def main(_):
console.start('{} on CIFAR-10 task'.format(model_name.upper()))
th = core.th
# ---------------------------------------------------------------------------
# 0. date set setup
# ---------------------------------------------------------------------------
# ---------------------------------------------------------------------------
# 1. folder/file names and device
# ---------------------------------------------------------------------------
th.job_dir += '/{:02d}_{}'.format(id, model_name)
summ_name = model_name
th.prefix = '{}_'.format(date_string())
th.suffix = '_t00'
th.visible_gpu_id = 0
# ---------------------------------------------------------------------------
# 2. model setup
# ---------------------------------------------------------------------------
th.model = model
th.centralize_data = True
th.num_layers = 50
th.layer_width = 100
th.spatial_activation = 'tanh'
th.bias_initializer = -5.
# ---------------------------------------------------------------------------
# 3. trainer setup
# ---------------------------------------------------------------------------
th.epoch = 200
th.batch_size = 128
th.validation_per_round = 1
th.optimizer = tf.train.AdamOptimizer
th.learning_rate = 0.0004
th.patience = 5
th.early_stop = False
th.validate_train_set = True
th.val_decimals = 6
# ---------------------------------------------------------------------------
# 4. summary and note setup
th.export_tensors_upon_validation = True
# th.export_gates = True
th.train = True
th.save_model = True
th.overwrite = True
# ---------------------------------------------------------------------------
# 5. other stuff and activate
# ---------------------------------------------------------------------------
th.mark = '{}({}x{}-{})'.format(
model_name, th.layer_width, th.num_layers, th.spatial_activation)
th.gather_summ_name = th.prefix + summ_name + th.suffix + '.sum'
core.activate()
def main(_):
console.start('{} on CIFAR-10 task'.format(model_name.upper()))
th = core.th
# ---------------------------------------------------------------------------
# 0. date set setup
# ---------------------------------------------------------------------------
# ---------------------------------------------------------------------------
# 1. folder/file names and device
# ---------------------------------------------------------------------------
th.job_dir += '/{:02d}_{}'.format(id, model_name)
summ_name = model_name
prefix = '{}_'.format(date_string())
suffix = ''
th.visible_gpu_id = 0
# ---------------------------------------------------------------------------
# 2. model setup
# ---------------------------------------------------------------------------
th.model = model
th.dropout = 0.2
# ---------------------------------------------------------------------------
# 3. trainer setup
# ---------------------------------------------------------------------------
th.epoch = 1000
th.batch_size = 64
th.validation_per_round = 5
th.optimizer = tf.train.AdamOptimizer
th.learning_rate = 0.001
th.patience = 5
# ---------------------------------------------------------------------------
# 4. summary and note setup
# ---------------------------------------------------------------------------
th.train = True
th.save_model = True
th.overwrite = True
# ---------------------------------------------------------------------------
# 5. other stuff and activate
# ---------------------------------------------------------------------------
tail = suffix
th.mark = prefix + '{}({}){}'.format(model_name, th.num_layers, tail)
th.gather_summ_name = prefix + summ_name + tail + '.sum'
core.activate(True)
def main(_):
console.start('{} on CIFAR-10 task'.format(model_name.upper()))
th = core.th
# ---------------------------------------------------------------------------
# 0. date set setup
# ---------------------------------------------------------------------------
# ---------------------------------------------------------------------------
# 1. folder/file names and device
# ---------------------------------------------------------------------------
th.job_dir += '/{:02d}_{}'.format(id, model_name)
summ_name = model_name
th.prefix = '{}_'.format(date_string())
th.visible_gpu_id = 1
# ---------------------------------------------------------------------------
# 2. model setup
# ---------------------------------------------------------------------------
th.model = model
th.spatial_activation = 'relu'
th.developer_code = '1024-512'
th.fc_dims = [int(s) for s in th.developer_code.split('-')]
th.use_batchnorm = False
th.dropout = 0.0
th.centralize_data = True
# ---------------------------------------------------------------------------
# 3. trainer setup
# ---------------------------------------------------------------------------
th.epoch = 1
th.batch_size = 64
th.validation_per_round = 5
th.optimizer = tf.train.AdamOptimizer
th.learning_rate = 0.001
th.patience = 5
th.lives = 1
th.lr_decay = 0.6
th.clip_threshold = 10.0
th.reset_optimizer_after_resurrection = False
th.summary = True
# ---------------------------------------------------------------------------
# 4. summary and note setup
# ---------------------------------------------------------------------------
th.train = True
th.save_model = True
th.overwrite = True
th.print_cycle = 20
# ---------------------------------------------------------------------------
# 5. other stuff and activate
# ---------------------------------------------------------------------------
tail = ''
th.mark = '{}({})'.format(model_name, '-'.join(
[str(dim) for dim in th.fc_dims])) + tail
th.gather_summ_name = th.prefix + summ_name + tail + '.sum'
core.activate()