Updates for tensorflow 2

keras_utils.py

@@ -2,10 +2,10 @@
 # -*- coding: utf-8 -*-
 from collections import defaultdict
 import numpy as np
-from keras.models import save_model
-import tensorflow as tf
-import keras
-from keras import backend as K
+import tensorflow.compat.v1 as tf
+from tensorflow.compat.v1 import keras
+from tensorflow.compat.v1.keras import backend as K
+from tensorflow.compat.v1.keras.models import save_model
 import tqdm_utils
 
 

week2/preprocessed_mnist.py

@@ -1,4 +1,4 @@
-import keras
+from tensorflow import keras
 
 
 def load_dataset(flatten=False):

week2/v2/digits_classification.ipynb

@@ -1,15 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set tf 1.x for colab\n",
-    "%tensorflow_version 1.x"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -38,7 +28,7 @@
     "from sklearn.metrics import accuracy_score\n",
     "from matplotlib import pyplot as plt\n",
     "%matplotlib inline\n",
-    "import tensorflow as tf\n",
+    "import tensorflow.compat.v1 as tf\n",
     "print(\"We're using TF\", tf.__version__)\n",
     "\n",
     "import sys\n",
@@ -176,7 +166,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import keras\n",
+    "from tensorflow import keras\n",
     "\n",
     "y_train_oh = keras.utils.to_categorical(y_train, 10)\n",
     "y_val_oh = keras.utils.to_categorical(y_val, 10)\n",
@@ -192,6 +182,7 @@
    "outputs": [],
    "source": [
     "# run this again if you remake your graph\n",
+    "tf.disable_eager_execution()\n",
     "s = reset_tf_session()"
    ]
   },
@@ -398,4 +389,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}

week2/v2/grading_utils.py

@@ -2,12 +2,8 @@
 # -*- coding: utf-8 -*-
 
 
-def get_tensor_shape(t):
-    return [d.value for d in t.shape]
-
-
 def get_tensors_shapes_string(tensors):
     res = []
     for t in tensors:
-        res.extend([str(v) for v in get_tensor_shape(t)])
+        res.extend([str(v) for v in t.shape])
     return " ".join(res)

week2/v2/intro_to_tensorflow.ipynb

@@ -1,15 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set tf 1.x for colab\n",
-    "%tensorflow_version 1.x"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -48,10 +38,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import tensorflow as tf\n",
+    "import tensorflow.compat.v1 as tf\n",
     "import sys\n",
     "sys.path.append(\"../..\")\n",
     "from keras_utils import reset_tf_session\n",
+    "tf.disable_eager_execution()\n",
     "s = reset_tf_session()\n",
     "print(\"We're using TF\", tf.__version__)"
    ]
@@ -581,4 +572,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}

week2/v2/mnist_with_keras.ipynb

@@ -1,15 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set tf 1.x for colab\n",
-    "%tensorflow_version 1.x"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -46,13 +36,12 @@
     "%matplotlib inline\n",
     "import tensorflow as tf\n",
     "print(\"We're using TF\", tf.__version__)\n",
-    "import keras\n",
+    "from tensorflow import keras\n",
     "print(\"We are using Keras\", keras.__version__)\n",
     "\n",
     "import sys\n",
     "sys.path.append(\"../..\")\n",
-    "import keras_utils\n",
-    "from keras_utils import reset_tf_session"
+    "import keras_utils"
    ]
   },
   {
@@ -127,11 +116,11 @@
    "outputs": [],
    "source": [
     "# building a model with keras\n",
-    "from keras.layers import Dense, Activation\n",
-    "from keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense, Activation\n",
+    "from tensorflow.keras.models import Sequential\n",
     "\n",
     "# we still need to clear a graph though\n",
-    "s = reset_tf_session()\n",
+    "K.clear_session()\n",
     "\n",
     "model = Sequential()  # it is a feed-forward network without loops like in RNN\n",
     "model.add(Dense(256, input_shape=(784,)))  # the first layer must specify the input shape (replacing placeholders)\n",
@@ -259,4 +248,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}

week2/v2/preprocessed_mnist.py

@@ -1,4 +1,4 @@
-import keras
+from tensorflow import keras
 
 
 def load_dataset(flatten=False):