mods for ws24/24

- torch support
- backend K removed
- complex valued left inverse by SGD

.binder/requirements.txt

@@ -1,11 +1,14 @@
-matplotlib
+pytorch
+torchvision
+complextorch
+tensorflow
+keras-tuner
+scikit-learn
+statsmodels
 numpy
 scipy
+matplotlib
+ipympl
 librosa
 soundfile
-ipympl
-scikit-learn
-tensorflow
-keras-tuner
-statsmodels
 pyloudnorm

ci/requirements_env.txt

@@ -1,10 +1,14 @@
-ipympl
+pytorch
+torchvision
+complextorch
+tensorflow
 keras-tuner
-librosa
-matplotlib
-numpy
-pyloudnorm
-scipy
 scikit-learn
 statsmodels
-tensorflow
+numpy
+scipy
+matplotlib
+ipympl
+librosa
+soundfile
+pyloudnorm

exercise12_HyperParameterTuning.ipynb

@@ -64,7 +64,6 @@
     "from sklearn.preprocessing import OneHotEncoder, LabelBinarizer\n",
     "import tensorflow as tf\n",
     "from tensorflow import keras\n",
-    "from keras import backend as K\n",
     "import time\n",
     "\n",
     "print(\n",
@@ -190,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "8b7a07f0-7151-4914-93ca-6efb3c22779c",
    "metadata": {},
    "outputs": [],
@@ -202,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "e1eae32c-83b4-4b69-837f-31e3d87a470a",
    "metadata": {},
    "outputs": [],
@@ -326,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "e76a4f45-6f5b-4755-9f98-7ad228efdf97",
    "metadata": {},
    "outputs": [],

exercise12_MusicGenreClassification.ipynb

@@ -85,7 +85,6 @@
     "from sklearn.preprocessing import OneHotEncoder, LabelBinarizer\n",
     "import tensorflow as tf\n",
     "from tensorflow import keras\n",
-    "from keras import backend as K\n",
     "import time\n",
     "\n",
     "\n",
@@ -157,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "ed5b4e1e-c6fe-4363-80bc-919558d6e06b",
    "metadata": {},
    "outputs": [],
@@ -269,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "23424375-717e-4ca1-bcf7-9bd4a4fe8af8",
    "metadata": {},
    "outputs": [],
@@ -281,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "4a58498f-2445-4837-a9c0-95b940fd9086",
    "metadata": {},
    "outputs": [],
@@ -307,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "39305f54-e211-4564-853d-28dfc52119c1",
    "metadata": {},
    "outputs": [],
@@ -465,7 +464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "id": "49cb1e2f-da96-4b43-8490-f5b96d0c60cb",
    "metadata": {},
    "outputs": [],
@@ -527,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "id": "dbf6b6d1-dfb7-47ed-a723-967f2d1f52dc",
    "metadata": {},
    "outputs": [],
@@ -539,7 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "id": "dabce6d1-009d-470f-8407-788b7478ab70",
    "metadata": {},
    "outputs": [],
@@ -660,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "id": "4f27ff18-5dd4-42fa-86a1-60926c9b5993",
    "metadata": {},
    "outputs": [],

exercise13_CNN.py

@@ -10,7 +10,7 @@
 
 import numpy as np
 import tensorflow as tf
-import tensorflow.keras as keras
+from tensorflow import keras
 
 
 print("element-wise multiplication of matrix slices")