here is my code
model = Sequential()
model.add(LSTM(128, input_shape=(None, 1),return_sequences=True))
model.add(Dropout(0.3))
#I want test 32,64,128,256,512,1024 number of entering the layer
model.add(LSTM(128))
model.add(Dropout(0.3))
#I want test 32,64,128,256,512,1024 number of entering the layer
model.add(Dense(128))
model.add(Dropout(0.3))
#I want test 32,64,128,256,512,1024 number of entering the layer
#and if possible, I want to add more layer using for loop like below
for i in [LSTM, Dense]
model.add(i,(j))
model.add(Dense(1))
I want to tuning the numbers to LSTM and Dense.
I want to use the for loop to test for the numbers in the code in my comments.
I wonder how it can be implemented.
and I wonder if there is a tool that can tune the parameters like this.
Your valuable opinions and thoughts will be very much appreciated.
You can build a list with all possible configuration for each parameter in your model you want to tune. Something like this:
all_configurations = [
(32, 64, 128, 256, 512, 1024), # Number of output for the 1st layer
(32, 64, 128, 256, 512, 1024), # Outputs for the 2nd layer
(32,64,128,256,512,1024) # Outputs for the 3th layer
]
Now you can do:
from itertools import product
def test_nn(a, b, c):
# a is the number of outputs for 1st layer, b for the 2nd and c for 3th
# Build network with those parameters and test it
# TODO
pass
for configuration in product(all_configurations):
test_nn(*configuration)
For each possible configuration of your three hyperparameters, test_nn
will be called. Build and test your network inside that function
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