "# The Data\n",
        "\n",
        "CIFAR-10 is a dataset of 50,000 32x32 color training images, labeled over 10 categories, and 10,000 test images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oJvKd1Qk1f5"
      },
      "source": [
        "from tensorflow.keras.datasets import cifar10\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dzCgQPsik1f6",
        "outputId": "1fac6cd9-273a-4c29-91a3-8566d29fbd09"
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LjFeIEtXk1f7",
        "outputId": "33873f80-da1f-4604-d3fa-6139c521ec8a"
      },
      "source": [
        "x_train[0].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(32, 32, 3)"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apK0aLV2k1f9"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4Y4iBD3k1f9",
        "outputId": "f3a64e7f-3792-4332-ca30-fa136fd899d7"
      },
      "source": [
        "# FROG\n",
        "plt.imshow(x_train[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x29857344cc8>"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64BPcxsCk1f_",
        "outputId": "9201be31-27ae-460a-e147-7e90bc5c7fbb"
      },
      "source": [
        "# HORSE\n",
        "plt.imshow(x_train[12])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x298573ecdc8>"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMT5oTgak1f_"
      },
      "source": [
        "# PreProcessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GtRRc1jsk1f_",
        "outputId": "e589e98b-8874-4243-b1dd-74c086083a3a"
      },
      "source": [
        "x_train[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[[ 59,  62,  63],\n",
              "        [ 43,  46,  45],\n",
              "        [ 50,  48,  43],\n",
              "        ...,\n",
              "        [158, 132, 108],\n",
              "        [152, 125, 102],\n",
              "        [148, 124, 103]],\n",
              "\n",
              "       [[ 16,  20,  20],\n",
              "        [  0,   0,   0],\n",
              "        [ 18,   8,   0],\n",
              "        ...,\n",
              "        [123,  88,  55],\n",
              "        [119,  83,  50],\n",
              "        [122,  87,  57]],\n",
              "\n",
              "       [[ 25,  24,  21],\n",
              "        [ 16,   7,   0],\n",
              "        [ 49,  27,   8],\n",
              "        ...,\n",
              "        [118,  84,  50],\n",
              "        [120,  84,  50],\n",
              "        [109,  73,  42]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[208, 170,  96],\n",
              "        [201, 153,  34],\n",
              "        [198, 161,  26],\n",
              "        ...,\n",
              "        [160, 133,  70],\n",
              "        [ 56,  31,   7],\n",
              "        [ 53,  34,  20]],\n",
              "\n",
              "       [[180, 139,  96],\n",
              "        [173, 123,  42],\n",
              "        [186, 144,  30],\n",
              "        ...,\n",
              "        [184, 148,  94],\n",
              "        [ 97,  62,  34],\n",
              "        [ 83,  53,  34]],\n",
              "\n",
              "       [[177, 144, 116],\n",
              "        [168, 129,  94],\n",
              "        [179, 142,  87],\n",
              "        ...,\n",
              "        [216, 184, 140],\n",
              "        [151, 118,  84],\n",
              "        [123,  92,  72]]], dtype=uint8)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_iXWskQk1gA",
        "outputId": "487acae5-7778-407c-c0af-0d7ae39a50ff"
      },
      "source": [
        "x_train[0].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(32, 32, 3)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bxIX4vCPk1gA",
        "outputId": "536f3541-40e1-4fce-8579-821ac1ae4f9a"
      },
      "source": [
        "x_train.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "255"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1At7ZHxDk1gB"
      },
      "source": [
        "x_train = x_train/225"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bf4826Tk1gB"
      },
      "source": [
        "x_test = x_test/255"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3G-6dLMjk1gB",
        "outputId": "eeca78e7-09df-4a7f-cc25-4cb53d732122"
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQ9j-uJfk1gC",
        "outputId": "3d7f8034-421b-4be4-9d5c-af43b0168d28"
      },
      "source": [
        "x_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0X3eOwYk1gC"
      },
      "source": [
        "## Labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rnC8pVXk1gC"
      },
      "source": [
        "from tensorflow.keras.utils import to_categorical"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nGMb8fd2k1gD",
        "outputId": "326b7313-a52b-428c-9b3c-879ff70f3227"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(50000, 1)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kdlp_cRNk1gD",
        "outputId": "5bc89604-9403-4fa7-dacd-80aad80ef6dc"
      },
      "source": [
        "y_train[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([6], dtype=uint8)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hv2-knyvk1gD"
      },
      "source": [
        "y_cat_train = to_categorical(y_train,10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YjvEhF5bk1gD",
        "outputId": "9fdeb1c1-7195-41f3-8230-8455feba67ff"
      },
      "source": [
        "y_cat_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(50000, 10)"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFpfxNrbk1gE",
        "outputId": "20c41c95-f452-4933-fe2b-95cad9066693"
      },
      "source": [
        "y_cat_train[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 1., 0., 0., 0.], dtype=float32)"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MiNXHhxgk1gE"
      },
      "source": [
        "y_cat_test = to_categorical(y_test,10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7_YPMHT2k1gE"
      },
      "source": [
        "----------\n",
        "# Building the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKJQvEs0k1gE"
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0S_ceb-_k1gE"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "## FIRST SET OF LAYERS\n",
        "\n",
        "# CONVOLUTIONAL LAYER\n",
        "model.add(Conv2D(filters=32, kernel_size=(4,4),input_shape=(32, 32, 3), activation='relu',))\n",
        "# POOLING LAYER\n",
        "model.add(MaxPool2D(pool_size=(2, 2)))\n",
        "\n",
        "## SECOND SET OF LAYERS\n",
        "\n",
        "# CONVOLUTIONAL LAYER\n",
        "model.add(Conv2D(filters=32, kernel_size=(4,4),input_shape=(32, 32, 3), activation='relu',))\n",
        "# POOLING LAYER\n",
        "model.add(MaxPool2D(pool_size=(2, 2)))\n",
        "\n",
        "# FLATTEN IMAGES FROM 28 by 28 to 764 BEFORE FINAL LAYER\n",
        "model.add(Flatten())\n",
        "\n",
        "# 256 NEURONS IN DENSE HIDDEN LAYER (YOU CAN CHANGE THIS NUMBER OF NEURONS)\n",
        "model.add(Dense(256, activation='relu'))\n",
        "\n",
        "# LAST LAYER IS THE CLASSIFIER, THUS 10 POSSIBLE CLASSES\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='rmsprop',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kobkibsk1gF",
        "outputId": "ce0e0807-1434-41ff-bff9-40b4bf90ad41"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 29, 29, 32)        1568      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 11, 11, 32)        16416     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 5, 5, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 800)               0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 256)               205056    \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 10)                2570      \n",
            "=================================================================\n",
            "Total params: 225,610\n",
            "Trainable params: 225,610\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5mnd-65k1gF"
      },
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lqqXrPJk1gF"
      },
      "source": [
        "early_stop = EarlyStopping(monitor='val_loss',patience=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfbARO9Kk1gF",
        "outputId": "4aa9966e-b325-45d1-dce6-1ee5cde2eeff"
      },
      "source": [
        "model.fit(x_train,y_cat_train,epochs=15,validation_data=(x_test,y_cat_test),callbacks=[early_stop])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train on 50000 samples, validate on 10000 samples\n",
            "Epoch 1/15\n",
            "50000/50000 [==============================] - 6s 129us/sample - loss: 1.5199 - accuracy: 0.4534 - val_loss: 1.3075 - val_accuracy: 0.5498\n",
            "Epoch 2/15\n",
            "50000/50000 [==============================] - 6s 113us/sample - loss: 1.1533 - accuracy: 0.5974 - val_loss: 1.1743 - val_accuracy: 0.5795\n",
            "Epoch 3/15\n",
            "50000/50000 [==============================] - 6s 113us/sample - loss: 0.9973 - accuracy: 0.6571 - val_loss: 1.0654 - val_accuracy: 0.6274\n",
            "Epoch 4/15\n",
            "50000/50000 [==============================] - 6s 113us/sample - loss: 0.8939 - accuracy: 0.6938 - val_loss: 0.9890 - val_accuracy: 0.6568\n",
            "Epoch 5/15\n",
            "50000/50000 [==============================] - 6s 113us/sample - loss: 0.8153 - accuracy: 0.7177 - val_loss: 0.9994 - val_accuracy: 0.6601\n",
            "Epoch 6/15\n",
            "50000/50000 [==============================] - 6s 113us/sample - loss: 0.7567 - accuracy: 0.7445 - val_loss: 1.0006 - val_accuracy: 0.6600\n",
            "Epoch 7/15\n",
            "50000/50000 [==============================] - 6s 113us/sample - loss: 0.7085 - accuracy: 0.7622 - val_loss: 0.9845 - val_accuracy: 0.6905\n",
            "Epoch 8/15\n",
            "50000/50000 [==============================] - 6s 113us/sample - loss: 0.6649 - accuracy: 0.7775 - val_loss: 1.2024 - val_accuracy: 0.6537\n",
            "Epoch 9/15\n",
            "50000/50000 [==============================] - 6s 115us/sample - loss: 0.6293 - accuracy: 0.7864 - val_loss: 1.1578 - val_accuracy: 0.6267\n",
            "Epoch 10/15\n",
            "50000/50000 [==============================] - 6s 112us/sample - loss: 0.6069 - accuracy: 0.7972 - val_loss: 1.0988 - val_accuracy: 0.6544\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x29ad5463c08>"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loBOt_D1k1gG"
      },
      "source": [
        "losses = pd.DataFrame(model.history.history)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kHuZLm4Zk1gG",
        "outputId": "75ac728d-249e-40aa-f53f-1b2acf3213f0"
      },
      "source": [
        "losses.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.519891</td>\n",
              "      <td>0.45336</td>\n",
              "      <td>1.307454</td>\n",
              "      <td>0.5498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.153265</td>\n",
              "      <td>0.59738</td>\n",
              "      <td>1.174292</td>\n",
              "      <td>0.5795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.997324</td>\n",
              "      <td>0.65712</td>\n",
              "      <td>1.065440</td>\n",
              "      <td>0.6274</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.893939</td>\n",
              "      <td>0.69382</td>\n",
              "      <td>0.988995</td>\n",
              "      <td>0.6568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.815260</td>\n",
              "      <td>0.71768</td>\n",
              "      <td>0.999398</td>\n",
              "      <td>0.6601</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       loss  accuracy  val_loss  val_accuracy\n",
              "0  1.519891   0.45336  1.307454        0.5498\n",
              "1  1.153265   0.59738  1.174292        0.5795\n",
              "2  0.997324   0.65712  1.065440        0.6274\n",
              "3  0.893939   0.69382  0.988995        0.6568\n",
              "4  0.815260   0.71768  0.999398        0.6601"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRM1utRYk1gG",
        "outputId": "829f4b9b-89e5-49b8-dad5-aacc43c0ffad"
      },
      "source": [
        "losses[['accuracy','val_accuracy']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x29ae4b5e348>"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4y_p6Xpk1gG",
        "outputId": "cb50b720-79a2-4eff-85d9-9debb8847b3f"
      },
      "source": [
        "losses[['loss','val_loss']].plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x29ae5be1c48>"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3mSit6Atk1gG",
        "outputId": "4f9a71e5-5a34-4142-ff5c-7e250e904070"
      },
      "source": [
        "model.metrics_names"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['loss', 'acc']"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ADMLZZvk1gG",
        "outputId": "5d5fb641-5354-4db9-d3be-e91dd7988251"
      },
      "source": [
        "print(model.metrics_names)\n",
        "print(model.evaluate(x_test,y_cat_test,verbose=0))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['loss', 'accuracy']\n",
            "[1.0987777906417846, 0.6544]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5A1Zg4MXk1gH"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TvTzr5yck1gH"
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "\n",
        "predictions = model.predict(x_test)\n",
        "predictions = np.argmax(predictions, axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kl3IXJa4k1gH",
        "outputId": "f4df007b-48fc-4fa7-c8b3-93587750843d"
      },
      "source": [
        "print(classification_report(y_test,predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.67      0.67      1000\n",
            "           1       0.81      0.77      0.79      1000\n",
            "           2       0.48      0.61      0.54      1000\n",
            "           3       0.54      0.39      0.46      1000\n",
            "           4       0.70      0.55      0.61      1000\n",
            "           5       0.55      0.61      0.58      1000\n",
            "           6       0.74      0.76      0.75      1000\n",
            "           7       0.88      0.55      0.68      1000\n",
            "           8       0.58      0.92      0.71      1000\n",
            "           9       0.77      0.71      0.74      1000\n",
            "\n",
            "    accuracy                           0.65     10000\n",
            "   macro avg       0.67      0.65      0.65     10000\n",
            "weighted avg       0.67      0.65      0.65     10000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-88HpZ3k1gH",
        "outputId": "7adfe856-546f-46b8-cf78-8979c6cc7e42"
      },
      "source": [
        "confusion_matrix(y_test,predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[665,  25,  50,   5,   8,   9,   9,   3, 206,  20],\n",
              "       [ 22, 769,  13,   9,   1,   5,  10,   1, 105,  65],\n",
              "       [ 83,   8, 613,  35,  45,  71,  60,   9,  59,  17],\n",
              "       [ 34,  22, 138, 394,  48, 210,  62,  11,  58,  23],\n",
              "       [ 41,   4, 145,  49, 550,  58,  67,  24,  54,   8],\n",
              "       [ 20,   7,  99, 131,  37, 614,  39,  16,  25,  12],\n",
              "       [ 15,  10,  74,  52,  30,  19, 757,   1,  30,  12],\n",
              "       [ 32,   7, 104,  44,  69, 122,  12, 548,  23,  39],\n",
              "       [ 27,  13,  14,   4,   3,   3,   1,   0, 924,  11],\n",
              "       [ 39,  89,  17,   8,   0,   9,   3,   7, 118, 710]], dtype=int64)"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqS_C84kk1gH",
        "outputId": "0a5c91f5-fb73-442a-9d68-710a9eaefc3a"
      },
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.heatmap(confusion_matrix(y_test,predictions),annot=True)\n",
        "# https://github.com/matplotlib/matplotlib/issues/14751"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x29ae7106fc8>"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffaN3WAqk1gH"
      },
      "source": [
        "# Predicting a given image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDWKvSZNk1gH"
      },
      "source": [
        "my_image = x_test[16]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hf_BEkX0k1gI",
        "outputId": "ab7b7b9d-a722-4c22-9b19-c202b8b16290"
      },
      "source": [
        "plt.imshow(my_image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x29ae76a3b48>"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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EWFmuyNnNbPCyPz8C4NDVGY4QYqVYivT2TQD3Aug3szMAPgfgXjPbg3qitBMA/nBpe2uBrd0YNA3ccjvtNnb0eLC9q8ZloVokBml6nMty1Ujut0JpLtiebeWy1tYdO6ltNs+j17I5fmyx+Kr2zvBY2jv5vhCR16oRLXJ+apTajh7aH2yfPvkm7TN8isuvx0+dp7ae7TdS2+533xFsPzlyivZZGOVfN2PBa9WIvGaR94ynp4tJb7EYuzCLOru7fzLQ/PV3vCchxKqiJ+iESAQ5uxCJIGcXIhHk7EIkgpxdiERobvknB4pEyTkD/sBN5y3vCrb3nufyCUo8uqo8HS5NBADIcHHFauHopPUDYTkRAAYHB6ltvMyjxjryXA5rj9jy+fAYLZI5slgIS4oAUIok7ixESnbNzoTn+JUDx2ifo6//nO+L1XECcNNAP7Vtv+PWcPvNN9E+I2cvUNvY2Ai1VRFJZBqT0YgsVwUvlZWths+BmDSoO7sQiSBnFyIR5OxCJIKcXYhEkLMLkQhydiESoanSm2UyyLeHo7LeKPLIsZnxcE203954He2TaQ9HygFAW/sUtblzOczaeoLtm27ikW2tkUSPlXNcxhm7eIbaNkRqm41PTgTb29t54suODh4RVyzypJgLJS4N5fLdwfaJOT6/UxGZr6MnvD0AaOvqorZaJSzZbbvhBtrn5DY+9xcneYa2O27nkZt33r2H2rItYbn00KHXaJ8jL78abM/M83Nbd3YhEkHOLkQiyNmFSAQ5uxCJIGcXIhGauhqfyWTQTlanCwivdANAPht+vP/66zfQPoWxc9Q2P88DOEpFXnZp8Jabg+09W3fw7WXaqG1tHw+gWSjylWmL5DorzoWVi4kLPF9cb28ftXkkuGN8jG/z9LmzwfZcO5+PzTuvp7Zsnpddmi/ynIIZEmRy9z330D6FAlcM1vfxufrUv3yY2nbcFi57BgAtHeE5uTjJc+E9/l+/Fmz/Pz/8Bu2jO7sQiSBnFyIR5OxCJIKcXYhEkLMLkQhydiESYSnln7YA+AsAGwHUAOxz96+YWR+AbwPYhnoJqI+5ezgKo0GlBkwWwjLa+CQPuPj47nB5n+0380CYgW08WOTNm8N5yQBgeoofwrabw9JbW/c62qdc4VnBunp4AEpnpAZmNZJProfIcoVzPLjj9VcPU1ulxCXAqQmeq230YjjIp7WPB620OJfljASLAED/4FZq61sflmc7Oltpn9+5773UNvMbQ3xfA+uprVzjQUNeDsuDuXaea/BDH//nwfbnnn2K9lnKnb0C4E/c/VYA7wbwx2a2C8BnADzj7jsBPNP4WwhxjbKos7v7sLu/1Hg9A+A1AJsAPATgica/PQHgwys1SCHE8nlH39nNbBuAuwA8D2DA3YeB+gUBAH+cTQix6izZ2c2sC8D3AHza3flzfL/c71Ez229m+ycnol/phRAryJKc3cxaUHf0b7j79xvNI2Y22LAPAgg+KO3u+9x9yN2H1vb2Xo0xCyGugEWd3epV378O4DV3/9JlpicBXHry/2EAP7z6wxNCXC2WEvX2HgCfAnDQzC7VVPosgC8A+I6ZPQLgFICPLrahUqWGU2PhqLL+dh7VtGt7eDkgG8md1tPDJZ7d976P2splLpGUFsK2cplHhmUy3FarcdvCAi9RVYlIb1US5dW7jpdIyoW7AABGzvLowVonj1S0TaSk0QKPUJub4d8O8508l9+m7Tyf3LqB8LmTy/L7XCkSRVcsc5fJt3M5j5XlAgDLhOcqU+XnRzuJlMtk+HEt6uzu/hxAziDgvsX6CyGuDfQEnRCJIGcXIhHk7EIkgpxdiESQswuRCE1NOLlQquD46YtB244eLr11doVlC8/yqCBE5KmI4gWnwgNgmfD+LMenMRMZRyUiryHDt1kp8X7lcjhKrRTZVylSdilDjhkA8nkuNXWQMl+VyPY8IjV1ruFhgJs2baK2tSTqrVYNl4UCgMlxbkN1jppac/zemc/y86ri4f1lI+dOjpynERVVd3YhUkHOLkQiyNmFSAQ5uxCJIGcXIhHk7EIkQlOlt2rNMFEMSy8fuoVHNbVYuPZWzSIyWeQ6Vq5xaaUSSRBZqoSloVhSyVKZ1w2LyWGViDRUIvIaAMzOhaWhC+eHaZ/xkfPUVitFogAL3DY1GU5UUirwOnuFuRlq272R18UbjNjyXeHIvLk5XtPPIudVaz4is0bq4mUQiVQsh8+DaomfO625sFQdG7vu7EIkgpxdiESQswuRCHJ2IRJBzi5EIjR1NR4G5EiOrC09PECiRhYyI3ECqFb4SmaFrKoDwALvhgWSa64SWbFeiAStVGL57iL9xi+MUdvI2XCZp2myOg7w1WAAmJ/hq9anfn6a2s6cPhtsz0ZWi++4k5flunHnTmrLkJJXAA/ymY0c19TEJLUV5nh+utidM2bLkeAgd666XAm6swuRCHJ2IRJBzi5EIsjZhUgEObsQiSBnFyIRFpXezGwLgL8AsBFADcA+d/+KmX0ewB8AuKQDfdbdn4ptK58BtnSHry8dzmWoqoflmlw0v1uR2oqkjBMAlKMBNOH9VSOBNbUq1/IKc7zc0cULwTqZddt5HrhSmgsHmtSKfD7Gxvi+jh49Qm2nT3LpbfOmrcH2j/3+v6B9tm/nAS0zkdJQw6f5OKwtXAZsenqK72uKS2/I8bx7uRaeRzGT49JyhuSgiyiKAC0rFstbtzgVAH/i7i+ZWTeAF83s6Ybty+7+Z0vYhhBilVlKrbdhAMON1zNm9hoAns5TCHFN8o6+s5vZNgB3AXi+0fSYmR0ws8fNTMXXhbiGWbKzm1kXgO8B+LS7TwP4KoAbAexB/c7/RdLvUTPbb2b7Z2f49yQhxMqyJGc3sxbUHf0b7v59AHD3EXevunsNwNcA7A31dfd97j7k7kNd3byetxBiZVnU2a2e5+brAF5z9y9d1j542b99BMChqz88IcTVYimr8e8B8CkAB83s5UbbZwF80sz2oL7WfwLAHy62oXzGcX17WPYqlbg00bIQjjTyfAftU4mUEqpWeDRRLJKO5RGbK/DcaVMT4XJXADA+OkJt0xPj1FYpRGS08+FtHj/+Bu1z5kw4Qg0AZiLRYd3da6ntQx/8QLB9z64dtM+x1/dT23xELi1leP7CyQJ5ryNvdDGSJ6+fSIoAL+MEAJWIPJtvbw+3l/h5WiASa+y4lrIa/xzCJaSimroQ4tpCT9AJkQhydiESQc4uRCLI2YVIBDm7EInQ1ISTeVSxycJP0U1P8+iwzs5wFFJnRzffWSSxYTmSzHEuUoJociqctHFinMtkc5M8gmr2IpflxiKRXEeOvUltJ8+FpbeFAp/faiWSsJEk2QSAWpVLgAcOHA22j5LxAUBrjstrXd1cZr0wzd+zsamwRFWY5/LaXCQa8YM38qSYWa6uwSOlnJhMXCClvACgOB+e+1otVoJKCJEEcnYhEkHOLkQiyNmFSAQ5uxCJIGcXIhGaKr1lzdGTC0fyzMxy3WL25Ilg+4YOHu20kA1HEgHA5HhE8jrPI8CmLl4Ib2+YJ4A8f+oUtY1E5LW5SL2x81O83tjUbFjimZnhMtkUkacAoLjAZcqqcznp4MHjwXYjtf4AYE0Pf8+2buqntnXruG14LCyXTkbmN5vhkWN9vXxflb18jnfccjO1IRd2w9HzvKbfT557Ptg+E5EhdWcXIhHk7EIkgpxdiESQswuRCHJ2IRJBzi5EIjRVequ6Y7Icjmzq8EgU0kQ4CqnMc1Siaz2vG2aTYQkNACpnuFR2/tDhYPvYiRO0T3ueD3LXxnXU1noTT8z494f5/g7/5GfB9slpPr+VWiQiDjwSzSOnT6ZG7iM8KAujFyNy40UeLWfBFImXdheW0bLg70s2cgv8u7/5G2obete7qK21ncvEbxB59vl//Cnt85fffTLYHpMUdWcXIhHk7EIkgpxdiESQswuRCHJ2IRLBPFbvCICZtQF4FkAr6qv333X3z5nZDQC+BaAPwEsAPuXuvF4NgMH+df7IBx8I2j7+4H2RMYRXTmfneVXYNV1ruK2T5zObmYwEyYyMBtunxnmJpLa2NmrrWMtX4//h4BFq2/fNH1Db8EQ4b5kbXzmvRc6BGmJvKe9nZKXeIyvnFlmqt0wLtWXAbXyDkRX8KlcnOtr4PO6+aze3DXFbtjW8zb/+0f+lfX7+6olge6E6i6pXgge3lDv7AoD3uftu1MszP2Bm7wbwpwC+7O47AUwAeGQJ2xJCrBKLOrvXuXTramn8OID3Afhuo/0JAB9ekREKIa4KS63Pnm1UcB0F8DSANwBMuv8ioPkMgE0rM0QhxNVgSc7u7lV33wNgM4C9AELJs4Nf4MzsUTPbb2b754s8uF8IsbK8o9V4d58E8HcA3g1grdkvVn02AzhH+uxz9yF3H+qILFYJIVaWRZ3dzNab2drG63YAvwPgNQB/C+D3G//2MIAfrtQghRDLZymBMIMAnrC6/pUB8B13/yszexXAt8zsPwD4GYCvL7ahSs0xVghLObXuPtqvNRuWSazCJa+Z+XDuMQDItvB8d/ke/uljsCu8LNE/wLfnzqf43DgPTvnRs/upbWSSlwVyIinFJK+axaQ3jnnkuMHkq9j9pZVaMh6R1yKyIoisGJOcM5k8tZW5Koef/MML1PYKCaICgL7+3mD7hQs8B11nW7j02UKBn1OLOru7HwBwV6D9TdS/vwshfgXQE3RCJIKcXYhEkLMLkQhydiESQc4uRCIsGvV2VXdmNgbgZOPPfgA8GVzz0DjeisbxVn7VxrHV3deHDE119rfs2Gy/uw+tys41Do0jwXHoY7wQiSBnFyIRVtPZ963ivi9H43grGsdb+bUZx6p9ZxdCNBd9jBciEVbF2c3sATM7YmbHzewzqzGGxjhOmNlBM3vZzHiY2dXf7+NmNmpmhy5r6zOzp83sWON3OBRq5cfxeTM725iTl83sA00YxxYz+1sze83MDpvZv260N3VOIuNo6pyYWZuZ/dTMXmmM49832m8ws+cb8/FtM+PheSHcvak/ALKop7XaDiAP4BUAu5o9jsZYTgDoX4X9/iaAuwEcuqztPwL4TOP1ZwD86SqN4/MA/k2T52MQwN2N190AjgLY1ew5iYyjqXMCwAB0NV63AHge9YQx3wHwiUb7fwPwR+9ku6txZ98L4Li7v+n11NPfAvDQKoxj1XD3ZwGMv635IdQTdwJNSuBJxtF03H3Y3V9qvJ5BPTnKJjR5TiLjaCpe56oneV0NZ98E4PRlf69mskoH8GMze9HMHl2lMVxiwN2HgfpJB2DDKo7lMTM70PiYv+JfJy7HzLahnj/heazinLxtHECT52QlkryuhrOHUqasliTwHne/G8D7Afyxmf3mKo3jWuKrAG5EvUbAMIAvNmvHZtYF4HsAPu3u4TrdqzOOps+JLyPJK2M1nP0MgC2X/U2TVa407n6u8XsUwA+wupl3RsxsEAAav8PlZ1YYdx9pnGg1AF9Dk+bEzFpQd7BvuPv3G81Nn5PQOFZrThr7fsdJXhmr4ewvANjZWFnMA/gEgHBl+RXEzDrNrPvSawC/C+BQvNeK8iTqiTuBVUzgecm5GnwETZgTMzPUcxi+5u5fuszU1Dlh42j2nKxYktdmrTC+bbXxA6ivdL4B4N+u0hi2o64EvALgcDPHAeCbqH8cLKP+SecRAOsAPAPgWON33yqN438COAjgAOrONtiEcbwX9Y+kBwC83Pj5QLPnJDKOps4JgDtRT+J6APULy7+77Jz9KYDjAP43gNZ3sl09QSdEIugJOiESQc4uRCLI2YVIBDm7EIkgZxciEeTsQiSCnF2IRJCzC5EI/x9L2LoCXmBhVgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Se_n0epgk1gI",
        "outputId": "c7015f37-be0a-4847-9608-39e214a4482f"
      },
      "source": [
        "# SHAPE --> (num_images,width,height,color_channels)\n",
        "model.predict_classes(my_image.reshape(1,32,32,3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([5], dtype=int64)"
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vZ2FAUXk1gI"
      },
      "source": [
        "# 5 is DOG\n",
        "# https://www.cs.toronto.edu/~kriz/cifar.html"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}