Workbench - Step 2
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Advanced Mode
Server-side Training
Design your own ML training algorithm here
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Model Settings and information
See and configure basic settings for the model here and proceed to customize the individual layers :
Number of dataset images:
0
Model Name:
Classes:
Train-Test Ratio:
Train-data Size:
Test-data Size:
Input Layer (Conv2D):
Resolution (px):
x
Kernel Size:
Filters:
Padding:
Strides:
Activation:
kernelInitializer:
RGB
Output Layer (Dense):
Activation:
kernelInitializer:
Next
Input Layer
Conv2D
inputShape:
kernelSize:
filters:
activation:
padding:
strides:
kernelInitializer:
+
Output Layer
Dense
activation:
units:
kernelInitializer:
Model.compile()
Select optimizer
adam
adadelta
adagrad
adamax
ftrl
nadam
rmsprop
sgd
loss:
metrics:
Model.fit()
batchSize:
epochs:
Shuffle
Train Model
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Model Training complete
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Download model json and binaries from here which might be used in the caMicroscope prediction tool.
After download you can exit the workbench or click on close to customize/train model again.
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