deep learning 1st.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled21.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BO4larurOeFB",
        "outputId": "67f34805-0f90-401e-aa8a-658c6b2b0324"
      },
      "source": [
        "import keras\n",
        "from tensorflow.keras.datasets import mnist\n",
        "(trainX, trainy), (testX, testy) = mnist.load_data()\n",
        "trainy.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000,)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOj1RYf7Pd2q",
        "outputId": "bde32aec-c95e-4f96-b776-5f414379fe5f"
      },
      "source": [
        "trainy[0]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "3JH0b8jePEsK",
        "outputId": "4a1f407f-6a42-4407-b04a-00cd7fd8e831"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.matshow(trainX[0])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5d5dc69450>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JAlnnAIpQnoi",
        "outputId": "41e287a3-e625-42d0-c86f-a433d815ef67"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Activation,Flatten\n",
        "model = Sequential()\n",
        "model.add(Flatten(input_shape=[28,28]))\n",
        "model.add(Dense(20,activation='relu'))\n",
        "model.add(Dense(10,activation='softmax'))\n",
        "model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.fit(trainX,trainy)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.4124 - accuracy: 0.3316\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f5d579a8ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPmXuqyMUPsa"
      },
      "source": [
        "import numpy as np\n"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "6gp1GerHUgZZ",
        "outputId": "98707444-af5d-454e-d893-a0f95ebb5ab8"
      },
      "source": [
        "plt.matshow(testX[0])"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5d582b4ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NmENAI1dTNrB",
        "outputId": "9c10ea9d-e8e0-49f9-9e28-ffbedd7cfd54"
      },
      "source": [
        "yp = model.predict(testX)\n",
        "np.argmax(yp[0])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tEEDajEIVGvy",
        "outputId": "bb24a282-189b-4447-b4c9-3650a1226b6e"
      },
      "source": [
        "model.evaluate(testX,testy)[1]"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 0s 1ms/step - loss: 1.3951 - accuracy: 0.5043\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5042999982833862"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Sl4x6gtJVYQR",
        "outputId": "712f3f27-ac2b-4fe5-9d0b-5161693f611d"
      },
      "source": [
        "scores=[]\n",
        "for i in range(20,50):\n",
        "  model = Sequential()\n",
        "  model.add(Flatten(input_shape=[28,28]))\n",
        "  model.add(Dense(i,activation='relu'))\n",
        "  model.add(Dense(10,activation='relu'))\n",
        "  model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "  model.fit(trainX,trainy)\n",
        "  scores.append(model.evaluate(testX,testy)[1])\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1875/1875 [==============================] - 4s 2ms/step - loss: 8.1502 - accuracy: 0.1935\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.2565 - accuracy: 0.3296\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 3.0563 - accuracy: 0.1092\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3001 - accuracy: 0.1028\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.6689 - accuracy: 0.1009\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3081 - accuracy: 0.0980\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 7.3884 - accuracy: 0.1953\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.1946 - accuracy: 0.1132\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 13.6743 - accuracy: 0.1024\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 12.9446 - accuracy: 0.0990\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 4.2935 - accuracy: 0.1555\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.2985 - accuracy: 0.1099\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 6.5662 - accuracy: 0.1042\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3026 - accuracy: 0.0980\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 14.2571 - accuracy: 0.0977\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 14.5469 - accuracy: 0.0974\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 12.6358 - accuracy: 0.0976\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 11.7562 - accuracy: 0.0982\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.8507 - accuracy: 0.1010\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3163 - accuracy: 0.0980\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 12.9903 - accuracy: 0.1092\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 13.7049 - accuracy: 0.0983\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.8572 - accuracy: 0.1050\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3042 - accuracy: 0.0981\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.6721 - accuracy: 0.1019\n",
            "313/313 [==============================] - 1s 2ms/step - loss: 2.3051 - accuracy: 0.0981\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 14.2136 - accuracy: 0.1036\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 14.2695 - accuracy: 0.1028\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.6547 - accuracy: 0.1023\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3334 - accuracy: 0.0981\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.7487 - accuracy: 0.1600\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.1957 - accuracy: 0.1543\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.6815 - accuracy: 0.0981\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3092 - accuracy: 0.0981\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 12.2034 - accuracy: 0.1119\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 11.6640 - accuracy: 0.1336\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.6067 - accuracy: 0.1001\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3233 - accuracy: 0.0980\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 5.1776 - accuracy: 0.1697\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.1914 - accuracy: 0.1511\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 9.6648 - accuracy: 0.1509\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 8.5153 - accuracy: 0.1862\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 5.0593 - accuracy: 0.1261\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3040 - accuracy: 0.0980\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 5.8589 - accuracy: 0.1717\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.2174 - accuracy: 0.1350\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 3.0373 - accuracy: 0.1180\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3370 - accuracy: 0.0980\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.9468 - accuracy: 0.1150\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3001 - accuracy: 0.0997\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 3.8128 - accuracy: 0.1285\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3026 - accuracy: 0.0980\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 4.8864 - accuracy: 0.1433\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.1863 - accuracy: 0.1497\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.5122 - accuracy: 0.0999\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3242 - accuracy: 0.0981\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 2.6589 - accuracy: 0.0997\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 2.3026 - accuracy: 0.0980\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 13.4648 - accuracy: 0.1272\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 13.5233 - accuracy: 0.1322\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-26-461231847bcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m   \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrainy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m   \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtestX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtesty\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'max'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FTg8cQQfXa36",
        "outputId": "c07f8e5a-5c59-4bd3-dfc7-eda785b71656"
      },
      "source": [
        "scores"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.3296000063419342,\n",
              " 0.10279999673366547,\n",
              " 0.09799999743700027,\n",
              " 0.11320000141859055,\n",
              " 0.0989999994635582,\n",
              " 0.10989999771118164,\n",
              " 0.09799999743700027,\n",
              " 0.09740000218153,\n",
              " 0.0982000008225441,\n",
              " 0.09799999743700027,\n",
              " 0.09830000251531601,\n",
              " 0.09809999912977219,\n",
              " 0.09809999912977219,\n",
              " 0.10279999673366547,\n",
              " 0.09809999912977219,\n",
              " 0.1543000042438507,\n",
              " 0.09809999912977219,\n",
              " 0.13359999656677246,\n",
              " 0.09799999743700027,\n",
              " 0.15109999477863312,\n",
              " 0.18619999289512634,\n",
              " 0.09799999743700027,\n",
              " 0.13500000536441803,\n",
              " 0.09799999743700027,\n",
              " 0.09969999641180038,\n",
              " 0.09799999743700027,\n",
              " 0.14970000088214874,\n",
              " 0.09809999912977219,\n",
              " 0.09799999743700027,\n",
              " 0.13220000267028809]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    }
  ]
}