Created using Colaboratory

samples/core/get_started/eager.ipynb

@@ -16,6 +16,16 @@
     }
   },
   "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "[View in Colaboratory](https://colab.research.google.com/github/MarkDaoust/models/blob/get_started/samples/core/get_started/eager.ipynb)"
+      ]
+    },
     {
       "metadata": {
         "id": "rwxGnsA92emp",
@@ -355,7 +365,7 @@
         "train_dataset = tf.contrib.data.make_csv_dataset(\n",
         "    train_dataset_fp, batch_size, \n",
         "    column_names=column_names,\n",
-        "    label_name='species',\n",
+        "    label_name=label_name,\n",
         "    num_epochs=1)"
       ],
       "execution_count": 0,
@@ -381,11 +391,11 @@
       },
       "cell_type": "code",
       "source": [
-        "features, labels = next(iter(train_dataset))\n",
-        "  \n",
-        "plt.scatter(features['petal_length'], features['petal_width'])\n",
-        "plt.xlabel(\"Petal Length\")\n",
-        "plt.ylabel(\"Petal Width\")\n"
+        "for features, labels in train_dataset.take(1):\n",
+        "  plt.scatter(features['petal_length'], features['sepal_length'], \n",
+        "              c=labels, cmap='viridis')\n",
+        "  plt.xlabel(\"Petal Length\")\n",
+        "  plt.ylabel(\"Sepal Length\")\n"
       ],
       "execution_count": 0,
       "outputs": []
@@ -400,7 +410,6 @@
         "To simplify the model building, let's repackage the features dictionary into an array with shape ``(batch_size,num_features)`.\n",
         "\n",
         "To do this we'll write a simple function using the [tf.stack](https://www.tensorflow.org/api_docs/python/tf/data/dataset/map) method to pack the features into a single array. Then we'll use the [tf.data.Dataset.map](https://www.tensorflow.org/api_docs/python/tf/data/dataset/map) method to apply this function to each `(features,label)` pair in the dataset. :\n"
-
       ]
     },
     {
@@ -412,8 +421,8 @@
       "cell_type": "code",
       "source": [
         "def pack_features_vector(features,labels):\n",
-        "  features = tf.stack([features[name] for name in feature_names],\n",
-        "                      axis=1)\n",
+        "  values = [value for value in features.values()]\n",
+        "  features = tf.stack(values, axis=1)\n",
         "  return features, labels\n",
         "  \n",
         "train_dataset = train_dataset.map(pack_features_vector)"
@@ -439,9 +448,8 @@
       },
       "cell_type": "code",
       "source": [
-        "features,labels = next(iter(train_dataset))\n",
-        "  \n",
-        "features[:5]"
+        "for features,labels in train_dataset.take(1):\n",
+        "  print(features[:5])"
       ],
       "execution_count": 0,
       "outputs": []
@@ -539,8 +547,8 @@
       },
       "cell_type": "code",
       "source": [
-        "prediction = model(features)\n",
-        "prediction[:5]"
+        "predictions = model(features)\n",
+        "predictions[:5]"
       ],
       "execution_count": 0,
       "outputs": []
@@ -554,7 +562,32 @@
       "source": [
         "For each example it returns a *[logit](https://developers.google.com/machine-learning/crash-course/glossary#logits)* score for each class. \n",
         "\n",
-        "You can convert logits to probabilities for each class using the [tf.nn.softmax](https://www.tensorflow.org/api_docs/python/tf/nn/softmax) function.\n",
+        "You can convert logits to probabilities for each class using the [tf.nn.softmax](https://www.tensorflow.org/api_docs/python/tf/nn/softmax) function."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "2fas18iHoiGB",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "prob = tf.nn.softmax(predictions[:5])\n",
+        "\n",
+        "prob"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "uRZmchElo481",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Taking the `tf.argmax` across the `classes` axis would give us the predicted class index.\n",
         "\n",
         "The model hasn't been trained yet, so these aren't very good predictions."
       ]
@@ -567,7 +600,20 @@
       },
       "cell_type": "code",
       "source": [
-        "tf.nn.softmax(prediction[:5])"
+        "tf.argmax(predictions, axis=1)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "8w3eDAp9o0G9",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "labels"
       ],
       "execution_count": 0,
       "outputs": []
@@ -660,7 +706,7 @@
         "def grad(model, inputs, targets):\n",
         "  with tf.GradientTape() as tape:\n",
         "    loss_value = loss(model, inputs, targets)\n",
-        "  return tape.gradient(loss_value, model.trainable_variables)"
+        "  return tape.gradient(loss_value, model.trainable_variables), loss_value"
       ],
       "execution_count": 0,
       "outputs": []
@@ -732,10 +778,11 @@
       },
       "cell_type": "code",
       "source": [
+        "grads, loss_value = grad(model, features, labels)\n",
+        "\n",
         "print(\"Step:        \", global_step.numpy())\n",
-        "print(\"Initial loss:\", loss(model, features, labels).numpy())\n",
+        "print(\"Initial loss:\", loss_value.numpy())\n",
         "\n",
-        "grads = grad(model, features, labels)\n",
         "optimizer.apply_gradients(zip(grads, model.variables), global_step)\n",
         "\n",
         "print()\n",
@@ -789,12 +836,12 @@
         "  # Training loop - using batches of 32\n",
         "  for x, y in train_dataset:\n",
         "    # Optimize the model\n",
-        "    grads = grad(model, x, y)\n",
+        "    grads, loss_value = grad(model, x, y)\n",
         "    optimizer.apply_gradients(zip(grads, model.variables),\n",
         "                              global_step)\n",
         "\n",
         "    # Track progress\n",
-        "    epoch_loss_avg(loss(model, x, y))  # add current batch loss\n",
+        "    epoch_loss_avg(loss_value)  # add current batch loss\n",
         "    # compare predicted label to actual label\n",
         "    epoch_accuracy(tf.argmax(model(x), axis=1, output_type=tf.int32), y)\n",
         "\n",