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MarkDaoustMarkDaoust
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Fix review comments - part 2
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‎samples/core/tutorials/estimators/wide.ipynb

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@@ -122,6 +122,19 @@
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" add the root directory to your python path, and jump to the `wide_deep` directory:"
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]
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},
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{
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"metadata": {
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"id": "tTwQzWcn8aBu",
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"colab_type": "code",
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"colab": {}
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},
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"cell_type": "code",
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"source": [
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"! git clone --depth 1 https://github.com/tensorflow/models"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"metadata": {
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"id": "yVvFyhnkcYvL",
@@ -130,12 +143,9 @@
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},
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"cell_type": "code",
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"source": [
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"if \"wide_deep\" not in os.getcwd():\n",
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" ! git clone --depth 1 https://github.com/tensorflow/models\n",
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" models_path = os.path.join(os.getcwd(), 'models')\n",
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" sys.path.append(models_path) \n",
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" os.environ['PYTHONPATH'] += os.pathsep+models_path\n",
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" os.chdir(\"models/official/wide_deep\")"
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"models_path = os.path.join(os.getcwd(), 'models')\n",
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"\n",
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"sys.path.append(models_path)"
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],
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"execution_count": 0,
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"outputs": []
@@ -158,8 +168,8 @@
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},
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"cell_type": "code",
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"source": [
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"import census_dataset\n",
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"import census_main\n",
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"from official.wide_deep import census_dataset\n",
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"from official.wide_deep import census_main\n",
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"\n",
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"census_dataset.download(\"/tmp/census_data/\")"
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],
@@ -173,19 +183,65 @@
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},
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"cell_type": "markdown",
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"source": [
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"Execute the tutorial code with the following command to train the model described in this tutorial, from the command line:"
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"To execute the tutorial code from the command line first add the path to tensorflow/models to your `PYTHONPATH`."
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]
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},
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{
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"metadata": {
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"id": "DYOkY8boUptJ",
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"colab_type": "code",
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"colab": {}
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},
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"cell_type": "code",
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"source": [
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"#export PYTHONPATH=${PYTHONPATH}:\"$(pwd)/models\"\n",
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"os.environ['PYTHONPATH'] += os.pathsep+models_path"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"metadata": {
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"id": "5r0V9YUMUyoh",
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"colab_type": "text"
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},
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"cell_type": "markdown",
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"source": [
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"Use `--help` to see what command line options are available: "
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]
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},
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{
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"metadata": {
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"id": "vbJ8jPAhcYvT",
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"id": "1_3tBaLW4YM4",
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"colab_type": "code",
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"colab": {}
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},
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"cell_type": "code",
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"source": [
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"output = !python -m census_main --model_type=wide --train_epochs=2\n",
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"print([line for line in output if 'accuracy:' in line])"
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"!python -m official.wide_deep.census_main --help"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"metadata": {
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"id": "RrMLazEN6DMj",
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"colab_type": "text"
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},
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"cell_type": "markdown",
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"source": [
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"Now run the model:\n"
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]
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},
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{
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"metadata": {
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"id": "py7MarZl5Yh6",
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"colab_type": "code",
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"colab": {}
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},
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"cell_type": "code",
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"source": [
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"!python -m official.wide_deep.census_main --model_type=wide --train_epochs=2"
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],
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"execution_count": 0,
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"outputs": []
@@ -322,8 +378,7 @@
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"cell_type": "code",
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"source": [
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"def easy_input_function(df, label_key, num_epochs, shuffle, batch_size):\n",
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" df = df.copy()\n",
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" label = df.pop(label_key)\n",
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" label = df[label_key]\n",
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" ds = tf.data.Dataset.from_tensor_slices((dict(df),label))\n",
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"\n",
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" if shuffle:\n",
@@ -374,9 +429,9 @@
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"cell_type": "markdown",
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"source": [
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"But this approach has severly-limited scalability. For larger data it should be streamed off disk.\n",
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"the `census_dataset.input_fn` provides an example of how to do this using `tf.decode_csv` and `tf.data.TextLineDataset`: \n",
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"The `census_dataset.input_fn` provides an example of how to do this using `tf.decode_csv` and `tf.data.TextLineDataset`: \n",
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"\n",
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"TODO(markdaoust): This `input_fn` should use `tf.contrib.data.make_csv_dataset`"
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"<!-- TODO(markdaoust): This `input_fn` should use `tf.contrib.data.make_csv_dataset` -->"
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]
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},
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{
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"\n",
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"Estimators use a system called `feature_columns` to describe how the model\n",
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"should interpret each of the raw input features. An Estimator exepcts a vector\n",
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"of numeric inputs, and feature columns describe how the model shoukld convert\n",
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"of numeric inputs, and feature columns describe how the model should convert\n",
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"each feature.\n",
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"\n",
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"Selecting and crafting the right set of feature columns is key to learning an\n",
@@ -752,7 +807,7 @@
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},
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"cell_type": "markdown",
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"source": [
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"if we run `input_layer` with the hashed column we see that the output shape is `(batch_size, hash_bucket_size)`"
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"If we run `input_layer` with the hashed column we see that the output shape is `(batch_size, hash_bucket_size)`"
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]
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},
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{
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"\n",
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"For more about estimators:\n",
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"\n",
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"- The [TensorFlow Hub transfer-learning tutorial](https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub)\n",
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"- The [TensorFlow Hub text classification tutorial](https://www.tensorflow.org/hub/tutorials/text_classification_with_tf_hub) uses `hub.text_embedding_column` to easily ingest free form text. \n",
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"- The [Gradient-boosted-trees estimator tutorial](https://github.com/tensorflow/models/tree/master/official/boosted_trees)\n",
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"- This [blog post]( https://medium.com/tensorflow/classifying-text-with-tensorflow-estimators) on processing text with `Estimators`\n",
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"- How to [build a custom CNN estimator](https://www.tensorflow.org/tutorials/estimators/cnn)"
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]
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},
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{
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"metadata": {
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"id": "amMnupRPVtsa",
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"colab_type": "code",
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"colab": {}
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},
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"cell_type": "code",
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"source": [
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""
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],
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"execution_count": 0,
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"outputs": []
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}
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]
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}

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