### Hyper parameter setting
#######################################################################
# Input data
flags.DEFINE_string('Vocab_Processor_PATH',
'./Ch01_Data_load/data/VocabularyProcessor',
'VocabularyProcessor object file path')
flags.DEFINE_integer('VOCAB_SIZE', 72844, 'The number of terms in vocabulary')
flags.DEFINE_integer('EMBEDDING_SIZE', 256, 'Dimension of embedded terms')
flags.DEFINE_integer('MAXLEN', 22, 'max length of document')
# Class
flags.DEFINE_integer('NUM_OF_CLASS', 2, 'positive, negative')
# Parameter
flags.DEFINE_integer('HIDDEN_DIMENSION', 256, 'CNN hidden dimension')
flags.DEFINE_multi_integer('CONV_KERNEL_WIDTH', [19, 13], 'kernel height')
flags.DEFINE_integer('RNN_HIDDEN_DIMENSION', 256, 'RNN hidden dimension')
flags.DEFINE_integer('ATTENTION_SIZE', 100, 'attention dimension')
flags.DEFINE_integer('FC_HIDDEN_DIMENSION', 256, 'FC hidden dimension')
flags.DEFINE_float('Dropout_Rate1', 0.8, 'Dropout_Rate1')
flags.DEFINE_float('Dropout_Rate2', 0.8, 'Dropout_Rate2')
flags.DEFINE_integer('N_LAYERS', 2, 'The number of layers')
# Save
flags.DEFINE_string('WRITER', 'Text_RCNN_attention', 'saver name')
flags.DEFINE_boolean('WRITER_generate', True, 'saver generate')
# Train
flags.DEFINE_integer('BATCH_SIZE', 128, 'batch size')
flags.DEFINE_integer('TEST_BATCH', 128, 'test batch size')
flags.DEFINE_integer('NUM_OF_EPOCH', 3, 'number of epoch')
'VocabularyProcessor object file path')
flags.DEFINE_integer('VOCAB_SIZE', 72844, 'The number of terms in vocabulary')
flags.DEFINE_integer('EMBEDDING_SIZE', 256, 'Dimension of embedded terms')
flags.DEFINE_integer('MAXLEN', 22, 'max length of document')
# Class
flags.DEFINE_integer('NUM_OF_CLASS', 2, 'positive, negative')
# Parameter
flags.DEFINE_string('RNN_CELL', 'LSTM', 'RNN cell default LSTM')
flags.DEFINE_integer('RNN_HIDDEN_DIMENSION', 256, 'RNN hidden dimension')
flags.DEFINE_integer('FC_HIDDEN_DIMENSION', 256, 'FC hidden dimension')
flags.DEFINE_float('Dropout_Rate1', 0.7, 'Dropout_Rate1')
flags.DEFINE_float('Dropout_Rate2', 0.7, 'Dropout_Rate2')
flags.DEFINE_integer('N_LAYERS', 3, 'The number of layers')
# Save
flags.DEFINE_string('WRITER', 'Text_RNN_word', 'saver name')
flags.DEFINE_boolean('WRITER_generate', True, 'saver generate')
# Train
flags.DEFINE_integer('BATCH_SIZE', 128, 'batch size')
flags.DEFINE_integer('TEST_BATCH', 128, 'test batch size')
flags.DEFINE_integer('NUM_OF_EPOCH', 3, 'number of epoch')
flags.DEFINE_float('lr_value', 0.01, 'initial learning rate')
flags.DEFINE_float('lr_decay', 0.7, 'learning rate decay')
flags.DEFINE_multi_integer('Check_Loss', [5] * 20, 'loss decay')
# FLAGS
FLAGS = flags.FLAGS
"format. The SequenceExamples are expected to have an 'rgb' byte array "
"sequence feature as well as a 'labels' int64 context feature.")
# Other flags.
flags.DEFINE_integer("batch_size", 1024,
"How many examples to process per batch.")
flags.DEFINE_integer("num_readers", 8,
"How many threads to use for reading input files.")
flags.DEFINE_boolean("run_once", False, "Whether to run eval only once.")
flags.DEFINE_integer("top_k", 20,
"How many predictions to output per video.")
flags.DEFINE_integer("ensemble_num", 1, "")
flags.DEFINE_multi_integer(
"netvlad_cluster_size", 64,
"Number of units in the NetVLAD cluster layer.")
flags.DEFINE_multi_integer("netvlad_hidden_size", 1024,
"Number of units in the NetVLAD hidden layer.")
flags.DEFINE_multi_float("ensemble_wts", 1, '')
flags.DEFINE_boolean(
"force_output_model_name", False,
"If true, force model name to be 'inference_model', for model submission"
)
flags.DEFINE_boolean(
"create_meta_only", False,
"If true, only create meta graph without evaluation on data")
