Architecture engineering .ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "day4.ipynb",
      "provenance": [],
      "mount_file_id": "1QSCk07TsJe8IdTvOoBj0dwVdTHLjIx0B",
      "authorship_tag": "ABX9TyNDiUT//54tEMkj0SmCPn6T",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Langrzyk/dw_matrix_road_sign/blob/master/Architecture%20engineering%20.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LceqTVvLGltX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e1d3985a-b1e3-447c-9c45-dfadd1d48ea7"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import datetime\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "%load_ext tensorboard\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from skimage import color, exposure\n",
        "\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The tensorboard extension is already loaded. To reload it, use:\n",
            "  %reload_ext tensorboard\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X0x_DZGYHQjl",
        "colab_type": "code",
        "outputId": "0d086983-1675-4d08-de90-cc1c9407a791",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "cd '/content/drive/My Drive/Colab Notebooks/matrix/matrix_three/dw_matrix_road_sign'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Colab Notebooks/matrix/matrix_three/dw_matrix_road_sign\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tmw1Q5ssIAgT",
        "colab_type": "code",
        "outputId": "00867223-2ba5-4c7d-fca7-908d166f032f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "ls"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\u001b[01;34mdata\u001b[0m/  LICENSE  \u001b[01;34mlogs\u001b[0m/  README.md\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ieYdHuuZICNH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = pd.read_pickle('data/train.p')\n",
        "test = pd.read_pickle('data/test.p')\n",
        "\n",
        "X_train, y_train = train['features'], train['labels']\n",
        "X_test, y_test = test['features'], test['labels']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6I6rofoWIYcS",
        "colab_type": "text"
      },
      "source": [
        "sieć oczekuje macierzy a nie wektora. w macierzy 43 unikalnych kolumn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GyEegJQUI66O",
        "colab_type": "code",
        "outputId": "98b5c553-7107-4bc3-b67e-cd6d188975da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "y_train"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([41, 41, 41, ..., 25, 25, 25], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DeqivfkuI4pI",
        "colab_type": "code",
        "outputId": "2f5828b4-eb88-419c-915e-ff344d55a621",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "np.unique(y_train)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
              "       34, 35, 36, 37, 38, 39, 40, 41, 42], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BpNeOB1aIRIW",
        "colab_type": "code",
        "outputId": "479be806-e940-40b6-9a3a-178a5f4ffe31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(np.unique(y_train))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pOrqaX1IIu3H",
        "colab_type": "code",
        "outputId": "5af14541-5a11-42a3-da99-af0162b98b00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "to_categorical(y_train)[0]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "       0., 0., 0., 0., 0., 0., 0., 1., 0.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCypc23jIyMv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#stworzenie macierzy\n",
        "if y_train.ndim == 1: y_train = to_categorical(y_train)\n",
        "if y_test.ndim == 1: y_test = to_categorical(y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sZrgUQ07JJ4O",
        "colab_type": "code",
        "outputId": "d0868950-e2c9-4c3f-920f-0ee803245e9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34799, 43)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SEeUDJtsJ2aA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_shape = X_train.shape[1:]\n",
        "num_classes = y_train.shape[1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9cTlD7tpJMrf",
        "colab_type": "code",
        "outputId": "0194d8ed-4ce7-4b95-b3d5-8cf60092f8aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "model = Sequential([\n",
        "  Conv2D(filters=64, kernel_size=(3,3), activation='relu',input_shape=input_shape),\n",
        "  \n",
        "  Flatten(),\n",
        "\n",
        "  Dense(num_classes, activation='softmax'),\n",
        "])\n",
        "\n",
        "#model.summary()\n",
        "model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\n",
        "model.fit(X_train, y_train)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1088/1088 [==============================] - 3s 3ms/step - loss: 8.2504 - accuracy: 0.7998\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fedd0189160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqMR1E1lKiPP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_cnn_v1(input_shape,num_classes):\n",
        "  model = Sequential([\n",
        "    Conv2D(filters=64, kernel_size=(3,3), activation='relu',input_shape=input_shape),\n",
        "  \n",
        "    Flatten(),\n",
        "\n",
        "    Dense(num_classes, activation='softmax'),\n",
        "  ])\n",
        "  return model\n",
        "\n",
        "def train_model(model, X_train, y_train, params_fit={}):\n",
        "  model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\n",
        "  \n",
        "  logdir = os.path.join('logs',datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
        "  tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
        "  \n",
        "  model.fit(\n",
        "      X_train, \n",
        "      y_train,\n",
        "      batch_size = params_fit.get('batch_size', 128),\n",
        "      epochs=params_fit.get('epochs',5),\n",
        "      verbose=params_fit.get('verbose',1),\n",
        "      validation_data=params_fit.get('validation_data', (X_train, y_train)),\n",
        "      callbacks= [tensorboard_callback]\n",
        "      )\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qq3ZZmgvMYur",
        "colab_type": "code",
        "outputId": "891c6235-f9f0-4f9f-849b-e22babc6dae7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "model = get_cnn_v1(input_shape, num_classes)\n",
        "model_trained = train_model(model, X_train, y_train)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "272/272 [==============================] - 2s 9ms/step - loss: 41.3814 - accuracy: 0.7270 - val_loss: 0.3151 - val_accuracy: 0.9214\n",
            "Epoch 2/5\n",
            "272/272 [==============================] - 2s 8ms/step - loss: 0.2468 - accuracy: 0.9422 - val_loss: 0.1837 - val_accuracy: 0.9545\n",
            "Epoch 3/5\n",
            "272/272 [==============================] - 2s 9ms/step - loss: 0.1603 - accuracy: 0.9630 - val_loss: 0.1290 - val_accuracy: 0.9704\n",
            "Epoch 4/5\n",
            "272/272 [==============================] - 2s 9ms/step - loss: 0.1347 - accuracy: 0.9700 - val_loss: 0.0940 - val_accuracy: 0.9764\n",
            "Epoch 5/5\n",
            "272/272 [==============================] - 2s 9ms/step - loss: 0.1097 - accuracy: 0.9759 - val_loss: 0.0806 - val_accuracy: 0.9845\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZxjgDHoQ5Na",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predict(model_trained, X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fh91bW6JJjxY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#utworzenie słownika labeli\n",
        "df = pd.read_csv('data/signnames.csv')\n",
        "labels_dict=df.to_dict()['b']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGqk26poJj-g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Okreslenia prawdopodobienstw\n",
        "def predict(model_trained, X_test, y_test, scoring=accuracy_score):\n",
        "  y_test_norm = np.argmax(y_test, axis=1)\n",
        "\n",
        "  y_pred_prob = model_trained.predict(X_test)\n",
        "  y_pred = np.argmax(y_pred_prob, axis=1)\n",
        "\n",
        "  return scoring(y_test_norm, y_pred)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uxHpu9KjPinE",
        "colab_type": "text"
      },
      "source": [
        "architecture engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tv3XviGxQQ9z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_and_predict(model):\n",
        "  model_trained = train_model(model, X_train, y_train)\n",
        "\n",
        "  return predict(model_trained, X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Or9qPFTAOVuh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "7d6834e1-aa6e-4864-c879-a227d757bac7"
      },
      "source": [
        "def get_cnn_v2(input_shape,num_classes):\n",
        "  model = Sequential([\n",
        "    Conv2D(filters=32, kernel_size=(3,3), activation='relu',input_shape=input_shape),\n",
        "    MaxPool2D(),\n",
        "    Dropout(0.3),\n",
        "\n",
        "    Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n",
        "    MaxPool2D(),\n",
        "    Dropout(0.3),\n",
        "\n",
        "    Flatten(),\n",
        "\n",
        "    Dense(1024, activation='relu'),\n",
        "    Dropout(0.3),\n",
        "    Dense(num_classes, activation='softmax'),\n",
        "  ])\n",
        "  return model\n",
        "\n",
        "\n",
        "train_and_predict( get_cnn_v2(input_shape,num_classes) )"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "272/272 [==============================] - 2s 9ms/step - loss: 5.8197 - accuracy: 0.2840 - val_loss: 1.2106 - val_accuracy: 0.6758\n",
            "Epoch 2/5\n",
            "272/272 [==============================] - 2s 9ms/step - loss: 1.1732 - accuracy: 0.6499 - val_loss: 0.5337 - val_accuracy: 0.8816\n",
            "Epoch 3/5\n",
            "272/272 [==============================] - 2s 9ms/step - loss: 0.6984 - accuracy: 0.7911 - val_loss: 0.2397 - val_accuracy: 0.9453\n",
            "Epoch 4/5\n",
            "272/272 [==============================] - 2s 9ms/step - loss: 0.4626 - accuracy: 0.8603 - val_loss: 0.1327 - val_accuracy: 0.9705\n",
            "Epoch 5/5\n",
            "272/272 [==============================] - 2s 9ms/step - loss: 0.3626 - accuracy: 0.8921 - val_loss: 0.0886 - val_accuracy: 0.9816\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8866213151927438"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RNqOxthBRSqV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "edd7c42c-a4e6-4c7d-e9ee-b864cbb97f5e"
      },
      "source": [
        "def get_cnn_v3(input_shape,num_classes):\n",
        "  model = Sequential([\n",
        "    Conv2D(filters=32, kernel_size=(3,3), activation='relu',input_shape=input_shape),\n",
        "    Conv2D(filters=32, kernel_size=(3,3), activation='relu'),\n",
        "    MaxPool2D(),\n",
        "    Dropout(0.3),\n",
        "\n",
        "    Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n",
        "    Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n",
        "    MaxPool2D(),\n",
        "    Dropout(0.3),\n",
        "\n",
        "    Flatten(),\n",
        "\n",
        "    Dense(1024, activation='relu'),\n",
        "    Dropout(0.3),\n",
        "    Dense(num_classes, activation='softmax'),\n",
        "  ])\n",
        "  return model\n",
        "\n",
        "\n",
        "train_and_predict( get_cnn_v3(input_shape,num_classes) )"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "272/272 [==============================] - 3s 12ms/step - loss: 2.3721 - accuracy: 0.5192 - val_loss: 0.3017 - val_accuracy: 0.9397\n",
            "Epoch 2/5\n",
            "272/272 [==============================] - 3s 11ms/step - loss: 0.4214 - accuracy: 0.8799 - val_loss: 0.0673 - val_accuracy: 0.9840\n",
            "Epoch 3/5\n",
            "272/272 [==============================] - 3s 11ms/step - loss: 0.2179 - accuracy: 0.9370 - val_loss: 0.0391 - val_accuracy: 0.9915\n",
            "Epoch 4/5\n",
            "272/272 [==============================] - 3s 11ms/step - loss: 0.1491 - accuracy: 0.9571 - val_loss: 0.0217 - val_accuracy: 0.9947\n",
            "Epoch 5/5\n",
            "272/272 [==============================] - 3s 11ms/step - loss: 0.1246 - accuracy: 0.9652 - val_loss: 0.0176 - val_accuracy: 0.9954\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.972562358276644"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SY1KNt_8RSjB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "002a62b5-451f-4a05-e4cd-464644bb255f"
      },
      "source": [
        "def get_cnn_v4(input_shape,num_classes):\n",
        "  model = Sequential([\n",
        "    Conv2D(filters=32, kernel_size=(3,3), activation='relu',input_shape=input_shape),\n",
        "    Conv2D(filters=32, kernel_size=(3,3), activation='relu', padding='same'),\n",
        "    MaxPool2D(),\n",
        "    Dropout(0.3),\n",
        "\n",
        "    Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'),\n",
        "    Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n",
        "    MaxPool2D(),\n",
        "    Dropout(0.3),\n",
        "\n",
        "    Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'),\n",
        "    Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n",
        "    MaxPool2D(),\n",
        "    Dropout(0.3),\n",
        "\n",
        "    Flatten(),\n",
        "\n",
        "    Dense(1024, activation='relu'),\n",
        "    Dropout(0.3),\n",
        "    Dense(num_classes, activation='softmax'),\n",
        "  ])\n",
        "  return model\n",
        "\n",
        "#get_cnn_v4(input_shape,num_classes).summary()\n",
        "train_and_predict( get_cnn_v4(input_shape,num_classes) )"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "272/272 [==============================] - 4s 14ms/step - loss: 2.3543 - accuracy: 0.3712 - val_loss: 0.5519 - val_accuracy: 0.8638\n",
            "Epoch 2/5\n",
            "272/272 [==============================] - 3s 13ms/step - loss: 0.5935 - accuracy: 0.8228 - val_loss: 0.1293 - val_accuracy: 0.9623\n",
            "Epoch 3/5\n",
            "272/272 [==============================] - 4s 13ms/step - loss: 0.3076 - accuracy: 0.9072 - val_loss: 0.0592 - val_accuracy: 0.9829\n",
            "Epoch 4/5\n",
            "272/272 [==============================] - 4s 13ms/step - loss: 0.2022 - accuracy: 0.9389 - val_loss: 0.0331 - val_accuracy: 0.9899\n",
            "Epoch 5/5\n",
            "272/272 [==============================] - 3s 13ms/step - loss: 0.1578 - accuracy: 0.9530 - val_loss: 0.0447 - val_accuracy: 0.9870\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9537414965986395"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "141Z7atZSs1I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fclB-L7NSsp5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "2b42c548-dbb8-40ae-b8e4-b60f8957ee41"
      },
      "source": [
        "def get_cnn_v5(input_shape,num_classes):\n",
        "  model = Sequential([\n",
        "    Conv2D(filters=32, kernel_size=(3,3), activation='relu',input_shape=input_shape),\n",
        "    Conv2D(filters=32, kernel_size=(3,3), activation='relu', padding='same'),\n",
        "    MaxPool2D(),\n",
        "    Dropout(0.3),\n",
        "\n",
        "    Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'),\n",
        "    Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n",
        "    MaxPool2D(),\n",
        "    Dropout(0.3),\n",
        "\n",
        "    Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'),\n",
        "    Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n",
        "    MaxPool2D(),\n",
        "    Dropout(0.3),\n",
        "\n",
        "    Flatten(),\n",
        "\n",
        "    Dense(1024, activation='relu'),\n",
        "    Dropout(0.3),\n",
        "\n",
        "    Dense(1024, activation='relu'),\n",
        "    Dropout(0.3),\n",
        "    \n",
        "    Dense(num_classes, activation='softmax'),\n",
        "  ])\n",
        "  return model\n",
        "\n",
        "#get_cnn_v4(input_shape,num_classes).summary()\n",
        "train_and_predict( get_cnn_v5(input_shape,num_classes) )"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "272/272 [==============================] - 4s 14ms/step - loss: 2.6177 - accuracy: 0.3035 - val_loss: 1.0296 - val_accuracy: 0.6797\n",
            "Epoch 2/5\n",
            "272/272 [==============================] - 4s 13ms/step - loss: 0.9258 - accuracy: 0.7072 - val_loss: 0.2798 - val_accuracy: 0.9140\n",
            "Epoch 3/5\n",
            "272/272 [==============================] - 4s 13ms/step - loss: 0.4650 - accuracy: 0.8540 - val_loss: 0.1209 - val_accuracy: 0.9636\n",
            "Epoch 4/5\n",
            "272/272 [==============================] - 4s 13ms/step - loss: 0.2872 - accuracy: 0.9113 - val_loss: 0.0672 - val_accuracy: 0.9801\n",
            "Epoch 5/5\n",
            "272/272 [==============================] - 4s 13ms/step - loss: 0.2178 - accuracy: 0.9349 - val_loss: 0.0472 - val_accuracy: 0.9851\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9621315192743765"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0PRSH8kTE1-",
        "colab_type": "text"
      },
      "source": [
        "konwersja RGB do szarosci"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "baujqBtwTEef",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "34174b98-77e3-46e1-e713-0f770c9783af"
      },
      "source": [
        "plt.imshow( color.rgb2gray( X_train[0] ) )"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fed5fc3c1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_Q9GgtnT5F5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "f7518d8b-2725-44ab-96fa-cb96a3008b7e"
      },
      "source": [
        "plt.imshow( color.rgb2gray( X_train[0] ), cmap=plt.get_cmap('gray') )"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fed5e0289b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8qB8kMOmTEVc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#dla wszystkich zdj\n",
        "X_train_gray = color.rgb2gray( X_train ).reshape(-1, 32, 32, 1)\n",
        "X_test_gray = color.rgb2gray( X_test ).reshape(-1, 32, 32, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lGLzVUUMRSX0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "7d0fd05a-0e73-4af5-9771-b0b322d712bc"
      },
      "source": [
        "model = get_cnn_v5((32,32,1),num_classes)\n",
        "model_trained = train_model(model, X_train_gray, y_train, params_fit={})\n",
        "predict(model_trained, X_test_gray, y_test, scoring=accuracy_score)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "272/272 [==============================] - 4s 14ms/step - loss: 2.3320 - accuracy: 0.3361 - val_loss: 0.6184 - val_accuracy: 0.8087\n",
            "Epoch 2/5\n",
            "272/272 [==============================] - 4s 13ms/step - loss: 0.5031 - accuracy: 0.8379 - val_loss: 0.1245 - val_accuracy: 0.9654\n",
            "Epoch 3/5\n",
            "272/272 [==============================] - 4s 13ms/step - loss: 0.2374 - accuracy: 0.9268 - val_loss: 0.0588 - val_accuracy: 0.9810\n",
            "Epoch 4/5\n",
            "272/272 [==============================] - 4s 13ms/step - loss: 0.1505 - accuracy: 0.9539 - val_loss: 0.0364 - val_accuracy: 0.9901\n",
            "Epoch 5/5\n",
            "272/272 [==============================] - 4s 13ms/step - loss: 0.1256 - accuracy: 0.9618 - val_loss: 0.0257 - val_accuracy: 0.9933\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9714285714285714"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t2gcJTZfWvto",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "giKN0TjPWvf5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def preproc_img(img):\n",
        "  hsv = color.rgb2hsv(img)\n",
        "  hsv[:, :, 2] = exposure.equalize_adapthist(hsv[:, :, 2])\n",
        "  img = color.hsv2rgb(hsv)\n",
        "\n",
        "  return img"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IoJBRqlRWvXg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "99db2791-bf2d-4a15-9379-4f467f914944"
      },
      "source": [
        "plt.imshow( X_train[400] )"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fed5ed68588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCEp2IRJByS5EIijZhUgEJbsQifB/aWOy4wHE7REAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5D5QkmlSWvSQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "f301fca4-275b-443d-b627-569dccadb7a6"
      },
      "source": [
        "plt.imshow( preproc_img( X_train[400] ) )"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fed5ed43748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtwjeoYBWvCH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_prp = X_train.copy().astype(np.float32)\n",
        "X_test_prp = X_test.copy().astype(np.float32)\n",
        "\n",
        "for i in tqdm(range(X_train.shape[0])):\n",
        "  X_train_prp[i] = preproc_img(X_train[i])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OzaTl3mvWu4B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmPadusKPbSf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predict(model_trained, X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1AbQ9frSPbF2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MtivIG1XJs4T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "5ca89a95-5931-4a6e-b4db-92a308259161"
      },
      "source": [
        "y_pred_prob[400] #prawdopodobienstwo znaku 400 dla kazdej klasy"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7.5079286e-01, 2.4816768e-01, 1.8961841e-09, 1.7146370e-07,\n",
              "       1.0370801e-03, 2.1409360e-06, 5.5268746e-08, 7.1560071e-15,\n",
              "       2.4283181e-15, 1.4816078e-13, 1.5582507e-13, 8.4647827e-13,\n",
              "       3.6692489e-23, 5.9112643e-13, 2.1825160e-16, 9.3562798e-25,\n",
              "       4.6301811e-15, 1.4111426e-19, 7.4528030e-11, 4.2945597e-19,\n",
              "       6.9909778e-17, 4.5919180e-10, 3.8563856e-16, 2.3337006e-19,\n",
              "       1.4872592e-13, 2.8728165e-16, 2.5623555e-11, 1.0165454e-23,\n",
              "       5.9829613e-10, 1.2400867e-14, 3.4685684e-14, 6.9730293e-16,\n",
              "       4.9714937e-24, 5.5207561e-22, 5.2226683e-27, 1.3597381e-23,\n",
              "       5.4790208e-24, 9.0553005e-21, 7.7005280e-24, 2.2441490e-22,\n",
              "       2.7298202e-11, 1.2829557e-14, 2.6478238e-17], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffRfOUlpLeLm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ffdfd646-e8c5-4cc6-c09e-889fb5a18323"
      },
      "source": [
        "np.argmax(y_pred_prob[400]) # zwraca indeks gdzie znajduje sie maksymalna wartosc"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xDDbw-gL9wC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b158840a-e31f-46a2-c1f7-287fb359b59a"
      },
      "source": [
        "labels_dict[ np.argmax(y_pred_prob[400]) ] #sprawdzenie labelki"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Speed limit (20km/h)'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GVd5FUpJMmFi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "a84b49fa-d2a1-4f1b-da72-f76caf93635d"
      },
      "source": [
        "plt.bar(range(43), y_pred_prob[400])"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 43 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAOyklEQVR4nO3dfYxdeV3H8feHKRUjKA87EtIHpmoJ\naRRBx4LB6AbZpOuSlgQkbSSBBG1MqK6CD101VWtMABPAP/oHFTYSA5S6GhzdMc0G1vgQwc7CCrZN\nYayLnQbZYVlAYtxS+PrHnF2uszNzz+ze6Ux/834lk73nnF/u/eZk+96z9849TVUhSbrxPWW9B5Ak\njYZBl6RGGHRJaoRBl6RGGHRJasSW9Xrhm266qSYmJtbr5SXphnTfffd9qarGlzq2bkGfmJhgZmZm\nvV5ekm5IST6/3DHfcpGkRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRqzbN0Wf\njImjdy+5/4G33XadJ5GkjcMrdElqhEGXpEYYdElqhEGXpEYYdElqhEGXpEYYdElqhEGXpEYYdElq\nhEGXpEb0CnqSfUkuJplNcnSJ4+9Kcn/389kkXxn9qJKklQy9l0uSMeAEcAswB5xNMlVV5x9dU1W/\nOrD+l4CXrMGskqQV9LlC3wvMVtWlqroKnAIOrLD+EPChUQwnSeqvT9C3AZcHtue6fY+T5PnALuBj\nyxw/nGQmycz8/PxqZ5UkrWDUH4oeBO6qqm8udbCqTlbVZFVNjo+Pj/ilJWlz6xP0K8COge3t3b6l\nHMS3WyRpXfQJ+llgd5JdSbayEO2pxYuSvBB4FvDPox1RktTH0KBX1TXgCHAGuACcrqpzSY4n2T+w\n9CBwqqpqbUaVJK2k119BV1XTwPSifccWbf/e6MaSJK2W3xSVpEYYdElqhEGXpEYYdElqhEGXpEYY\ndElqhEGXpEYYdElqhEGXpEYYdElqhEGXpEYYdElqhEGXpEYYdElqhEGXpEYYdElqhEGXpEYYdElq\nRK+gJ9mX5GKS2SRHl1nzuiTnk5xL8sHRjilJGmbo3ymaZAw4AdwCzAFnk0xV1fmBNbuBO4CXV9XD\nSb53rQaWJC2tzxX6XmC2qi5V1VXgFHBg0ZpfAE5U1cMAVfXgaMeUJA3TJ+jbgMsD23PdvkEvAF6Q\n5J+SfDzJvqWeKMnhJDNJZubn55/YxJKkJY3qQ9EtwG7gZuAQ8CdJnrl4UVWdrKrJqpocHx8f0UtL\nkqBf0K8AOwa2t3f7Bs0BU1X1jar6D+CzLAReknSd9An6WWB3kl1JtgIHgalFaz7CwtU5SW5i4S2Y\nSyOcU5I0xNCgV9U14AhwBrgAnK6qc0mOJ9nfLTsDPJTkPHAv8OtV9dBaDS1Jeryhv7YIUFXTwPSi\nfccGHhfwlu5HkrQO/KaoJDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtS\nIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDWiV9CT7EtyMclskqNLHH9j\nkvkk93c/Pz/6USVJKxn6l0QnGQNOALcAc8DZJFNVdX7R0g9X1ZE1mFGS1EOfK/S9wGxVXaqqq8Ap\n4MDajiVJWq0+Qd8GXB7Ynuv2LfaaJJ9OcleSHUs9UZLDSWaSzMzPzz+BcSVJyxnVh6J/DUxU1YuA\ne4D3L7Woqk5W1WRVTY6Pj4/opSVJ0C/oV4DBK+7t3b7HVNVDVfVIt/le4EdHM54kqa8+QT8L7E6y\nK8lW4CAwNbggyfMGNvcDF0Y3oiSpj6G/5VJV15IcAc4AY8CdVXUuyXFgpqqmgF9Osh+4BnwZeOMa\nzixJWsLQoANU1TQwvWjfsYHHdwB3jHY0SdJq+E1RSWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0\nSWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRvQKepJ9\nSS4mmU1ydIV1r0lSSSZHN6IkqY+hQU8yBpwAbgX2AIeS7Fli3TOA24FPjHpISdJwfa7Q9wKzVXWp\nqq4Cp4ADS6z7A+DtwP+OcD5JUk99gr4NuDywPdfte0ySHwF2VNXdKz1RksNJZpLMzM/Pr3pYSdLy\nnvSHokmeArwTeOuwtVV1sqomq2pyfHz8yb60JGlAn6BfAXYMbG/v9j3qGcAPAn+X5AHgZcCUH4xK\n0vXVJ+hngd1JdiXZChwEph49WFVfraqbqmqiqiaAjwP7q2pmTSaWJC1paNCr6hpwBDgDXABOV9W5\nJMeT7F/rASVJ/Wzps6iqpoHpRfuOLbP25ic/liRptfymqCQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBL\nUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMM\nuiQ1olfQk+xLcjHJbJKjSxz/xSSfSXJ/kn9Msmf0o0qSVjI06EnGgBPArcAe4NASwf5gVf1QVb0Y\neAfwzpFPKklaUZ8r9L3AbFVdqqqrwCngwOCCqvrawOZ3ATW6ESVJfWzpsWYbcHlgew546eJFSd4M\nvAXYCrxiqSdKchg4DLBz587VzipJWsHIPhStqhNV9f3AbwK/s8yak1U1WVWT4+Pjo3ppSRL9gn4F\n2DGwvb3bt5xTwKufzFCSpNXrE/SzwO4ku5JsBQ4CU4MLkuwe2LwN+NzoRpQk9TH0PfSqupbkCHAG\nGAPurKpzSY4DM1U1BRxJ8krgG8DDwBvWcmhJ0uP1+VCUqpoGphftOzbw+PYRzyVJWiW/KSpJjTDo\nktQIgy5JjTDoktQIgy5JjTDoktQIgy5JjTDoktSIXl8supFMHL17yf0PvO226zyJJF1fXqFLUiMM\nuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiN6BT3JviQXk8wmObrE8bckOZ/k00k+\nmuT5ox9VkrSSoUFPMgacAG4F9gCHkuxZtOxTwGRVvQi4C3jHqAeVJK2szxX6XmC2qi5V1VXgFHBg\ncEFV3VtV/9NtfhzYPtoxJUnD9An6NuDywPZct285bwL+9skMJUlavZHebTHJ64FJ4KeWOX4YOAyw\nc+fOUb60JG16fa7QrwA7Bra3d/v+nySvBH4b2F9Vjyz1RFV1sqomq2pyfHz8icwrSVpGn6CfBXYn\n2ZVkK3AQmBpckOQlwHtYiPmDox9TkjTM0KBX1TXgCHAGuACcrqpzSY4n2d8t+yPg6cCfJ7k/ydQy\nTydJWiO93kOvqmlgetG+YwOPXzniuSRJq+Q3RSWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWp\nEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEb2C\nnmRfkotJZpMcXeL4Tyb5ZJJrSV47+jElScMMDXqSMeAEcCuwBziUZM+iZf8JvBH44KgHlCT1s6XH\nmr3AbFVdAkhyCjgAnH90QVU90B371hrMKEnqoc9bLtuAywPbc92+VUtyOMlMkpn5+fkn8hSSpGVc\n1w9Fq+pkVU1W1eT4+Pj1fGlJal6foF8Bdgxsb+/2SZI2kD5BPwvsTrIryVbgIDC1tmNJklZraNCr\n6hpwBDgDXABOV9W5JMeT7AdI8mNJ5oCfBd6T5NxaDi1Jerw+v+VCVU0D04v2HRt4fJaFt2IkSevE\nb4pKUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBL\nUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiN6BT3JviQXk8wmObrE8e9I8uHu+CeSTIx6UEnS\nyoYGPckYcAK4FdgDHEqyZ9GyNwEPV9UPAO8C3j7qQSVJK+tzhb4XmK2qS1V1FTgFHFi05gDw/u7x\nXcBPJ8noxpQkDbOlx5ptwOWB7TngpcutqaprSb4KPAf40uCiJIeBw93m15NcfCJDL3LTo6+TFf6/\nYKVjDXvs3OhxPDfL89ysbL3Pz/OXO9An6CNTVSeBk6N8ziQzVTU5yudshedmeZ6b5XluVraRz0+f\nt1yuADsGtrd3+5Zck2QL8D3AQ6MYUJLUT5+gnwV2J9mVZCtwEJhatGYKeEP3+LXAx6qqRjemJGmY\noW+5dO+JHwHOAGPAnVV1LslxYKaqpoD3AX+WZBb4MgvRv15G+hZOYzw3y/PcLM9zs7INe37ihbQk\ntcFvikpSIwy6JDXihg76sFsSbCZJ7kzyYJJ/G9j37CT3JPlc989nreeM6yXJjiT3Jjmf5FyS27v9\nm/78JHlakn9J8q/dufn9bv+u7jYes91tPbau96zrJclYkk8l+Ztue8Oemxs26D1vSbCZ/Cmwb9G+\no8BHq2o38NFuezO6Bry1qvYALwPe3P274vmBR4BXVNUPAy8G9iV5GQu373hXdzuPh1m4vcdmdTtw\nYWB7w56bGzbo9LslwaZRVX/Pwm8YDRq8JcP7gVdf16E2iKr6QlV9snv83yz84dyG54da8PVu86nd\nTwGvYOE2HrBJzw1Aku3AbcB7u+2wgc/NjRz0pW5JsG2dZtmonltVX+ge/xfw3PUcZiPo7gT6EuAT\neH6Ax95SuB94ELgH+HfgK1V1rVuymf9svRv4DeBb3fZz2MDn5kYOulah+6LXpv4d1SRPB/4C+JWq\n+trgsc18fqrqm1X1Yha+Bb4XeOE6j7QhJHkV8GBV3bfes/R1Xe/lMmJ9bkmw2X0xyfOq6gtJnsfC\nFdimlOSpLMT8A1X1l91uz8+AqvpKknuBHweemWRLdyW6Wf9svRzYn+RngKcB3w38MRv43NzIV+h9\nbkmw2Q3ekuENwF+t4yzrpnvf833Ahap658ChTX9+kowneWb3+DuBW1j4jOFeFm7jAZv03FTVHVW1\nvaomWOjLx6rq59jA5+aG/qZo91/Od/PtWxL84TqPtG6SfAi4mYVbe34R+F3gI8BpYCfweeB1VbX4\ng9PmJfkJ4B+Az/Dt90J/i4X30Tf1+UnyIhY+2Btj4QLvdFUdT/J9LPyiwbOBTwGvr6pH1m/S9ZXk\nZuDXqupVG/nc3NBBlyR92438loskaYBBl6RGGHRJaoRBl6RGGHRJaoRBl6RGGHRJasT/AVm6+dAm\nvu8+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzFqNvxoMz1X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "23dbd8f1-30a7-423e-d132-2fb3c9e8a8fd"
      },
      "source": [
        "labels_dict[ 1 ]"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Speed limit (30km/h)'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xw3gdwdRJvTN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "f4bb7665-fe4e-43b5-fac9-65b039597c13"
      },
      "source": [
        "plt.imshow(X_test[400])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7feda608ef60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNM4FmVYhAv-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}