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Code Issues 6 Releases Activity

Update notebooks to current Keras, pip deps

master
root 2022-05-29 16:06:43 -06:00
parent 6cdb5a304e
commit b70aadee1a
6 changed files with 49 additions and 73 deletions
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2
README.md
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@ -121,7 +121,7 @@ Install dependencies from Debian.
```
sudo apt update
sudo apt install curl jq python3-pip
sudo apt install curl jq python3-pip graphviz
```
## Install Python Packages

2
docs/README-voila.md
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@ -17,6 +17,8 @@ sudo su - wut
pip3 install --user --upgrade pip
# make sure new `pip3` at `~/.local/bin/pip3` is in front in `$PATH`.
echo 'PATH=~/.local/bin:$PATH' >> ~/.bashrc
```
logout #log back in as user wut
sudo su - wut
# Install Python packages for Voila

67
notebooks/wut-predict.ipynb
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@ -46,15 +46,8 @@
"metadata": {},
"outputs": [],
"source": [
"import os"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"import numpy as np"
]
},
@ -64,43 +57,18 @@
"metadata": {},
"outputs": [],
"source": [
"import tensorflow.python.keras"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow import keras\n",
"from tensorflow.keras import layers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.python.keras import Sequential\n",
"from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense\n",
"import keras\n",
"from keras import layers\n",
"from keras import Sequential\n",
"from keras.layers import Activation, Dropout, Flatten, Dense\n",
"from keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
"from tensorflow.python.keras import optimizers\n",
"from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
"from keras import optimizers\n",
"from keras.preprocessing import image\n",
"from tensorflow.python.keras.models import load_model\n",
"#from tensorflow.python.keras.preprocessing.image import load_img\n",
"#from tensorflow.python.keras.preprocessing.image import img_to_array"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D"
"from keras.models import load_model\n",
"#from keras.preprocessing.image import load_img\n",
"#from keras.preprocessing.image import img_to_array\n",
"from keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D"
]
},
{
@ -113,15 +81,8 @@
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn.decomposition import PCA"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA\n",
"\n",
"# Seaborn pip dependency\n",
"import seaborn as sns"
]

49
notebooks/wut-train.ipynb
View File

@ -25,19 +25,19 @@
"import os\n",
"import datetime\n",
"import numpy as np\n",
"import tensorflow.python.keras\n",
"from tensorflow.python.keras import Sequential\n",
"from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense\n",
"from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
"from tensorflow.python.keras import optimizers\n",
"from tensorflow.python.keras.preprocessing import image\n",
"from tensorflow.python.keras.models import load_model\n",
"from tensorflow.python.keras.preprocessing.image import load_img\n",
"from tensorflow.python.keras.preprocessing.image import img_to_array\n",
"from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D\n",
"from tensorflow.python.keras.models import Model\n",
"from tensorflow.python.keras.layers import Input, concatenate\n",
"import keras\n",
"from keras import Sequential\n",
"from keras.layers import Activation, Dropout, Flatten, Dense\n",
"from keras.preprocessing.image import ImageDataGenerator\n",
"from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
"from keras import optimizers\n",
"from keras.preprocessing import image\n",
"from keras.models import load_model\n",
"#from keras.preprocessing.image import load_img\n",
"#from keras.preprocessing.image import img_to_array\n",
"from keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D\n",
"from keras.models import Model\n",
"from keras.layers import Input, concatenate\n",
"# Visualization\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
@ -50,6 +50,8 @@
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets\n",
"# Display Images\n",
"\n",
"\n",
"from IPython.display import display, Image"
]
},
@ -219,7 +221,7 @@
"#log_dir=\"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
"#tensorboard_callback = tensorflow.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
"#tensorboard_callback = tensorflow.keras.callbacks.TensorBoard(log_dir=log_dir)\n",
"tensorboard_callback = tensorflow.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1, write_graph=True, write_images=True, embeddings_freq=1, update_freq='batch')"
"tensorboard_callback = keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1, write_graph=True, write_images=True, embeddings_freq=1, update_freq='batch')"
]
},
{
@ -248,10 +250,19 @@
"outputs": [],
"source": [
"#wutoptimizer = 'adam'\n",
"wutoptimizer = tensorflow.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=True)\n",
"wutoptimizer = keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=True)\n",
"\n",
"wutloss = 'binary_crossentropy'\n",
"#wutmetrics = 'accuracy'\n",
"wutmetrics = ['accuracy']\n",
"wutmetrics = ['accuracy']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.compile(optimizer=wutoptimizer,\n",
" loss=wutloss,\n",
" metrics=[wutmetrics])"
@ -354,7 +365,7 @@
"metadata": {},
"outputs": [],
"source": [
"model.save('/srv/satnogs/data/models/GMSK/wut-GMSK-201912.h5')"
"model.save('/srv/satnogs/data/models/GMSK/wut-GMSK-202205.h5')"
]
},
{
@ -363,7 +374,7 @@
"metadata": {},
"outputs": [],
"source": [
"model.save('/srv/satnogs/data/models/GMSK/wut-GMSK-201912.tf')"
"model.save('/srv/satnogs/data/models/GMSK/wut-GMSK-202205.tf')"
]
},
{
@ -372,7 +383,7 @@
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.utils import plot_model"
"from keras.utils import plot_model"
]
},
{

1
requirements-voila.txt
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@ -4,6 +4,7 @@ jupyterlab
matplotlib
pandas
pillow
pydot
seaborn
simplejson
sklearn

1
requirements.txt
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@ -3,6 +3,7 @@ ipywidgets
jupyterlab
matplotlib
pandas
pydot
seaborn
sklearn
tensorboard