def main(_):
console.start('Task CNN (MEMBRANE)')
th = core.th
th.job_dir = './records_unet_alpha'
th.model = models.unet
th.suffix = '01'
th.batch_size = 2
th.learning_rate = 1e-4
th.epoch = 3
th.early_stop = True
th.patience = 5
th.print_cycle = 1
th.validation_per_round = 4
th.val_batch_size = 10
th.validate_train_set = True
th.export_tensors_upon_validation = True
# th.probe_cycle = 1
th.warm_up = False
th.save_model = True
th.overwrite = True
th.gather_note = True
th.summary = False
th.warm_up_thres = 0
#
th.train = False
th.mark = 'unet_{}'.format('x')
core.activate()
def main(_):
console.start('GPAT Classification task (MLP)')
# Configurations
th = core.th
th.model = models.mlp
th.num_blocks = 2
th.hidden_dim = 500
th.actype1 = 'relu'
th.idle_tol = 30
th.epoch = 500
th.learning_rate = 1e-3
th.batch_size = 64
th.validation_per_round = 1
th.print_cycle = 1
th.shuffle = True
# th.train = False
th.smart_train = False
th.max_bad_apples = 4
th.lr_decay = 0.6
th.save_model = True
th.overwrite = True
th.export_note = True
th.summary = True
th.monitor = False
description = 'demo'
th.mark = 'mlp_{}x{}{}'.format(th.hidden_dim, th.num_blocks, description)
core.activate()
def main(_):
console.start('MLP task')
# Configurations
th = core.th
th.model = models.mlp
th.fc_dims = [800, 500]
th.actype1 = 'relu'
th.epoch = 50
th.learning_rate = 1e-5
th.batch_size = 64
th.validation_per_round = 2
th.print_cycle = 20
th.shuffle = True
# th.train = False
th.smart_train = False
th.max_bad_apples = 4
th.lr_decay = 0.6
th.save_model = True
th.overwrite = True
th.export_note = True
th.summary = True
th.monitor = False
description = ''
th.mark = 'mlp_{}{}'.format(ms(th.fc_dims), description)
export_false = True
core.activate(export_false=export_false)
def main(_):
console.start('CNN task ')
# Configurations
th = core.th
# th.model = models.conv_test
th.model = models.conv_2d_test
th.actype1 = 'relu'
th.patience = 100
th.epoch = 5000
th.learning_rate = 1e-3
th.batch_size = 64
th.validation_per_round = 1
th.print_cycle = 10
th.shuffle = False
# th.train = False
th.smart_train = True
th.max_bad_apples = 4
th.lr_decay = 0.6
th.save_model = True
th.overwrite = True
th.export_note = True
th.summary = True
th.monitor = False
th.allow_growth = True
# th.gpu_memory_fraction =
description = 'conv_2d_add_noise'
th.mark = 'cnn_{}x{}{}'.format(th.hidden_dim, th.num_blocks, description)
core.activate()
def main(_):
console.start('LSTM task')
# Configurations
th = core.th
th.model = models.lstm_test
# th.model = models.lstm
th.num_blocks = 1
th.memory_depth = 3
th.hidden_dim = 100
th.epoch = 50000
th.learning_rate = 1e-4
th.batch_size = 512
th.num_steps = 100
th.val_preheat = 500
th.validation_per_round = 0
th.validate_cycle = 0
th.print_cycle = 2
th.train = True
th.smart_train = False
th.max_bad_apples = 4
th.lr_decay = 0.5
th.save_model = True
th.overwrite = True
th.export_note = True
th.summary = False
th.monitor = False
description = ''
th.mark = '{}x{}{}'.format(th.num_blocks, th.memory_depth, description)
core.activate()
def main(_):
console.start('WHBM task (MLP model)')
# Configurations
th = core.th
th.model = models.mlp
th.num_blocks = 2
th.memory_depth = 80
multiplier = 4
th.hidden_dim = th.memory_depth * multiplier
th.actype1 = 'relu'
th.epoch = 50000
th.learning_rate = 1e-4
th.batch_size = 64
th.validation_per_round = 5
th.print_cycle = 50
th.shuffle = True
# th.train = False
th.smart_train = True
th.max_bad_apples = 4
th.lr_decay = 0.5
th.save_model = True
th.overwrite = True
th.export_note = True
th.summary = True
th.monitor = False
description = ''
th.mark = '{}x[{}x{}]{}'.format(th.num_blocks, th.memory_depth, multiplier,
description)
core.activate()
def main(_):
console.start('basis task')
# Configurations
th = core.th
id = 11
th.job_dir = 'basis_task'
th.model = models.multinput_ver_only
th.actype1 = 'relu'
th.epoch = 5000
th.learning_rate = 1e-3
th.batch_size = 32
th.validation_per_round = 1
th.val_batch_size = th.batch_size
th.print_cycle = 20
th.patience = 100
th.shuffle = True
# th.train = False
th.smart_train = False
th.max_bad_apples = 4
th.lr_decay = 0.6
th.rand_over_classes = False
th.save_model = True
th.overwrite = True
th.export_note = True
th.summary = True
th.monitor = False
th.allow_growth = False
th.gpu_memory_fraction = 0.3
th.raw_keep_prob = 0.9
th.mfcc_keep_prob = 0.7
th.concat_keep_prob = 0.9
th.fold = 1
# th.shuffle = False
th.rand_pos = True
th.test_all = True
th.val_on_train_set = False
th.visible_gpu_id = '1'
# description = 'raw_data_mfcc_dropout_{}_random_{}_fold_{}'.format(
# th.mfcc_keep_prob, th.concat_keep_prob, th.fold)
# description = 'raw_data_mfcc_dropout_{}_{}'.format(th.mfcc_keep_prob,
# th.concat_keep_prob)
description = 'raw_data_mfcc_model_{}'.format(id)
# description = 'raw_data_mfcc_simlified_dropout_0.7_reg_0.2_sap_all'
th.mark = 'cnn_{}'.format(description)
export_false = True
core.activate()
def main(_):
console.start('Multinput task')
# Configurations
th = core.th
th.job_dir = 'res_task'
th.model = models.res_00
th.actype1 = 'relu'
th.epoch = 5000
th.learning_rate = 1e-3
th.batch_size = 32
th.validation_per_round = 1
th.val_batch_size = th.batch_size
th.print_cycle = 20
th.patience = 100
th.shuffle = True
# th.train = False
th.smart_train = False
th.max_bad_apples = 4
th.lr_decay = 0.6
th.rand_over_classes = False
th.save_model = True
th.overwrite = True
th.export_note = True
th.summary = True
th.monitor = False
th.allow_growth = False
th.gpu_memory_fraction = 0.4
th.raw_keep_prob = 0.9
th.mfcc_keep_prob = 0.7
th.concat_keep_prob = 0.9
th.fold = 0
# th.shuffle = False
th.rand_pos = True
th.visible_gpu_id = '0'
# description = 'cnn_raw_data_mfcc_random_rand'
description = 'raw_data_mfcc_dropout_{}_{}'.format(th.mfcc_keep_prob,
th.concat_keep_prob)
th.mark = 'cnn_{}'.format(description)
export_false = True
core.activate()
def main(_):
console.start('LSTM task on GPAT')
core.train_size = 50
core.val_size = 4
core.batches_per_epoch = 1000
# Configurations
th = core.th
th.job_dir = core.from_gpat('lstm_task')
th.model = models.lstm0
th.input_shape = [1000]
input_dim = th.input_shape[0]
# th.rc_dims = [1000, 41]
th.rc_dims = [500]
th.fc_dims = []
th.epoch = 1000
th.learning_rate = 0.1
th.batch_size = 20
th.num_steps = 4
th.validate_cycle = 200
# th.validation_per_round = 10
th.print_cycle = 1
th.notify_when_reset = True
th.early_stop = True
th.idle_tol = 50
# th.train = False
# th.overwrite = True
th.save_model = True
th.export_note = True
th.summary = True
th.monitor = False
description = '_t{}v{}lr{}'.format(core.train_size, core.val_size,
th.learning_rate)
th.mark = 'i{}_rc({})_s{}_bs{}'.format(input_dim, ms(th.rc_dims),
th.num_steps, th.batch_size)
if len(th.fc_dims) > 0: th.mark += '_fc({})'.format(ms(th.fc_dims))
th.mark += description
core.activate()
def main(_):
console.start('FC-LSTM task on GPAT')
core.train_size = 8
core.val_size = 2
# Configurations
th = core.th
th.job_dir = core.from_gpat('fc_records')
th.model = models.fc_lstm
th.input_shape = [2000]
input_dim = th.input_shape[0]
th.fc_dims = [1000]
th.rc_dims = [41]
th.epoch = 1000
th.learning_rate = 0.1
th.batch_size = 1
th.num_steps = 10
# th.validate_cycle = 100
th.validation_per_round = 2
th.print_cycle = 1
th.notify_when_reset = True
th.early_stop = True
th.idle_tol = 50
# th.train = False
th.overwrite = True
th.save_model = True
th.export_note = True
th.summary = True
th.monitor = False
description = '_t8v2_0'
th.mark = 'i{}_fc({})_rc({})'.format(
input_dim, ms(th.fc_dims), ms(th.rc_dims))
th.mark += description
core.activate()