diff --git a/notebooks/Block_0/Checking_Correct_Installation.ipynb b/notebooks/Block_0/Checking_Correct_Installation.ipynb
index da8803f9e6bb92d63f7199416e47fd5225435c21..7e8f12fa4799a07ff9292dad5e8392e44b28fb85 100644
--- a/notebooks/Block_0/Checking_Correct_Installation.ipynb
+++ b/notebooks/Block_0/Checking_Correct_Installation.ipynb
@@ -32,23 +32,23 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'2.1.0'"
+ "'2.4.1'"
]
},
- "execution_count": 2,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import tensorflow as tf\n",
- "tf.__version__ #Should work and give 2.1.0"
+ "tf.__version__ #Should work and give 2.4.1"
]
},
{
@@ -80,7 +80,7 @@
{
"data": {
"text/plain": [
- "<matplotlib.collections.PathCollection at 0x7fb7d492ebd0>"
+ "<matplotlib.collections.PathCollection at 0x7ff7a900c990>"
]
},
"execution_count": 4,
@@ -105,6 +105,13 @@
"%matplotlib inline\n",
"plt.scatter(range(100), np.sin(0.1 * np.array(range(100))))"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
diff --git a/notebooks/Block_0/Exercise Sheet - Basics Numpy.ipynb b/notebooks/Block_0/Exercise Sheet - Basics Numpy.ipynb
index ce2bb78b87ba4652d57442c3bbf0448f18a0d259..5531ef06fac284253dc86ce2d91048412fdc2e92 100644
--- a/notebooks/Block_0/Exercise Sheet - Basics Numpy.ipynb
+++ b/notebooks/Block_0/Exercise Sheet - Basics Numpy.ipynb
@@ -451,9 +451,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python [conda env:root]",
+ "display_name": "Python 3",
"language": "python",
- "name": "conda-root-py"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -465,7 +465,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.3"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/notebooks/Block_0/Solution - Basics Numpy.ipynb b/notebooks/Block_0/Solution - Basics Numpy.ipynb
index 1aa12bc3f0023ea3cb638a0d1c411759f2489081..c3aa33038665da7dda83520ad0f954e5b76780e4 100644
--- a/notebooks/Block_0/Solution - Basics Numpy.ipynb
+++ b/notebooks/Block_0/Solution - Basics Numpy.ipynb
@@ -162,7 +162,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -249,7 +249,7 @@
"Jun 25 21 23 27"
]
},
- "execution_count": 11,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -272,7 +272,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -310,7 +310,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -404,7 +404,7 @@
"Jun 25 21 23 27"
]
},
- "execution_count": 13,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -434,7 +434,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -468,7 +468,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -555,7 +555,7 @@
"Jun 25 21 23 27"
]
},
- "execution_count": 15,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -566,7 +566,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -653,7 +653,7 @@
"Jan 2 5 -3 4"
]
},
- "execution_count": 16,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -671,7 +671,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -1190,7 +1190,7 @@
"59 60 3690 19 Van"
]
},
- "execution_count": 18,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -1217,7 +1217,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -1296,7 +1296,7 @@
"5 6 2285 26 Small"
]
},
- "execution_count": 19,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -1308,7 +1308,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -1387,7 +1387,7 @@
"4 5 2440 32 Small"
]
},
- "execution_count": 20,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -1409,7 +1409,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -1443,7 +1443,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
diff --git a/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb b/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb
index 8e955cfc0890f6dd93460d1a5852abec7c7a5e30..e3f58f29162a80e614f21183e6830ee9377a1ca5 100644
--- a/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb
+++ b/notebooks/Block_5/Jupyter Notebook Block 5 - Object Detection and Segmentation.ipynb
@@ -31,20 +31,6 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Found 2 image links\n",
- "Saved 2 images\n",
- "Found 2 image links\n",
- "Saved 2 images\n",
- "Found 2 image links\n",
- "Saved 2 images\n",
- "Found 2 image links\n",
- "ERROR - Could not save https://upload.wikimedia.org/wikipedia/commons/thumb/b/bd/1990_Venice_Film_Festival_Robert_De_Niro.jpg/1200px-1990_Venice_Film_Festival_Robert_De_Niro.jpg - cannot identify image file <_io.BytesIO object at 0x7f4f951b6b30>\n",
- "Saved 1 images\n",
- "Found 2 image links\n",
- "Saved 2 images\n",
- "Found 2 image links\n",
- "ERROR - Could not save https://upload.wikimedia.org/wikipedia/commons/1/15/Sandra_Bullock_in_July_2013.jpg - cannot identify image file <_io.BytesIO object at 0x7f4f950e8410>\n",
- "Saved 1 images\n",
"Found 2 image links\n",
"Saved 2 images\n",
"Found 2 image links\n",
@@ -58,7 +44,8 @@
"from Image_crawling import Image_crawling\n",
"\n",
"# Specifiy the queries\n",
- "queries = [\"brad pitt\",\"johnny depp\", \"leonardo dicaprio\", \"robert de niro\", \"angelina jolie\", \"sandra bullock\", \"catherine deneuve\", \"marion cotillard\"]\n",
+ "#queries = [\"brad pitt\",\"johnny depp\", \"leonardo dicaprio\", \"robert de niro\", \"angelina jolie\", \"sandra bullock\", \"catherine deneuve\", \"marion cotillard\"]\n",
+ "queries = [\"Bart Simpson\",\"Homer Simpson\"]\n",
"limit = 2\n",
"download_folder = \"./brandnew_images/train/\"\n",
"waittime = 0.1 # Time to wait between actions, depends on the number of pictures you want to crawl. More pictures means you need to wait longer for them to load. \n",
@@ -255,13 +242,25 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 2,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "UuJV4JBKGhJO"
},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'Sequential' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-2-e37eef5858cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mnum_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mmodel_scratch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mmodel_scratch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mConv2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mmodel_scratch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mActivation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'Sequential' is not defined"
+ ]
+ }
+ ],
"source": [
"batch_size = 20\n",
"num_train_images = 480\n",