mnist_cnn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "mnist-cnn.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrA6dEo0itEv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsvCpVA8jDd8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.datasets import mnist"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OeH092-6jIm-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(x_train,y_train),(x_test,y_test)=mnist.load_data()"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ctwxn3eAjSra",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "79ad6485-7aa8-4ad0-cdcf-8e625ec297a4"
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cO4SxUeEjk5_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "610e3c75-4050-43fe-b4c6-3f98e2f1b981"
      },
      "source": [
        "y_test.shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txCYF-MTjzW2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#First image\n",
        "img1=x_train[0]"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5dWJLBZkEdi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "1e7d68b6-4e7e-46dd-a412-52aba9827048"
      },
      "source": [
        "plt.imshow(img1)\n",
        "#note- mnist consists of greyscale images, since we are using matplotlib to plot it uses the default color"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f3714faf978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LJX1U0jpJmyT9l6SeLurtPyQ9JelJDQVrdod6W6ihQ/QnJW2o/Z3b6feu0Fdb3jculwWS4AQdkARhB5Ig7EAShB1IgrADSRB2IAnCDiTx/65XcTNOWsh5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M0q0R5RekIQ9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "a6136ef8-9bfe-4b12-ea50-d20c4722d7af"
      },
      "source": [
        "y_train[0]"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VRn7nzzjkg1h",
        "colab_type": "text"
      },
      "source": [
        "Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1Tj_ZknkNdh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.utils import to_categorical"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wR5z6D1kafd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_train_cat = to_categorical(y_train,10)\n",
        "y_test_cat = to_categorical(y_test,10)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pbYPHxQjoIBu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "0fa08131-ac58-40b2-dd28-d17644ef2e76"
      },
      "source": [
        "y_train_cat[0]"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cqe8i7LToKz8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train=x_train/255\n",
        "x_test=x_test/255"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V56p-dqToYBQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "1a9c97e8-7c0e-4c25-f418-0193b41760d8"
      },
      "source": [
        "plt.imshow(x_train[0])"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f3714f0c860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmX8VE6doe8k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "6ec7695f-38cd-46b2-ee05-dbe740a6bae7"
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oeUmbJ_0pL0K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train=x_train.reshape(60000,28,28,1)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTXVB_aopSoY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_test=x_test.reshape(10000,28,28,1)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dUjy3LXhpg5A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense,Conv2D,MaxPool2D,Flatten"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "731Oyd1NqD_5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=Sequential()\n",
        "\n",
        "model.add(Conv2D(filters=32,kernel_size=(4,4),activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=(2,2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "\n",
        "model.add(Dense(10,activation='softmax'))\n",
        "\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy']) "
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ggDT-MPtlbr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wo4E8o56to0Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6z2K_Ziit6nk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "early_stop = EarlyStopping(monitor='val_loss',patience=2)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGyaLEEft-VZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "20f1d280-e6fe-4c0d-90e4-6a0dbc426256"
      },
      "source": [
        "model.fit(x_train,y_train_cat,epochs=100,validation_data=(x_test,y_test_cat),callbacks=[early_stop])"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1875/1875 [==============================] - 34s 18ms/step - loss: 0.1314 - accuracy: 0.9604 - val_loss: 0.0483 - val_accuracy: 0.9830\n",
            "Epoch 2/100\n",
            "1875/1875 [==============================] - 34s 18ms/step - loss: 0.0441 - accuracy: 0.9866 - val_loss: 0.0385 - val_accuracy: 0.9875\n",
            "Epoch 3/100\n",
            "1875/1875 [==============================] - 34s 18ms/step - loss: 0.0292 - accuracy: 0.9906 - val_loss: 0.0417 - val_accuracy: 0.9871\n",
            "Epoch 4/100\n",
            "1875/1875 [==============================] - 33s 18ms/step - loss: 0.0192 - accuracy: 0.9940 - val_loss: 0.0400 - val_accuracy: 0.9874\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f3714ee67b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rRRF67cJuKyf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Trained=pd.DataFrame(model.history.history)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1u45pGBtu8Ww",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "4f2decc5-cd56-4e25-ec36-9be1a6a9e30f"
      },
      "source": [
        "Trained"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>0.131413</td>\n",
              "      <td>0.960367</td>\n",
              "      <td>0.048321</td>\n",
              "      <td>0.9830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.044060</td>\n",
              "      <td>0.986617</td>\n",
              "      <td>0.038544</td>\n",
              "      <td>0.9875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.029164</td>\n",
              "      <td>0.990633</td>\n",
              "      <td>0.041744</td>\n",
              "      <td>0.9871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.019183</td>\n",
              "      <td>0.993967</td>\n",
              "      <td>0.040009</td>\n",
              "      <td>0.9874</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       loss  accuracy  val_loss  val_accuracy\n",
              "0  0.131413  0.960367  0.048321        0.9830\n",
              "1  0.044060  0.986617  0.038544        0.9875\n",
              "2  0.029164  0.990633  0.041744        0.9871\n",
              "3  0.019183  0.993967  0.040009        0.9874"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C_CC3hMhvFGM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "107f6ec6-2ab4-424d-c702-93d118435a04"
      },
      "source": [
        "Trained['accuracy'].plot()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f371353a710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fHBRGqlvLZe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "3cd9d6a8-2831-47f0-eafb-cd397975e451"
      },
      "source": [
        "Trained[['loss','val_loss']].plot()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3714df2160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jMAcCj4zvmPh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        },
        "outputId": "01726a45-0208-49a4-d001-3ed676659d32"
      },
      "source": [
        "test=x_test[555]\n",
        "model.predict_classes(test.reshape(1,28,28,1))"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-50-c88bda7f7b55>:2: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8aqpOEH3wHWm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "b9288669-e657-41e4-f08b-3834bd6fe7b9"
      },
      "source": [
        "y_test[555]"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxJvWM5GwjT6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "93f0523f-5ae1-4e19-fb29-26d3d4479295"
      },
      "source": [
        "plt.imshow(test.reshape(28,28))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f37133f1940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAOGElEQVR4nO3df6zV9X3H8ddLvMBAaEQpUmSzIp2x+6HtDdrUGRZTZzHzxz+urLU2M15ta1Yzm9W5ZiXrP87N2i5taLBi6dLauKiRZEZktJsxqYSLpYowh6VQYMC10hQ0Chd474/7tbnqPZ9z7/kN7+cjuTnnfN/ne77vnPDi+z3fzznfjyNCAE5+p3S7AQCdQdiBJAg7kARhB5Ig7EASp3ZyY5M9JaZqeic3CaTypl7XkTjssWpNhd32lZK+IWmSpO9ExN2l50/VdF3sy5vZJICC9bGuZq3hw3jbkyR9S9LHJV0gaantCxp9PQDt1cxn9kWSXo6I7RFxRNIPJV3TmrYAtFozYZ8nadeox7urZW9je8D2oO3BYR1uYnMAmtH2s/ERsSIi+iOiv09T2r05ADU0E/Y9kuaPenx2tQxAD2om7BskLbT9ftuTJX1C0urWtAWg1RoeeouIo7Zvk7RGI0NvKyPixZZ1BqClmhpnj4gnJD3Rol4AtBFflwWSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiCJpmZxBU5U2+/5SLG+5ZPfLNavXvKpYv34z7ZOuKd2ayrstndIOiTpmKSjEdHfiqYAtF4r9ux/GhG/asHrAGgjPrMDSTQb9pD0lO2NtgfGeoLtAduDtgeHdbjJzQFoVLOH8ZdGxB7b75W01vb/RMTTo58QESskrZCkmZ4VTW4PQIOa2rNHxJ7qdkjSY5IWtaIpAK3XcNhtT7c94637kq6QtLlVjQForWYO4+dIesz2W6/zg4h4siVdYUImzZxZs7Z/6QeL655ypPzasx78SSMt9YTffOqSmrV64+gDuxYX6704jl5Pw2GPiO2S/riFvQBoI4begCQIO5AEYQeSIOxAEoQdSIKfuJ4E9v1l7eG15/5heXHdD2+8vvziDzbSUYdc8kfF8tf/8Vs1a+vemFZcd/+fT62z8UN16r2HPTuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJME4+wlg0uzZxfpXv1h7MHzJS0uK65559baGeuoFQ39X/n3us28sqFl76rJzi+see/WVhnrqZezZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJxtlPAJ4xvVi/atqbNWv33Ht2cd2p8X8N9dQJp84v9/5PH3ykWJ936sGatbWTz2+opxMZe3YgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIJx9hPA6+eXf89eMu2/y1MLH2/4ldtveP4ZxfoV04aL9QU/+lzN2nl7f9pQTyeyunt22yttD9nePGrZLNtrbW+rbk9vb5sAmjWew/jvSrryHcvulLQuIhZKWlc9BtDD6oY9Ip6WdOAdi6+RtKq6v0rStS3uC0CLNfqZfU5E7K3u75M0p9YTbQ9IGpCkqSrPrwWgfZo+Gx8RISkK9RUR0R8R/X2a0uzmADSo0bDvtz1Xkqrboda1BKAdGg37akk3VvdvlPR4a9oB0C51P7PbfkjSYkln2t4t6SuS7pb0sO2bJO2UVGeSb5RMOmNWsf65rz9crH956A9r1uLNww311AmnnlXzVI8kacG/binWh469Xqwv/MwLNWs1P3eexOqGPSKW1ihd3uJeALQRX5cFkiDsQBKEHUiCsANJEHYgCX7i2gMOLl5YrP/J1NXF+vK/rj3yOXl4Q0M9dcK+q8vTJv/H+9YU61uPuFiPo0cn3NPJjD07kARhB5Ig7EAShB1IgrADSRB2IAnCDiTBOHsPOOv2nxfr246eVqxPfrJ3x9I9pfbViRbfsr647i+GXyvWB/7mjmJ9msqvnw17diAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgnH2Dnjl1o8U62sWLC/WP7zss8X6mfrJhHvqlIPXXlSzdu/cbxfXvXnXnxXr0x5lHH0i2LMDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKMs3fA8b7y9c3rmb3xYLHezumHS79Hl6Shv/pQsf7PX1xRs/bwa+8prrvn03OLdenlOnWMVnfPbnul7SHbm0ctW2Z7j+1N1d+S9rYJoFnjOYz/rqQrx1h+X0RcWP090dq2ALRa3bBHxNOSDnSgFwBt1MwJuttsP18d5p9e60m2B2wP2h4c1uEmNgegGY2GfbmkBZIulLRX0r21nhgRKyKiPyL6+1Q+2QOgfRoKe0Tsj4hjEXFc0v2SFrW2LQCt1lDYbY8eE7lO0uZazwXQG+qOs9t+SNJiSWfa3i3pK5IW275QI0O8OyTd0sYe03vp1mnF+uxnav9efubO8nmSX3+g/NGq3rXdn5xb/i1+yce2Xlasn/IS4+itVDfsEbF0jMUPtKEXAG3E12WBJAg7kARhB5Ig7EAShB1Igp+4dsCcDeWph58/8max/our7i9v4Krapd8cf6P82sPl/+/v2nFdsX7LsfJlsr8575matUMPziuu+x7tKtYxMezZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJR7TzQsRvN9Oz4mJf3rHtnSiGr+gv1nfecKzh156x4XeK9fetGSrWj2//ZbF+2o9mFuvbXp1ds3bWtVuL62Li1sc6HYwDY167nD07kARhB5Ig7EAShB1IgrADSRB2IAnCDiTB79l7QN9Tg8X6eU+1b9v1RvC3rSpPybx9wcpi/fef/uwEO0K7sGcHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQYZ0/u1HPPKda/esnjxfp/vVHeX5x3/56ataPFNdFqdffstufb/rHtLbZftP2Favks22ttb6tuT29/uwAaNZ7D+KOS7oiICyRdIunzti+QdKekdRGxUNK66jGAHlU37BGxNyKeq+4fkrRV0jxJ10haVT1tlaRr29UkgOZN6DO77XMkXSRpvaQ5EbG3Ku2TNKfGOgOSBiRpqqY12ieAJo37bLzt0yQ9Iun2iDg4uhYjV60c88qVEbEiIvojor9PU5pqFkDjxhV2230aCfr3I+LRavF+23Or+lxJ5cuUAuiquofxti3pAUlbI+Jro0qrJd0o6e7qtjxGg+44ZVKx/N4fvFqsf3JGuX7uv99arC/c8Wyxjs4Zz2f2j0q6QdILtjdVy+7SSMgftn2TpJ2Srm9PiwBaoW7YI+IZSWNedF4SMz4AJwi+LgskQdiBJAg7kARhB5Ig7EAS/MT1JPfrTy8q1tf87vJi/fItVxfrH/jbnxbrnZsQHPWwZweSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJBhnPwlMOr32hX2/s+y+4rrPvtlXrPd9aUaxHod/Wayjd7BnB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkGGc/Cez7i/Nr1oZjTXHdL990c7E+aeNzDfWE3sOeHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeScET5yt6250v6nqQ5GrkM+IqI+IbtZZJulvRK9dS7IuKJ0mvN9Ky42Ez8CrTL+ling3FgzFmXx/OlmqOS7oiI52zPkLTR9tqqdl9E/EurGgXQPuOZn32vpL3V/UO2t0qa1+7GALTWhD6z2z5H0kWS1leLbrP9vO2Vtse8NpLtAduDtgeHdbipZgE0btxht32apEck3R4RByUtl7RA0oUa2fPfO9Z6EbEiIvojor9PU1rQMoBGjCvstvs0EvTvR8SjkhQR+yPiWEQcl3S/pPIMggC6qm7YbVvSA5K2RsTXRi2fO+pp10na3Pr2ALTKeM7Gf1TSDZJesL2pWnaXpKW2L9TIcNwOSbe0pUMALTGes/HPSBpr3K44pg6gt/ANOiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBJ1LyXd0o3Zr0jaOWrRmZJ+1bEGJqZXe+vVviR6a1Qre/u9iJg9VqGjYX/Xxu3BiOjvWgMFvdpbr/Yl0VujOtUbh/FAEoQdSKLbYV/R5e2X9GpvvdqXRG+N6khvXf3MDqBzur1nB9AhhB1Ioitht32l7Zdsv2z7zm70UIvtHbZfsL3J9mCXe1lpe8j25lHLZtlea3tbdTvmHHtd6m2Z7T3Ve7fJ9pIu9Tbf9o9tb7H9ou0vVMu7+t4V+urI+9bxz+y2J0n6X0kfk7Rb0gZJSyNiS0cbqcH2Dkn9EdH1L2DYvkzSa5K+FxF/UC27R9KBiLi7+o/y9Ij4Uo/0tkzSa92exruarWju6GnGJV0r6TPq4ntX6Ot6deB968aefZGklyNie0QckfRDSdd0oY+eFxFPSzrwjsXXSFpV3V+lkX8sHVejt54QEXsj4rnq/iFJb00z3tX3rtBXR3Qj7PMk7Rr1eLd6a773kPSU7Y22B7rdzBjmRMTe6v4+SXO62cwY6k7j3UnvmGa8Z967RqY/bxYn6N7t0oj4kKSPS/p8dbjak2LkM1gvjZ2OaxrvThljmvHf6uZ71+j0583qRtj3SJo/6vHZ1bKeEBF7qtshSY+p96ai3v/WDLrV7VCX+/mtXprGe6xpxtUD7103pz/vRtg3SFpo+/22J0v6hKTVXejjXWxPr06cyPZ0SVeo96aiXi3pxur+jZIe72Ivb9Mr03jXmmZcXX7vuj79eUR0/E/SEo2ckf+5pL/vRg81+jpX0s+qvxe73ZukhzRyWDeskXMbN0k6Q9I6Sdsk/aekWT3U279JekHS8xoJ1twu9XapRg7Rn5e0qfpb0u33rtBXR943vi4LJMEJOiAJwg4kQdiBJAg7kARhB5Ig7EAShB1I4v8BglchQHvkqyAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63GXRGMFwpMN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlTqxy3-xYsc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = model.predict_classes(x_test)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Sdp0uS4xdKB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "b9911df5-39f8-4986-f44a-29862a9aab66"
      },
      "source": [
        "print(classification_report(y_test,predictions))"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99       980\n",
            "           1       0.99      1.00      0.99      1135\n",
            "           2       0.98      0.99      0.98      1032\n",
            "           3       0.98      0.99      0.98      1010\n",
            "           4       0.99      0.99      0.99       982\n",
            "           5       1.00      0.97      0.98       892\n",
            "           6       0.99      0.98      0.99       958\n",
            "           7       0.99      0.98      0.99      1028\n",
            "           8       0.98      0.98      0.98       974\n",
            "           9       0.99      0.98      0.98      1009\n",
            "\n",
            "    accuracy                           0.99     10000\n",
            "   macro avg       0.99      0.99      0.99     10000\n",
            "weighted avg       0.99      0.99      0.99     10000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2wR73TqRxiAU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}