def model_tasks(self):
convnet_tasks = [
train.ValidateLogMelSpectrogramResNetv2(
data_files=[t.path for t in
self.input()['clean']['data'][:-2]],
label_files=[t.path for t in
self.input()['clean']['labels'][:-2]],
test_data=[t.path for t in
self.input()['clean']['data'][-2:-1]],
test_labels=[t.path for t in
self.input()['clean']['labels'][-2:-1]],
validation_data=[t.path for t in
self.input()['clean']['data'][-1:]],
validation_labels=[t.path for t in
self.input()['clean']['labels'][-1:]],
model_settings={'spectrogram_opts': PUB_SPECTROGRAM_OPTS,
'block_sizes': [5, 5, 5],
'block_strides': [1, 2, 2],
'filters': [32, 64, 128],
'kernel_sizes': [3, 3, 3],
'initial_learning_rate': 0.01,
'lr_decay_rate': 0.1,
'lr_decay_epochs': 20},
num_epochs=75,
batch_size=128,
dropout_rate=0.0,
percentage=0.6,
noise_volume=0.8,
)
]
return convnet_tasks
def model_tasks(self):
convnet_tasks = [
train.ValidateLogMelSpectrogramResNetv2(
data_files=[t.path for t in
self.input()['clean']['data'][:-1]],
label_files=[t.path for t in
self.input()['clean']['labels'][:-1]],
validation_data=[t.path for t in
self.input()['clean']['data'][-1:]],
validation_labels=[t.path for t in
self.input()['clean']['labels'][-1:]],
model_settings={'spectrogram_opts': PUB_SPECTROGRAM_OPTS,
'block_sizes': [3, 3, 3, 3, 3],
'block_strides': [1, 1, 1, 1, 1],
'filters': [16, 32, 64, 128, 256],
'kernel_sizes': [3, 3, 3, 3, 3],
'initial_learning_rate': 0.1},
batch_size=32,
num_epochs=50,
dropout_rate=0.2,
percentage=0.8,
noise_volume=0.1,
)
]
return convnet_tasks
def model_tasks(self):
convnet_tasks = [
train.ValidateLogMelSpectrogramResNetv2(
model_settings={'spectrogram_opts': PUB_SPECTROGRAM_OPTS,
'block_sizes': [3, 3, 3],
'block_strides': [1, 2, 2],
'filters': [16, 32, 64],
'kernel_sizes': [3, 3, 3],
'final_pool_type': 'no_pooling',
'initial_learning_rate': 0.001},
batch_size=128,
num_epochs=50,
dropout_rate=0.2,
percentage=0.8,
noise_volume=0.1,
**self.clean_file_params(),
)
]
return convnet_tasks
def model_tasks(self):
convnet_tasks = [
train.ValidateLogMelSpectrogramResNetv2(
data_files=[t.path for t in
self.input()['clean']['data'][:-1]],
label_files=[t.path for t in
self.input()['clean']['labels'][:-1]],
validation_data=[t.path for t in
self.input()['clean']['data'][-1:]],
validation_labels=[t.path for t in
self.input()['clean']['labels'][-1:]],
model_settings={'spectrogram_opts': PUB_SPECTROGRAM_OPTS,
'block_sizes': [3, 3, 3],
'block_strides': [1, 2, 2],
'filters': [16, 32, 64],
'kernel_sizes': [3, 3, 3],
'initial_learning_rate': 0.01},
num_epochs=50,
dropout_rate=dropout_rate
)
for dropout_rate in [0.20]
]
return convnet_tasks