Fix codes for 1.15

Author: healess

lab-05-1-logistic_regression-eager.ipynb

@@ -12,14 +12,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.12.0\n"
+      "1.15.0\n"
      ]
     }
    ],
@@ -28,7 +28,6 @@
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
     "import tensorflow as tf\n",
-    "import tensorflow.contrib.eager as tfe\n",
     "\n",
     "tf.enable_eager_execution()\n",
     "tf.set_random_seed(777)  # for reproducibility\n",
@@ -47,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "scrolled": true
    },
@@ -107,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -186,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -208,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -227,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -250,13 +249,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "WARNING:tensorflow:From <ipython-input-6-6afa75b867be>:2: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Deprecated in favor of operator or tf.math.divide.\n",
       "Iter: 0, Loss: 0.6874\n",
       "Iter: 100, Loss: 0.5776\n",
       "Iter: 200, Loss: 0.5349\n",
@@ -276,7 +278,7 @@
     "EPOCHS = 1001\n",
     "\n",
     "for step in range(EPOCHS):\n",
-    "    for features, labels  in tfe.Iterator(dataset):\n",
+    "    for features, labels  in iter(dataset):\n",
     "        grads = grad(logistic_regression(features), features, labels)\n",
     "        optimizer.apply_gradients(grads_and_vars=zip(grads,[W,b]))\n",
     "        if step % 100 == 0:\n",
@@ -309,7 +311,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,

lab-05-2-logistic_regression_diabetes-eager.ipynb

@@ -27,7 +27,6 @@
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
     "import tensorflow as tf\n",
-    "import tensorflow.contrib.eager as tfe\n",
     "\n",
     "tf.enable_eager_execution()\n",
     "tf.set_random_seed(777)  # for reproducibility\n",
@@ -249,7 +248,7 @@
     "EPOCHS = 1001\n",
     "\n",
     "for step in range(EPOCHS):\n",
-    "    for features, labels  in tfe.Iterator(dataset):\n",
+    "    for features, labels  in iter(dataset):\n",
     "        grads = grad(logistic_regression(features), features, labels)\n",
     "        optimizer.apply_gradients(grads_and_vars=zip(grads,[W,b]))\n",
     "        if step % 100 == 0:\n",
@@ -280,7 +279,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,

lab-07-1-learning_rate_and_evaluation-eager.ipynb

@@ -29,7 +29,6 @@
     "%matplotlib inline\n",
     "import tensorflow as tf\n",
     "from mpl_toolkits.mplot3d import Axes3D\n",
-    "import tensorflow.contrib.eager as tfe\n",
     "\n",
     "tf.enable_eager_execution()\n",
     "tf.set_random_seed(777)  # for reproducibility\n",
@@ -284,7 +283,7 @@
     "EPOCHS = 1001\n",
     "\n",
     "for step in range(EPOCHS):\n",
-    "    for features, labels  in tfe.Iterator(dataset):\n",
+    "    for features, labels  in iter(dataset):\n",
     "        features = tf.cast(features, tf.float32)\n",
     "        labels = tf.cast(labels, tf.float32)\n",
     "        grads = grad(softmax_fn(features), features, labels)\n",
@@ -321,7 +320,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,