Code example for XOR problem:
##first import tensorflow
import tensorflow as tf
##second import BrainMl
import BrainMl as dl
from BrainMl.layers import Dense,Dropout
from BrainMl.optimizer import Optimizer,Adam
##from BrainMl.activation import Activator,relu,softmax
##define an empty model
model=dl.Model()
##define Sequential model and add layers
save=model.Sequential([
##Here we add 4 dense layers and a Dropout
Dense(4,activation="relu",shape=[2,2]),
Dense(8, activation="relu"),
Dense(2, activation="relu"),
Dropout(0.9),
Dense(1, activation="relu")
],name="model1")
## X is our data
X = [[0,0],[0,1],[1,0],[1,1]]
## y is our label
Y = [[0],[1],[1],[0]]
##we define an optimizer
optim=Adam(learning_rate=0.01)
##and compile the model along with optimizer
model.compile(optimizer=optim)
##than we start trainning the model
model.fit(X,Y,epochs=100,display=["all"])
##all-displays current epoch and error
##in list you can add "epoch"-to show just epoch or "error"-just for the error
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Release files for BrainML 0.0.3
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| BrainML-0.0.3-py3-none-any.whl | Python 3 | none | any | Details |
Release files / BrainML-0.0.3-py3-none-any.whl
| Download URL | BrainML-0.0.3-py3-none-any.whl |
|---|---|
| Size | 9.0 kB |
| Tags | Python 3 |
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