Created using Colaboratory

keras_simple_tensorboard.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "keras_simple_tensorboard.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "authorship_tag": "ABX9TyP7jeNbiXxTpmnqHB7+xQdl",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/AmitNaikRepository/How-to-Deploy-a-Tensorflow-Model-in-Production/blob/master/keras_simple_tensorboard.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "h4ZK-NRymt0U",
+        "colab_type": "code",
+        "outputId": "5775d458-4989-4dd6-ce12-335be50ed133",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        }
+      },
+      "source": [
+        "#import liabraries \n",
+        "import pandas as pd \n",
+        "import numpy as np \n",
+        "import tensorflow as tf\n",
+        "from tensorflow import keras \n",
+        "from datetime import datetime \n",
+        "\n",
+        "from packaging import version \n",
+        "\n",
+        "\n",
+        "print(\"TensorFlow version: \", tf.__version__)\n",
+        "\n",
+        "\n",
+        "assert version.parse(tf.__version__).release[0] >= 2, \\\n",
+        "    \"This notebook requires TensorFlow 2.0 or above.\""
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow version:  2.2.0\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fjbhPHe4m8C-",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "#import datafile and split the dataset \n",
+        "data_size=1000\n",
+        "train_dict=0.8\n",
+        "\n",
+        "train_size=int(data_size * train_dict)\n",
+        "\n",
+        "x=np.linspace(-1,1,data_size)\n",
+        "np.random.shuffle(x)\n",
+        "\n",
+        "\n",
+        "\n",
+        "#generate teh output data \n",
+        "\n",
+        "y=0.5*x+2+np.random.normal(0,0.05,(data_size,))\n",
+        "\n",
+        "\n",
+        "#now split the test pair in the data \n",
+        "\n",
+        "x_train,y_train=x[:train_size],y[:train_size]\n",
+        "x_test,y_test=x[train_size:],y[train_size:]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lt1NBdTImFoM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 129
+        },
+        "outputId": "6cabb5be-a83a-4e43-9582-68b0c0edad6d"
+      },
+      "source": [
+        "#now import tqdm and intall the process \n",
+        "logdir=\"logs/scalars/\" + datetime.now().strftime(\"%Y%M%D-%H%M%S\")\n",
+        "tensorboard_callback=keras.callbacks.TensorBoard(log_dir=logdir)\n",
+        "\n",
+        "model=keras.models.Sequential([keras.layers.Dense(16,input_dim=1),\n",
+        "                               keras.layers.Dense(1)\n",
+        "                               \n",
+        "])\n",
+        "\n",
+        "model.compile(\n",
+        "    loss='mse',\n",
+        "    optimizer=keras.optimizers.SGD(lr=0.2),\n",
+        ")\n",
+        "\n",
+        "print(\"training with defualt parameters this takes less than 10 seconds\")\n",
+        "\n",
+        "training_history=model.fit(\n",
+        "    x_train,\n",
+        "    y_train,\n",
+        "    batch_size=train_size,\n",
+        "    verbose=0,\n",
+        "    epochs=100,\n",
+        "    validation_data(x_test,y_test),\n",
+        "    callbacks=[tensorboard_callback],\n",
+        "\n",
+        "\n",
+        ")\n",
+        "\n",
+        "print(\"average test loss \", np.average(training_history.history['loss']))"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "SyntaxError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-7-9752b521e83b>\"\u001b[0;36m, line \u001b[0;32m23\u001b[0m\n\u001b[0;31m    validation_data(x_test,y_test),\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Yf7YhURUmFlC",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "#get the data and the preprocess it "
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "A2Y_bNS9mFih",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QacRn8B1mFfB",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "U1wtDvoGmFc6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}