Update "Customizing AdaNet" notebook to use tf.keras.layers.

PiperOrigin-RevId: 220364794
tensorflow · Nov 7, 2018 · 7df4b71 · 7df4b71
1 parent 5abce79
commit 7df4b71
Showing 1 changed file with 35 additions and 31 deletions.
diff --git a/adanet/examples/tutorials/customizing_adanet.ipynb b/adanet/examples/tutorials/customizing_adanet.ipynb
@@ -51,7 +51,9 @@
         "\n",
         "In this tutorial, we will explore the flexibility of the `adanet` framework, and\n",
         "create a custom search space for an image-classificatio dataset using high-level\n",
-        "TensorFlow libraries like `tf.layers`.\n",
+        "TensorFlow libraries like the\n",
+        "[`tf.keras.layers`](https://www.tensorflow.org/guide/keras#build_advanced_models)\n",
+        "functional API.\n",
         "\n"]
     },
     {
@@ -296,18 +298,18 @@
         },
         "colab_type": "code",
         "executionInfo": {
-          "elapsed": 32813,
+          "elapsed": 102633,
           "status": "ok",
-          "timestamp": 1534440488365,
+          "timestamp": 1541473803741,
           "user": {
             "displayName": "",
             "photoUrl": "",
             "userId": ""
           },
-          "user_tz": 240
+          "user_tz": 300
         },
         "id": "s8wJKsi06blX",
-        "outputId": "5acd325b-1de9-4c21-d825-8f9c10ffdd6c"
+        "outputId": "3e1cb4c2-2b03-40e2-ff82-e1d5bf249512"
       },
       "outputs": [
         {
@@ -370,18 +372,18 @@
         },
         "colab_type": "code",
         "executionInfo": {
-          "elapsed": 51048,
+          "elapsed": 146251,
           "status": "ok",
-          "timestamp": 1534440539502,
+          "timestamp": 1541473950057,
           "user": {
             "displayName": "",
             "photoUrl": "",
             "userId": ""
           },
-          "user_tz": 240
+          "user_tz": 300
         },
         "id": "9fAoRYd19eUs",
-        "outputId": "0b5678f1-4d44-430b-d84d-d3315366d4b8"
+        "outputId": "bace83c6-765a-4efb-8977-7d8b6dbf64e5"
       },
       "outputs": [
         {
@@ -513,21 +515,23 @@
         "    \"\"\"See `adanet.subnetwork.Builder`.\"\"\"\n",
         "    images = list(features.values())[0]\n",
         "    kernel_initializer = tf.keras.initializers.he_normal(seed=self._seed)\n",
-        "    x = tf.layers.conv2d(\n",
-        "        images,\n",
+        "    x = tf.keras.layers.Conv2D(\n",
         "        filters=16,\n",
         "        kernel_size=3,\n",
         "        padding=\"same\",\n",
         "        activation=\"relu\",\n",
-        "        kernel_initializer=kernel_initializer)\n",
-        "    x = tf.layers.max_pooling2d(x, pool_size=2, strides=2)\n",
-        "    x = tf.layers.flatten(x)\n",
-        "    x = tf.layers.dense(\n",
-        "        x, units=64, activation=\"relu\", kernel_initializer=kernel_initializer)\n",
+        "        kernel_initializer=kernel_initializer)(\n",
+        "            images)\n",
+        "    x = tf.keras.layers.MaxPool2D(pool_size=2, strides=2)(x)\n",
+        "    x = tf.keras.layers.Flatten()(x)\n",
+        "    x = tf.keras.layers.Dense(\n",
+        "        units=64, activation=\"relu\", kernel_initializer=kernel_initializer)(\n",
+        "            x)\n",
         "\n",
         "    # The `Head` passed to adanet.Estimator will apply the softmax activation.\n",
-        "    logits = tf.layers.dense(\n",
-        "        x, units=10, activation=None, kernel_initializer=kernel_initializer)\n",
+        "    logits = tf.keras.layers.Dense(\n",
+        "        units=10, activation=None, kernel_initializer=kernel_initializer)(\n",
+        "            x)\n",
         "\n",
         "    # Use a constant complexity measure, since all subnetworks have the same\n",
         "    # architecture and hyperparameters.\n",
@@ -539,13 +543,13 @@
         "        complexity=complexity,\n",
         "        persisted_tensors={})\n",
         "\n",
-        "  def build_subnetwork_train_op(self, \n",
-        "                                subnetwork, \n",
-        "                                loss, \n",
-        "                                var_list, \n",
-        "                                labels, \n",
+        "  def build_subnetwork_train_op(self,\n",
+        "                                subnetwork,\n",
+        "                                loss,\n",
+        "                                var_list,\n",
+        "                                labels,\n",
         "                                iteration_step,\n",
-        "                                summary, \n",
+        "                                summary,\n",
         "                                previous_ensemble=None):\n",
         "    \"\"\"See `adanet.subnetwork.Builder`.\"\"\"\n",
         "\n",
@@ -656,26 +660,26 @@
         },
         "colab_type": "code",
         "executionInfo": {
-          "elapsed": 74112,
+          "elapsed": 123749,
           "status": "ok",
-          "timestamp": 1534440614010,
+          "timestamp": 1541474074594,
           "user": {
             "displayName": "",
             "photoUrl": "",
             "userId": ""
           },
-          "user_tz": 240
+          "user_tz": 300
         },
         "id": "-Fhi1SjkzVBt",
-        "outputId": "fd4b8c9c-4665-473b-ea7d-cd47c8680811"
+        "outputId": "5f53d056-3164-42dc-e237-4b526d88803a"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Accuracy: 0.9041\n",
-            "Loss: 0.26544\n"
+            "Accuracy: 0.904\n",
+            "Loss: 0.266946\n"
           ]
         }
       ],
@@ -749,7 +753,7 @@
   "metadata": {
     "colab": {
       "collapsed_sections": [],
-      "name": "customizing_adanet.ipynb",
+            "name": "customizing_adanet.ipynb",
       "provenance": [],
       "version": "0.3.2"
     }