# model_path = 'thibault_model2/'
# model_name = 'cnn_gpu.model'
# size = 50
# model = nn.CNN_thibault()
# model_path = 'thibault_model3/'
# model_name = 'cnn_gpu.model'
# size = 50
# model = nn.CNN_thibault()
model_path = 'thibault_model5/'
model_name = 'cnn_gpu.model'
size = 50
model = nn.CNN_thibault2()
# model_path = 'thibault_model6/'
# model_name = 'cnn_gpu.model'
# size = 50
# model = nn.CNN_thibault()
serializers.load_npz(model_path + model_name, model)
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)
Xts, Yts = ld.load_test_dataset(test_N)
Ye = validation(size, model, Xts, Yts)
v.loss_visualizer(model_path)
plt.show()
trainer.extend(extensions.LogReport())
# Print selected entries of the log to stdout
# Here "main" refers to the target link of the "main" optimizer again, and
# "validation" refers to the default name of the Evaluator extension.
# Entries other than 'epoch' are reported by the Classifier link, called by
# either the updater or the evaluator.
trainer.extend(
extensions.PrintReport([
'epoch', 'main/loss', 'validation/main/loss', 'main/accuracy',
'validation/main/accuracy'
]))
# Print a progress bar to stdout
trainer.extend(extensions.ProgressBar())
start_time = time.time() #start time measurement
# Run the training
trainer.run()
execution_time = time.time() - start_time
print "execution time : " + str(execution_time)
print('saved the model')
serializers.save_npz('cnn.model', model)
print('saved the optimizer')
serializers.save_npz('cnn.state', optimizer)
v.loss_visualizer()
