[samples/core/get_starter/eager]: Update with API simplifications in 1.8

samples/core/get_started/eager.ipynb

@@ -114,7 +114,7 @@
       "source": [
         "### Install the latest version of TensorFlow\n",
         "\n",
-        "This tutorial uses eager execution, which is available in [TensorFlow 1.7](https://www.tensorflow.org/install/). (You may need to restart the runtime after upgrading.)"
+        "This tutorial uses eager execution, which is available in [TensorFlow 1.8](https://www.tensorflow.org/install/). (You may need to restart the runtime after upgrading.)"
       ]
     },
     {
@@ -374,7 +374,7 @@
         "train_dataset = train_dataset.batch(32)\n",
         "\n",
         "# View a single example entry from a batch\n",
-        "features, label = tfe.Iterator(train_dataset).next()\n",
+        "features, label = iter(train_dataset).next()\n",
         "print(\"example features:\", features[0])\n",
         "print(\"example label:\", label[0])"
       ],
@@ -508,7 +508,7 @@
         "\n",
         "\n",
         "def grad(model, inputs, targets):\n",
-        "  with tfe.GradientTape() as tape:\n",
+        "  with tf.GradientTape() as tape:\n",
         "    loss_value = loss(model, inputs, targets)\n",
         "  return tape.gradient(loss_value, model.variables)"
       ],
@@ -522,7 +522,7 @@
       },
       "cell_type": "markdown",
       "source": [
-        "The `grad` function uses the `loss` function and the [tfe.GradientTape](https://www.tensorflow.org/api_docs/python/tf/contrib/eager/GradientTape) to record operations that compute the *[gradients](https://developers.google.com/machine-learning/crash-course/glossary#gradient)* used to optimize our model. For more examples of this, see the [eager execution guide](https://www.tensorflow.org/programmers_guide/eager)."
+        "The `grad` function uses the `loss` function and the [tf.GradientTape](https://www.tensorflow.org/api_docs/python/tf/GradientTape) to record operations that compute the *[gradients](https://developers.google.com/machine-learning/crash-course/glossary#gradient)* used to optimize our model. For more examples of this, see the [eager execution guide](https://www.tensorflow.org/programmers_guide/eager)."
       ]
     },
     {
@@ -614,7 +614,7 @@
         "  epoch_accuracy = tfe.metrics.Accuracy()\n",
         "\n",
         "  # Training loop - using batches of 32\n",
-        "  for x, y in tfe.Iterator(train_dataset):\n",
+        "  for x, y in train_dataset:\n",
         "    # Optimize the model\n",
         "    grads = grad(model, x, y)\n",
         "    optimizer.apply_gradients(zip(grads, model.variables),\n",
@@ -800,7 +800,7 @@
       "source": [
         "test_accuracy = tfe.metrics.Accuracy()\n",
         "\n",
-        "for (x, y) in tfe.Iterator(test_dataset):\n",
+        "for (x, y) in test_dataset:\n",
         "  prediction = tf.argmax(model(x), axis=1, output_type=tf.int32)\n",
         "  test_accuracy(prediction, y)\n",
         "\n",