Handwritten_Digit_Classification_using_mnist.ipynb

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      "name": "Handwritten Digit Classification using mnist.ipynb",
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        "id": "BEfn5fvwguFe"
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      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(xtrain, ytrain),(xtest,ytest) = keras.datasets.mnist.load_data()"
      ],
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        "id": "n1jlGEWahW-r"
      },
      "execution_count": 2,
      "outputs": []
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            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(xtrain[44])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "inRryanLi2yt",
        "outputId": "bec7d34d-8c08-4bf8-aa91-ba77da84362a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f51cdc99550>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain = xtrain / 255\n",
        "xtest = xtest / 255"
      ],
      "metadata": {
        "id": "3XRuxvbFjCUe"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ykL_yJ9Sjmdy",
        "outputId": "412ca746-61fc-469e-f7a6-5de63cef8431"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "        0.        , 0.        , 0.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Flatten(input_shape=(28,28)))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_JMtIPQFjpLT",
        "outputId": "95ad5f7b-e574-46e0-a94f-1d1d369ea0ac"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               100480    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 32)                4128      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                330       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 104,938\n",
            "Trainable params: 104,938\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='sparse_categorical_crossentropy',optimizer='Adam')"
      ],
      "metadata": {
        "id": "IAKG73XtykUh"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(xtrain,ytrain,epochs=20, validation_split=.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCRwID65zEHT",
        "outputId": "5c5a03ff-1467-43b2-c885-e4741cb740f0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1500/1500 [==============================] - 5s 2ms/step - loss: 0.2713 - val_loss: 0.1309\n",
            "Epoch 2/20\n",
            "1500/1500 [==============================] - 4s 2ms/step - loss: 0.1165 - val_loss: 0.1008\n",
            "Epoch 3/20\n",
            "1500/1500 [==============================] - 4s 2ms/step - loss: 0.0812 - val_loss: 0.1296\n",
            "Epoch 4/20\n",
            "1500/1500 [==============================] - 4s 2ms/step - loss: 0.0584 - val_loss: 0.0996\n",
            "Epoch 5/20\n",
            "1500/1500 [==============================] - 4s 2ms/step - loss: 0.0446 - val_loss: 0.0938\n",
            "Epoch 6/20\n",
            "1500/1500 [==============================] - 4s 2ms/step - loss: 0.0385 - val_loss: 0.0843\n",
            "Epoch 7/20\n",
            "1500/1500 [==============================] - 4s 2ms/step - loss: 0.0288 - val_loss: 0.0905\n",
            "Epoch 8/20\n",
            "1500/1500 [==============================] - 4s 2ms/step - loss: 0.0252 - val_loss: 0.0920\n",
            "Epoch 9/20\n",
            "1500/1500 [==============================] - 4s 2ms/step - loss: 0.0217 - val_loss: 0.1001\n",
            "Epoch 10/20\n",
            "1500/1500 [==============================] - 4s 2ms/step - loss: 0.0177 - val_loss: 0.1115\n",
            "Epoch 11/20\n",
            "1500/1500 [==============================] - 3s 2ms/step - loss: 0.0156 - val_loss: 0.1113\n",
            "Epoch 12/20\n",
            "1500/1500 [==============================] - 3s 2ms/step - loss: 0.0152 - val_loss: 0.1133\n",
            "Epoch 13/20\n",
            "1500/1500 [==============================] - 4s 2ms/step - loss: 0.0132 - val_loss: 0.1034\n",
            "Epoch 14/20\n",
            "1500/1500 [==============================] - 3s 2ms/step - loss: 0.0134 - val_loss: 0.1275\n",
            "Epoch 15/20\n",
            "1500/1500 [==============================] - 3s 2ms/step - loss: 0.0128 - val_loss: 0.1192\n",
            "Epoch 16/20\n",
            "1500/1500 [==============================] - 4s 2ms/step - loss: 0.0098 - val_loss: 0.1197\n",
            "Epoch 17/20\n",
            "1500/1500 [==============================] - 4s 3ms/step - loss: 0.0101 - val_loss: 0.1200\n",
            "Epoch 18/20\n",
            "1500/1500 [==============================] - 4s 2ms/step - loss: 0.0091 - val_loss: 0.1588\n",
            "Epoch 19/20\n",
            "1500/1500 [==============================] - 3s 2ms/step - loss: 0.0119 - val_loss: 0.1494\n",
            "Epoch 20/20\n",
            "1500/1500 [==============================] - 3s 2ms/step - loss: 0.0094 - val_loss: 0.1351\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(xtest)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KKjRZAi5zOO-",
        "outputId": "69ce884b-0ed5-4024-8ee4-d8e0f79d059c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.2019574e-16, 1.0785997e-12, 4.1138710e-13, ..., 9.9999952e-01,\n",
              "        1.8597845e-14, 4.2525065e-07],\n",
              "       [4.3540508e-13, 3.0923791e-11, 1.0000000e+00, ..., 1.4250861e-16,\n",
              "        2.7744093e-10, 1.5500475e-23],\n",
              "       [1.0427971e-09, 9.9999535e-01, 1.7583776e-07, ..., 1.4061836e-06,\n",
              "        7.3209819e-08, 2.5321633e-12],\n",
              "       ...,\n",
              "       [1.7875817e-23, 3.2924073e-13, 7.5675208e-21, ..., 1.2169437e-11,\n",
              "        4.5921494e-17, 3.7129686e-09],\n",
              "       [2.6680446e-23, 3.7199757e-22, 5.1007639e-31, ..., 6.2216681e-21,\n",
              "        3.0792775e-11, 2.4522988e-23],\n",
              "       [3.8276471e-16, 3.4677470e-27, 1.4507249e-21, ..., 5.3618174e-21,\n",
              "        2.2515958e-20, 3.5346118e-23]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob = model.predict(xtest)"
      ],
      "metadata": {
        "id": "ALggu097zZ4x"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "46utyTK-zicR",
        "outputId": "f9d8f41e-5808-469f-df47-fed4aa71d223"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.2019574e-16, 1.0785997e-12, 4.1138710e-13, ..., 9.9999952e-01,\n",
              "        1.8597845e-14, 4.2525065e-07],\n",
              "       [4.3540508e-13, 3.0923791e-11, 1.0000000e+00, ..., 1.4250861e-16,\n",
              "        2.7744093e-10, 1.5500475e-23],\n",
              "       [1.0427971e-09, 9.9999535e-01, 1.7583776e-07, ..., 1.4061836e-06,\n",
              "        7.3209819e-08, 2.5321633e-12],\n",
              "       ...,\n",
              "       [1.7875817e-23, 3.2924073e-13, 7.5675208e-21, ..., 1.2169437e-11,\n",
              "        4.5921494e-17, 3.7129686e-09],\n",
              "       [2.6680446e-23, 3.7199757e-22, 5.1007639e-31, ..., 6.2216681e-21,\n",
              "        3.0792775e-11, 2.4522988e-23],\n",
              "       [3.8276471e-16, 3.4677470e-27, 1.4507249e-21, ..., 5.3618174e-21,\n",
              "        2.2515958e-20, 3.5346118e-23]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = y_prob.argmax(axis=1)"
      ],
      "metadata": {
        "id": "ypKEAdcdzjl6"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "gXpcMaWdzm-N"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(ytest, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJTGcBzWzxOf",
        "outputId": "bfc58123-0cf3-43a1-f209-9bb69988bca3"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9773"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "0oTp4H6Mz23y",
        "outputId": "9af290b6-1b60-49ee-98ee-82786e3bc88c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f51281913d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(xtest[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "XWY6hu_x2HpX",
        "outputId": "708ad2ac-431e-4c89-9e72-b26a5a876f19"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f512935b8d0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(xtest[1].reshape(1,28,28)).argmax(axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UZxJ0TeS2hno",
        "outputId": "3f32e74a-84de-41c1-8cd6-01bdea65821d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "E0JJlUYw25ml"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}