Delete code_model.ipynb

code_model.ipynb

@@ -1,193 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "kLutYXp-ecSf"
-      },
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "import matplotlib.pyplot as plt\n",
-        "from tensorflow.keras.datasets import mnist\n",
-        "from tensorflow import keras\n",
-        "import keras.backend as K\n",
-        "from tensorflow.keras.layers import Dense, Flatten, Reshape, Input, Lambda, BatchNormalization, Dropout\n",
-        "\n",
-        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-        "\n",
-        "x_train = x_train / 255\n",
-        "x_test = x_test/ 255\n",
-        "\n",
-        "y_train = y_train % 2\n",
-        "y_train = keras.utils.to_categorical(y_train, 10)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "input_img = Input((28, 28))\n",
-        "x = Flatten()(input_img)\n",
-        "x = Dense(128, activation = 'relu')(x)\n",
-        "x = Dense(256, activation = 'relu')(x)\n",
-        "x = Dense(64, activation = 'relu')(x)\n",
-        "classif = Dense(10, activation = 'softmax')(x)"
-      ],
-      "metadata": {
-        "id": "Ffd2RsvUedfQ"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model = keras.Model(input_img, classif)"
-      ],
-      "metadata": {
-        "id": "5aVLXHYNe5R_"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy')"
-      ],
-      "metadata": {
-        "id": "tG0HHttBVuxs"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model.fit(x_train, y_train, epochs = 10, batch_size = 30, shuffle = True)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "L6tEkyZdWIZy",
-        "outputId": "2a98272e-fb00-440a-e4f6-10c92a477318"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Epoch 1/10\n",
-            "2000/2000 [==============================] - 12s 5ms/step - loss: 0.1153\n",
-            "Epoch 2/10\n",
-            "2000/2000 [==============================] - 11s 5ms/step - loss: 0.0524\n",
-            "Epoch 3/10\n",
-            "2000/2000 [==============================] - 9s 5ms/step - loss: 0.0384\n",
-            "Epoch 4/10\n",
-            "2000/2000 [==============================] - 11s 6ms/step - loss: 0.0308\n",
-            "Epoch 5/10\n",
-            "2000/2000 [==============================] - 11s 6ms/step - loss: 0.0250\n",
-            "Epoch 6/10\n",
-            "2000/2000 [==============================] - 11s 5ms/step - loss: 0.0199\n",
-            "Epoch 7/10\n",
-            "2000/2000 [==============================] - 10s 5ms/step - loss: 0.0168\n",
-            "Epoch 8/10\n",
-            "2000/2000 [==============================] - 11s 6ms/step - loss: 0.0142\n",
-            "Epoch 9/10\n",
-            "2000/2000 [==============================] - 11s 6ms/step - loss: 0.0131\n",
-            "Epoch 10/10\n",
-            "2000/2000 [==============================] - 9s 5ms/step - loss: 0.0110\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<keras.callbacks.History at 0x7f7234824ee0>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 16
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "model.predict(x_train[:1])"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "WGei66Vbdtzk",
-        "outputId": "673fe3fb-8363-427e-c753-27e6471aaf51"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "1/1 [==============================] - 0s 100ms/step\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[1.21183645e-11, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-              "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
-              "        0.00000000e+00, 0.00000000e+00]], dtype=float32)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 18
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "y_train[:1]"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "YkhzAnVeePCm",
-        "outputId": "c3c041c1-1ef4-441f-abb6-a771632a3617"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "array([[0., 1., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 19
-        }
-      ]
-    }
-  ]
-}