def main(_):
console.start('{} on TO task'.format(model_name.upper()))
th = core.th
# ---------------------------------------------------------------------------
# 0. date set setup
# ---------------------------------------------------------------------------
th.sequence_length = 100
th.bits = 3
# ---------------------------------------------------------------------------
# 1. folder/file names and device
# ---------------------------------------------------------------------------
th.job_dir += '/{:02d}_{}'.format(id, model_name)
summ_name = model_name
th.visible_gpu_id = 0
th.prefix = '{}_'.format(date_string())
th.suffix = ''
# ---------------------------------------------------------------------------
# 2. model setup
# ---------------------------------------------------------------------------
th.model = model
th.fast_layers = 2
table = {2: 41, 3: 45, 4: 47, 5: 49, 6: 50}
th.fast_size = table[th.fast_layers]
th.slow_size = th.fast_size
th.hyper_kernel = 'lstm'
th.forget_bias_initializer = 2.0
# ---------------------------------------------------------------------------
# 3. trainer setup
# ---------------------------------------------------------------------------
th.max_iterations = 50000
th.optimizer = tf.train.AdamOptimizer
th.learning_rate = 0.001
# ---------------------------------------------------------------------------
# 4. summary and note setup
# ---------------------------------------------------------------------------
th.export_tensors_upon_validation = True
# ---------------------------------------------------------------------------
# 5. other stuff and activate
# ---------------------------------------------------------------------------
tail = '_{}bits_L{}'.format(th.bits, th.sequence_length)
th.mark = FastSlow.mark() + tail
th.gather_summ_name = th.prefix + summ_name + tail + th.suffix + '.sum'
core.activate()
def main(_):
console.start('{} on TO task'.format(model_name.upper()))
th = core.th
# ---------------------------------------------------------------------------
# 0. date set setup
# ---------------------------------------------------------------------------
th.sequence_length = 100
th.bits = 3
# ---------------------------------------------------------------------------
# 1. folder/file names and device
# ---------------------------------------------------------------------------
th.job_dir += '/{:02d}_{}'.format(id, model_name)
summ_name = model_name
th.visible_gpu_id = 0
th.prefix = '{}_'.format(date_string())
th.suffix = ''
# ---------------------------------------------------------------------------
# 2. model setup
# ---------------------------------------------------------------------------
th.model = model
th.gam_config = '6x10'
th.head_size = 10
th.hyper_kernel = 'gru'
th.state_size = 60
th.num_layers = 1
# ---------------------------------------------------------------------------
# 3. trainer setup
# ---------------------------------------------------------------------------
th.max_iterations = 50000
th.optimizer = tf.train.AdamOptimizer
th.learning_rate = 0.001
# ---------------------------------------------------------------------------
# 4. summary and note setup
# ---------------------------------------------------------------------------
th.export_tensors_upon_validation = True
# ---------------------------------------------------------------------------
# 5. other stuff and activate
# ---------------------------------------------------------------------------
tail = '_{}bits_L{}'.format(th.bits, th.sequence_length)
th.mark = GamRHN.mark() + tail
th.gather_summ_name = th.prefix + summ_name + tail + th.suffix + '.sum'
core.activate()
def main(_):
console.start('{} on TO task'.format(model_name.upper()))
th = core.th
# ---------------------------------------------------------------------------
# 0. date set setup
# ---------------------------------------------------------------------------
th.sequence_length = 200
th.bits = 3
# ---------------------------------------------------------------------------
# 1. folder/file names and device
# ---------------------------------------------------------------------------
th.job_dir += '/{:02d}_{}'.format(id, model_name)
summ_name = model_name
th.visible_gpu_id = 0
prefix = '{}_'.format(date_string())
suffix = ''
# ---------------------------------------------------------------------------
# 2. model setup
# ---------------------------------------------------------------------------
th.model = model
th.state_size = 67
th.forget_bias_initializer = 2.0
# ---------------------------------------------------------------------------
# 3. trainer setup
# ---------------------------------------------------------------------------
th.max_iterations = 10000
th.optimizer = tf.train.AdamOptimizer
th.learning_rate = 0.001
# ---------------------------------------------------------------------------
# 4. summary and note setup
# ---------------------------------------------------------------------------
th.export_tensors_upon_validation = True
# ---------------------------------------------------------------------------
# 5. other stuff and activate
# ---------------------------------------------------------------------------
tail = '_{}bits_L{}'.format(th.bits, th.sequence_length) + suffix
th.mark = prefix + '{}({})'.format(model_name, th.state_size) + tail
th.gather_summ_name = prefix + summ_name + tail + '.sum'
core.activate()