Upload code_model.ipynb

code_model.ipynb

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+{
+  "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
+        }
+      ]
+    }
+  ]
+}