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Use best parameters as found by experimentation

This commit is contained in:
Mattéo Delabre 2019-12-18 09:41:50 -05:00
parent 5cb087d971
commit 0769a61fcf
Signed by: matteo
GPG Key ID: AE3FBD02DC583ABB
1 changed files with 8 additions and 5 deletions
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13
train.py
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@ -4,16 +4,19 @@ import torch
from matplotlib import pyplot from matplotlib import pyplot
from mpl_toolkits.axisartist.axislines import SubplotZero from mpl_toolkits.axisartist.axislines import SubplotZero
torch.manual_seed(42) torch.manual_seed(57)
# Number of symbols to learn # Number of symbols to learn
order = 16 order = 16
# Initial value for the learning rate
initial_learning_rate = 0.1
# Number of batches to skip between every loss report # Number of batches to skip between every loss report
loss_report_batch_skip = 50 loss_report_batch_skip = 50
# Size of batches # Size of batches
batch_size = 32 batch_size = 2048
# File in which the trained model is saved # File in which the trained model is saved
output_file = 'output/constellation-order-{}.pth'.format(order) output_file = 'output/constellation-order-{}.pth'.format(order)
@ -30,8 +33,8 @@ pyplot.show(block=False)
# Train the model with random data # Train the model with random data
model = constellation.ConstellationNet( model = constellation.ConstellationNet(
order=order, order=order,
encoder_layers_sizes=(8, 4), encoder_layers_sizes=(8, 4,),
decoder_layers_sizes=(4, 8), decoder_layers_sizes=(4, 8,),
channel_model=constellation.GaussianChannel() channel_model=constellation.GaussianChannel()
) )
@ -52,7 +55,7 @@ total_change = float('inf')
# Optimizer settings # Optimizer settings
criterion = torch.nn.CrossEntropyLoss() criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.1) optimizer = torch.optim.Adam(model.parameters(), lr=initial_learning_rate)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, verbose=True, optimizer, verbose=True,
factor=0.25, factor=0.25,