MNIST_digit_pred.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST_digit_pred.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNxBRxQaKjRSdW3y4ojzeBz",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abhirukth/Data-Sci.-File/blob/main/MNIST_digit_pred.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ylkHjoq_El0a"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7AyIG4zFVOL"
      },
      "source": [
        "(ds_train, ds_test), ds_info = tfds.load('mnist',\n",
        "                                         split = ['train', 'test'],\n",
        "                                         shuffle_files = True,\n",
        "                                         as_supervised = True,\n",
        "                                         with_info = True)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DIe4R4nDGNtN"
      },
      "source": [
        "def normalize(image, label):\n",
        "  return tf.cast(image, tf.float32) / 255. , label"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuKcd4cVIqrz"
      },
      "source": [
        "ds_train = ds_train.map(normalize, num_parallel_calls = tf.data.experimental.AUTOTUNE)\n",
        "ds_train = ds_train.cache().shuffle(ds_info.splits['train'].num_examples).batch(128).prefetch(tf.data.experimental.AUTOTUNE)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pvkE9IEiKQfT"
      },
      "source": [
        "ds_test = ds_test.map(normalize, num_parallel_calls = tf.data.experimental.AUTOTUNE)\n",
        "ds_test = ds_test.cache().shuffle(ds_info.splits['test'].num_examples).batch(128).prefetch(tf.data.experimental.AUTOTUNE)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-SURXk9OfQD"
      },
      "source": [
        "classes = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "VgsCPhn_LCQo",
        "outputId": "35e6a48c-fad0-4423-bfea-5730c5b86e01"
      },
      "source": [
        "plt.figure(figsize = (10, 10))\n",
        "\n",
        "for images, labels in ds_train.take(3):\n",
        "  for i in range(9):\n",
        "    plt.subplot(3, 3, i + 1)\n",
        "    image = np.squeeze(images[i])\n",
        "    plt.imshow(image)\n",
        "    plt.title(classes[labels[i]])\n",
        "    plt.axis(\"off\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SkennH_COUjE"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape = (28, 28, 1)),\n",
        "  tf.keras.layers.Dense(150, activation = 'relu'),\n",
        "  tf.keras.layers.Dense(10, activation = 'softmax')  \n",
        "])"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0R7Eb3nxQ1Hw",
        "outputId": "8e2bf82a-51fd-425b-f134-7b8b28cf801a"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_4 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 150)               117750    \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 10)                1510      \n",
            "=================================================================\n",
            "Total params: 119,260\n",
            "Trainable params: 119,260\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "FJaOO_ifRCiY",
        "outputId": "a22698b2-cce7-445c-85b8-418d1299d0ed"
      },
      "source": [
        "tf.keras.utils.plot_model(model)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KAWFrQHRZfH"
      },
      "source": [
        "model.compile(optimizer = tf.keras.optimizers.Adam(0.001),\n",
        "              loss = 'sparse_categorical_crossentropy',\n",
        "              metrics = ['accuracy'])"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJxeCRLqSh9m",
        "outputId": "7465f3fd-24b4-4a8c-a871-ffc2e71fbaf9"
      },
      "source": [
        "history = model.fit(\n",
        "    ds_train,\n",
        "    validation_data = ds_test,\n",
        "    epochs = 15\n",
        ")"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.3518 - accuracy: 0.9027 - val_loss: 0.1867 - val_accuracy: 0.9474\n",
            "Epoch 2/15\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.1593 - accuracy: 0.9537 - val_loss: 0.1310 - val_accuracy: 0.9607\n",
            "Epoch 3/15\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.1143 - accuracy: 0.9667 - val_loss: 0.1042 - val_accuracy: 0.9674\n",
            "Epoch 4/15\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0872 - accuracy: 0.9749 - val_loss: 0.0892 - val_accuracy: 0.9736\n",
            "Epoch 5/15\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0698 - accuracy: 0.9797 - val_loss: 0.0889 - val_accuracy: 0.9729\n",
            "Epoch 6/15\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0571 - accuracy: 0.9836 - val_loss: 0.0737 - val_accuracy: 0.9771\n",
            "Epoch 7/15\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0480 - accuracy: 0.9861 - val_loss: 0.0720 - val_accuracy: 0.9769\n",
            "Epoch 8/15\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0395 - accuracy: 0.9886 - val_loss: 0.0714 - val_accuracy: 0.9770\n",
            "Epoch 9/15\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0327 - accuracy: 0.9911 - val_loss: 0.0685 - val_accuracy: 0.9787\n",
            "Epoch 10/15\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0276 - accuracy: 0.9929 - val_loss: 0.0706 - val_accuracy: 0.9775\n",
            "Epoch 11/15\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0228 - accuracy: 0.9942 - val_loss: 0.0695 - val_accuracy: 0.9791\n",
            "Epoch 12/15\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0196 - accuracy: 0.9952 - val_loss: 0.0701 - val_accuracy: 0.9777\n",
            "Epoch 13/15\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0165 - accuracy: 0.9959 - val_loss: 0.0705 - val_accuracy: 0.9784\n",
            "Epoch 14/15\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0143 - accuracy: 0.9967 - val_loss: 0.0728 - val_accuracy: 0.9786\n",
            "Epoch 15/15\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0122 - accuracy: 0.9973 - val_loss: 0.0706 - val_accuracy: 0.9785\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7PQ9VCIuS8HI",
        "outputId": "16507936-c5b3-48c8-ef3e-94ca7560f10f"
      },
      "source": [
        "for i in history.history:\n",
        "  print(i)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loss\n",
            "accuracy\n",
            "val_loss\n",
            "val_accuracy\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "naEoVgHEVdTM",
        "outputId": "85d356a6-c9c8-4fcb-cced-ca005cc7e287"
      },
      "source": [
        "plt.figure(figsize = (10, 10))\n",
        "plt.plot(range(15), history.history['val_loss'])"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fcaa1aa3a90>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "uw2X6Py1V9Tk",
        "outputId": "738f0e02-5354-41f6-f301-cde1409a6874"
      },
      "source": [
        "plt.figure(figsize = (10, 10))\n",
        "plt.plot(range(15), history.history['accuracy'])"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fcaa1a15210>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gb8eOD2-Wgnc",
        "outputId": "5eab4645-0cb3-4021-faf0-a553a01e696a"
      },
      "source": [
        "model.evaluate(ds_test)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79/79 [==============================] - 0s 2ms/step - loss: 0.0706 - accuracy: 0.9785\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07056189328432083, 0.9785000085830688]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-r7KPLVIZfZS"
      },
      "source": [
        "pred = model.predict(ds_test)"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hyiElJqzZ9m5",
        "outputId": "15c0715d-6268-4090-e1ca-46499a58e380"
      },
      "source": [
        "pred"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.5331431e-10, 4.0921489e-12, 1.0093203e-11, ..., 1.3707191e-12,\n",
              "        2.1209332e-10, 1.6554575e-08],\n",
              "       [3.2186666e-14, 9.7335820e-11, 1.0876312e-11, ..., 5.3044206e-13,\n",
              "        4.6924189e-08, 4.9159548e-11],\n",
              "       [5.1463933e-13, 8.9677703e-11, 2.1259680e-12, ..., 4.7592729e-13,\n",
              "        7.5872408e-10, 1.2821467e-08],\n",
              "       ...,\n",
              "       [6.6225914e-09, 1.4361197e-10, 8.6497165e-10, ..., 1.6122526e-19,\n",
              "        1.1846320e-10, 1.4458544e-14],\n",
              "       [1.0000000e+00, 6.2041596e-19, 1.3351805e-11, ..., 4.6396526e-11,\n",
              "        1.7655443e-14, 1.1765482e-10],\n",
              "       [4.8641655e-12, 9.4406633e-13, 5.9397104e-10, ..., 9.9999964e-01,\n",
              "        3.5376751e-14, 1.4005330e-07]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RjiGbWdZ_bK"
      },
      "source": [
        "pred = list(map(lambda x : np.argmax(x), pred))"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9G31i7jAazUh",
        "outputId": "a4504995-9711-4157-e655-8ec746c81785"
      },
      "source": [
        "pred"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 2,\n",
              " 3,\n",
              " 7,\n",
              " 4,\n",
              " 5,\n",
              " 1,\n",
              " 3,\n",
              " 5,\n",
              " 0,\n",
              " 7,\n",
              " 3,\n",
              " 5,\n",
              " 3,\n",
              " 4,\n",
              " 4,\n",
              " 9,\n",
              " 9,\n",
              " 4,\n",
              " 4,\n",
              " 7,\n",
              " 0,\n",
              " 6,\n",
              " 9,\n",
              " 6,\n",
              " 8,\n",
              " 3,\n",
              " 8,\n",
              " 0,\n",
              " 8,\n",
              " 0,\n",
              " 2,\n",
              " 3,\n",
              " 1,\n",
              " 9,\n",
              " 5,\n",
              " 4,\n",
              " 0,\n",
              " 5,\n",
              " 7,\n",
              " 1,\n",
              " 8,\n",
              " 1,\n",
              " 9,\n",
              " 4,\n",
              " 4,\n",
              " 5,\n",
              " 9,\n",
              " 1,\n",
              " 7,\n",
              " 8,\n",
              " 1,\n",
              " 7,\n",
              " 7,\n",
              " 2,\n",
              " 2,\n",
              " 1,\n",
              " 6,\n",
              " 1,\n",
              " 9,\n",
              " 0,\n",
              " 4,\n",
              " 4,\n",
              " 5,\n",
              " 7,\n",
              " 7,\n",
              " 3,\n",
              " 9,\n",
              " 3,\n",
              " 9,\n",
              " 2,\n",
              " 4,\n",
              " 5,\n",
              " 8,\n",
              " 4,\n",
              " 5,\n",
              " 4,\n",
              " 8,\n",
              " 5,\n",
              " 4,\n",
              " 1,\n",
              " 8,\n",
              " 8,\n",
              " 2,\n",
              " 5,\n",
              " 9,\n",
              " ...]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cByX0kcsdQE6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}