" <a target=\"_blank\" href=\"https://www.tensorflow.org/guide/autograph\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/guide/autograph.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/models/blob/master/samples/core/guide/autograph.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
"</table>"
]
},
{
"metadata": {
"id": "CydFK2CL7ZHA",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"[AutoGraph](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/) helps you write complicated graph code using normal Python. Behind the scenes, AutoGraph automatically transforms your code into the equivalent [TensorFlow graph code](https://www.tensorflow.org/guide/graphs). AutoGraph already supports much of the Python language, and that coverage continues to grow. For a list of supported Python langauge features, see the [Autograph capabilities and limitations](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/LIMITATIONS.md)."
]
},
{
"metadata": {
"id": "n4EKOpw9mObL",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Setup\n",
"\n",
"To use AutoGraph, install the latest version of TensorFlow:"
]
},
{
"metadata": {
"id": "RSez0n7Ptcvb",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
},
"cell_type": "code",
"source": [
"! pip install tf-nightly"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "qLp9VZfit9oR",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Import TensorFlow, AutoGraph, and any supporting modules:"
"We'll enable [eager execution](https://www.tensorflow.org/guide/eager) for demonstration purposes, but AutoGraph works in both eager and [graph execution](https://www.tensorflow.org/guide/graphs) environments:"
]
},
{
"metadata": {
"id": "ks_hiqcSJNvg",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
},
"cell_type": "code",
"source": [
"tf.enable_eager_execution()"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "WR4lG3hsuWQT",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Note: AutoGraph converted code is designed to run during graph execution. When eager exectuon is enabled, use explicit graphs (as this example shows) or `tf.contrib.eager.defun`."
]
},
{
"metadata": {
"id": "ohbSnA79mcJV",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Automatically convert Python control flow\n",
"\n",
"AutoGraph will convert much of the Python language into the equivalent TensorFlow graph building code. It converts a function like:"
]
},
{
"metadata": {
"id": "aA3gOodCBkOw",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
},
"cell_type": "code",
"source": [
"def g(x):\n",
" if x > 0:\n",
" x = x * x\n",
" else:\n",
" x = 0.0\n",
" return x"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "LICw4XQFZrhH",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"To a function that uses graph building:"
]
},
{
"metadata": {
"id": "_EMhGUjRZoKQ",
"colab_type": "code",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
}
},
"cell_type": "code",
"source": [
"print(autograph.to_code(g))"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "xpK0m4TCvkJq",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Code written for eager execution can run in a `tf.Graph` with the same results, but with the benfits of graph execution:"
"AutoGraph supports common Python statements like `while`, `for`, `if`, `break`, and `return`, with support for nesting. Compare this function with the complicated graph verson displayed in the following code blocks:"
"## Advanced example: An in-graph training loop\n",
"\n",
"Since writing control flow in AutoGraph is easy, running a training loop in a TensorFlow graph should also be easy. \n",
"\n",
"<!--TODO(markdaoust) link to examples showing autograph **in** keras models when ready-->\n",
"\n",
"Important: While this example wraps a `tf.keras.Model` using AutoGraph, `tf.contrib.autograph` is compatible with `tf.keras` and can be used in [Keras custom layers and models](https://tensorflow.org/guide/keras#build_advanced_models). The easiest way is to `@autograph.convert()` the `call` method.\n",
"\n",
"This example shows how to train a simple Keras model on MNIST with the entire training process—loading batches, calculating gradients, updating parameters, calculating validation accuracy, and repeating until convergence—is performed in-graph."
