train_model.ipynb

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   "source": [
    "##### Copyright 2019 The TensorFlow Authors.\n",
    "##### Modified and provided for the binder project by Bartosz Telenczuk, 2020"
   ]
  },
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   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9GtR_cTTkf9G"
   },
   "source": [
    "Import TensorFlow, datetime, and os:"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "colab": {},
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   "source": [
    "import tensorflow as tf\n",
    "import datetime, os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Cu1fbH-S3oAX"
   },
   "source": [
    "## TensorBoard in notebooks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XfCa27_8kov6"
   },
   "source": [
    "Download the [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist) dataset and scale it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab_type": "code",
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   "outputs": [],
   "source": [
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "\n",
    "(x_train, y_train),(x_test, y_test) = fashion_mnist.load_data()\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lBk1BqAZKEKd"
   },
   "source": [
    "Create a very simple model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab": {},
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   "source": [
    "def create_model():\n",
    "  return tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(512, activation='relu'),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Dense(10, activation='softmax')\n",
    "  ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RNaPPs5ZKNOV"
   },
   "source": [
    "Train the model using Keras and the TensorBoard callback:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "lpUO9HqUKP6z",
    "outputId": "40acf9dc-919d-4dca-8f83-fd11ac53f45c"
   },
   "outputs": [],
   "source": [
    "def train_model():\n",
    "  \n",
    "  model = create_model()\n",
    "  model.compile(optimizer='adam',\n",
    "                loss='sparse_categorical_crossentropy',\n",
    "                metrics=['accuracy'])\n",
    "\n",
    "  logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
    "  tensorboard_callback = tf.keras.callbacks.TensorBoard(\n",
    "      logdir, histogram_freq=1, profile_batch=0)\n",
    "\n",
    "  model.fit(x=x_train, \n",
    "            y=y_train,\n",
    "            epochs=5, \n",
    "            validation_data=(x_test, y_test), \n",
    "            callbacks=[tensorboard_callback])\n",
    "\n",
    "train_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aQq3UHgmLBpC"
   },
   "source": [
    "You can now go to the [tensorboard interface](../proxy/6006/) to watch dashboards such as scalars, graphs, histograms, and others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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