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Commit 7a1ee166 authored by b1jeong's avatar b1jeong
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split scripts

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kerasDeloyment.yaml
+ 9
− 9
View file @ 7a1ee166
......@@ -44,12 +44,12 @@ spec:
persistentVolumeClaim:
claimName: modeltraining
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: gpu-type
operator: In # Use NotIn for other types
values:
- M40
# affinity:
# nodeAffinity:
# requiredDuringSchedulingIgnoredDuringExecution:
# nodeSelectorTerms:
# - matchExpressions:
# - key: gpu-type
# operator: In # Use NotIn for other types
# values:
# - M40
wildfireDemo/README.md 0 → 100644
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View file @ 7a1ee166
to use this demo, go to the notebook provided and enter the correct pathway to the model (h5 file) and the image directory. Everything else should be provided.
\ No newline at end of file
wildfireDemo/utilitization.ipynb → wildfireDemo/model_apply.ipynb
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− 2
View file @ 7a1ee166
%% Cell type:code id: tags:
``` python
import tensorflow as tf
import os
from PIL import Image
import numpy as np
import cv2
import re
```
%% Cell type:code id: tags:
``` python
tf.test.is_built_with_cuda()
tf.test.is_gpu_available(cuda_only=False, min_cuda_compute_capability=None)
```
%% Output
True
%% Cell type:code id: tags:
``` python
test = np.array([1,2,3,4])
test/4
```
%% Output
array([0.25, 0.5 , 0.75, 1. ])
%% Cell type:code id: tags:
``` python
pathToModel = "/userdata/kerasData/firstSplitmodel.h5"
pathToImageDir = "/userdata/kerasData/images/setForLk"
pathToModel = "/firstSplitmodel.h5"
pathToImageDir = "/images/setForLk"
```
%% Cell type:code id: tags:
``` python
def loadData(datasetPath):
tempF = []
fireLabel = []
for element in os.listdir(datasetPath):
if re.search('\+', element):
fireLabel.append(1)
else:
fireLabel.append(0)
image = cv2.imread(datasetPath + "/"+ element)
image = cv2.resize(image, (128,128))
tempF.append(image)
labels = np.array(fireLabel)
labels = tf.keras.utils.to_categorical(labels, num_classes=2)
data = np.array(tempF)
data = np.true_divide(data, 255)
return data, labels
data, Label = loadData(pathToImageDir)
# cv2.imread("/userdata/kerasData/images/setForLk/1512676384_+02400.jpg")
```
%% Cell type:code id: tags:
``` python
model = tf.keras.models.load_model(pathToImageDirectory)
```
%% Cell type:code id: tags:
``` python
model.predict(data)
```
%% Output
array([[0.5107457 , 0.4892543 ],
[0.41123033, 0.5887697 ],
[0.43183708, 0.56816286],
[0.44897774, 0.55102223],
[0.47739947, 0.52260053],
[0.3578842 , 0.6421158 ],
[0.4941746 , 0.5058254 ],
[0.34056282, 0.6594372 ],
[0.35624158, 0.64375836],
[0.47470272, 0.5252973 ],
[0.4808885 , 0.5191116 ],
[0.431317 , 0.568683 ],
[0.5134212 , 0.4865788 ],
[0.40128127, 0.59871876],
[0.4639625 , 0.53603756],
[0.35375822, 0.64624184],
[0.36264202, 0.63735795],
[0.45890424, 0.5410958 ],
[0.33185333, 0.66814667],
[0.3523977 , 0.6476023 ],
[0.34115818, 0.6588418 ],
[0.48866385, 0.5113361 ],
[0.455409 , 0.544591 ],
[0.46895435, 0.5310457 ],
[0.38263088, 0.6173691 ],
[0.5233783 , 0.47662166],
[0.44299665, 0.5570033 ],
[0.47639135, 0.5236086 ],
[0.3761411 , 0.6238589 ],
[0.47419727, 0.52580273],
[0.40086877, 0.5991312 ],
[0.48351654, 0.5164834 ],
[0.43688735, 0.5631126 ],
[0.4520515 , 0.5479485 ],
[0.3890876 , 0.6109124 ],
[0.4929448 , 0.50705516],
[0.35062018, 0.64937985],
[0.46544933, 0.53455067],
[0.4987746 , 0.5012254 ],
[0.4678061 , 0.53219396],
[0.468355 , 0.53164506],
[0.37291136, 0.62708867],
[0.5106242 , 0.4893759 ],
[0.4318202 , 0.5681798 ],
[0.39998996, 0.60001004],
[0.40117145, 0.59882855],
[0.45087582, 0.5491242 ],
[0.3823646 , 0.6176354 ],
[0.3389846 , 0.6610154 ],
[0.36759573, 0.63240427],
[0.4042852 , 0.5957148 ],
[0.39802316, 0.6019768 ],
[0.34497583, 0.6550242 ],
[0.4933747 , 0.5066253 ],
[0.4834825 , 0.51651746],
[0.43642092, 0.5635791 ],
[0.47396407, 0.52603596],
[0.4887493 , 0.5112507 ],
[0.47656032, 0.52343965],
[0.46176285, 0.53823715],
[0.37929055, 0.6207095 ],
[0.49937135, 0.50062865],
[0.48522255, 0.5147774 ],
[0.45080933, 0.5491907 ],
[0.46133736, 0.5386627 ],
[0.39081547, 0.60918456],
[0.41144982, 0.58855015],
[0.52494085, 0.47505915],
[0.38156646, 0.61843354],
[0.36862728, 0.6313727 ],
[0.4544768 , 0.5455232 ],
[0.34168276, 0.6583172 ],
[0.37399516, 0.6260049 ],
[0.47957534, 0.52042466],
[0.50483143, 0.49516863],
[0.4877852 , 0.5122148 ],
[0.42307466, 0.57692534],
[0.51999307, 0.4800069 ],
[0.39840838, 0.60159165],
[0.4879313 , 0.5120687 ],
[0.4254754 , 0.57452464]], dtype=float32)
%% Cell type:code id: tags:
``` python
print(model.evaluate(x=data, y=Label))
print(model.metrics_names)
```
%% Output
81/1 [==============================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 0s 588us/sample - loss: 0.5996 - accuracy: 0.5926
[0.6112031642301583, 0.5925926]
['loss', 'accuracy']
%% Cell type:code id: tags:
``` python
```
......
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