Example of Parameters ============================== :: Importing candle utils for keras usage: mnist_cnn [-h] [--config_file CONFIG_FILE] [-d {f16,f32,f64}] [-r RNG_SEED] [--train_bool TRAIN_BOOL] [--eval_bool EVAL_BOOL] [--timeout TIMEOUT] [--gpus GPUS [GPUS ...]] [-p PROFILING] [-s SAVE_PATH] [--model_name MODEL_NAME] [--home_dir HOME_DIR] [--train_data TRAIN_DATA] [--val_data VAL_DATA] [--test_data TEST_DATA] [--output_dir OUTPUT_DIR] [--data_url DATA_URL] [--experiment_id EXPERIMENT_ID] [--run_id RUN_ID] [-v VERBOSE] [-l LOGFILE] [--scaling {minabs,minmax,std,none}] [--shuffle SHUFFLE] [--feature_subsample FEATURE_SUBSAMPLE] [--dense DENSE [DENSE ...]] [--conv CONV [CONV ...]] [--locally_connected LOCALLY_CONNECTED] [-a ACTIVATION] [--out_activation OUT_ACTIVATION] [--lstm_size LSTM_SIZE [LSTM_SIZE ...]] [--recurrent_dropout RECURRENT_DROPOUT] [--dropout DROPOUT] [--pool POOL] [--batch_normalization BATCH_NORMALIZATION] [--loss LOSS] [--optimizer OPTIMIZER] [--metrics METRICS] [-e EPOCHS] [-z BATCH_SIZE] [-lr LEARNING_RATE] [--early_stop EARLY_STOP] [--momentum MOMENTUM] [--initialization {constant,uniform,normal,glorot_uniform,glorot_normal,lecun_uniform,he_normal}] [--val_split VAL_SPLIT] [--train_steps TRAIN_STEPS] [--val_steps VAL_STEPS] [--test_steps TEST_STEPS] [--train_samples TRAIN_SAMPLES] [--val_samples VAL_SAMPLES] [--clr_flag CLR_FLAG] [--clr_mode {trng1,trng2,exp}] [--clr_base_lr CLR_BASE_LR] [--clr_max_lr CLR_MAX_LR] [--clr_gamma CLR_GAMMA] [--ckpt_restart_mode {off,auto,required}] [--ckpt_checksum CKPT_CHECKSUM] [--ckpt_skip_epochs CKPT_SKIP_EPOCHS] [--ckpt_directory CKPT_DIRECTORY] [--ckpt_save_best CKPT_SAVE_BEST] [--ckpt_save_best_metric CKPT_SAVE_BEST_METRIC] [--ckpt_save_weights_only CKPT_SAVE_WEIGHTS_ONLY] [--ckpt_save_interval CKPT_SAVE_INTERVAL] [--ckpt_keep_mode {linear,exponential}] [--ckpt_keep_limit CKPT_KEEP_LIMIT]MNIST CNN exampleoptional arguments: -h, --help show this help message and exit --config_file CONFIG_FILE specify model configuration file -d {f16,f32,f64}, --data_type {f16,f32,f64} default floating point. -r RNG_SEED, --rng_seed RNG_SEED random number generator seed. --train_bool TRAIN_BOOL train model. (default: True) --eval_bool EVAL_BOOL evaluate model (use it for inference). --timeout TIMEOUT seconds allowed to train model (default: no timeout). --gpus GPUS [GPUS ...] set IDs of GPUs to use. -p PROFILING, --profiling PROFILING Turn profiling on or off. (default: False) -s SAVE_PATH, --save_path SAVE_PATH file path to save model snapshots. --model_name MODEL_NAME specify model name to use when building filenames for saving. --home_dir HOME_DIR set home directory. --train_data TRAIN_DATA training data filename. --val_data VAL_DATA validation data filename. --test_data TEST_DATA testing data filename. --output_dir OUTPUT_DIR output directory. --data_url DATA_URL set data source url. --experiment_id EXPERIMENT_ID set the experiment unique identifier. (default: EXP000) --run_id RUN_ID set the run unique identifier. (default: RUN000) -v VERBOSE, --verbose VERBOSE increase output verbosity. (default: False) -l LOGFILE, --logfile LOGFILE log file (default: None) --scaling {minabs,minmax,std,none} type of feature scaling; 'minabs': to [-1,1]; 'minmax': to [0,1], 'std': standard unit normalization; 'none': no normalization. --shuffle SHUFFLE randomly shuffle data set (produces different training and testing partitions each run depending on the seed) (default: False) --feature_subsample FEATURE_SUBSAMPLE number of features to randomly sample from each category (cellline expression, drug descriptors, etc), 0 means using all features --dense DENSE [DENSE ...] number of units in fully connected layers in an integer array. --conv CONV [CONV ...] integer array describing convolution layers: conv1_filters, conv1_filter_len, conv1_stride, conv2_filters, conv2_filter_len, conv2_stride .... --locally_connected LOCALLY_CONNECTED use locally connected layers instead of convolution layers. -a ACTIVATION, --activation ACTIVATION keras activation function to use in inner layers: relu, tanh, sigmoid... --out_activation OUT_ACTIVATION keras activation function to use in out layer: softmax, linear, ... --lstm_size LSTM_SIZE [LSTM_SIZE ...] integer array describing size of LSTM internal state per layer. --recurrent_dropout RECURRENT_DROPOUT ratio of recurrent dropout. --dropout DROPOUT ratio of dropout used in fully connected layers. --pool POOL pooling layer length. --batch_normalization BATCH_NORMALIZATION use batch normalization. --loss LOSS keras loss function to use: mse, ... --optimizer OPTIMIZER keras optimizer to use: sgd, rmsprop, ... --metrics METRICS metrics to evaluate performance: accuracy, ... -e EPOCHS, --epochs EPOCHS number of training epochs. -z BATCH_SIZE, --batch_size BATCH_SIZE batch size. -lr LEARNING_RATE, --learning_rate LEARNING_RATE overrides the learning rate for training. --early_stop EARLY_STOP activates keras callback for early stopping of training in function of the monitored variable specified. --momentum MOMENTUM overrides the momentum to use in the SGD optimizer when training. --initialization {constant,uniform,normal,glorot_uniform,glorot_normal,lecun_uniform,he_normal} type of weight initialization; 'constant': to 0; 'uniform': to [-0.05,0.05], 'normal': mean 0, stddev 0.05; 'glorot_uniform': [-lim,lim] with lim = sqrt(6/(fan_in+fan_out)); 'lecun_uniform' : [-lim,lim] with lim = sqrt(3/fan_in); 'he_normal' : mean 0, stddev sqrt(2/fan_in). --val_split VAL_SPLIT fraction of data to use in validation. --train_steps TRAIN_STEPS overrides the number of training batches per epoch if set to nonzero. --val_steps VAL_STEPS overrides the number of validation batches per epoch if set to nonzero. --test_steps TEST_STEPS overrides the number of test batches per epoch if set to nonzero. --train_samples TRAIN_SAMPLES overrides the number of training samples if set to nonzero. --val_samples VAL_SAMPLES overrides the number of validation samples if set to nonzero. --clr_flag CLR_FLAG CLR flag (boolean). --clr_mode {trng1,trng2,exp} CLR mode (default: trng1). --clr_base_lr CLR_BASE_LR Base lr for cycle lr. --clr_max_lr CLR_MAX_LR Max lr for cycle lr. --clr_gamma CLR_GAMMA Gamma parameter for learning cycle LR. --ckpt_restart_mode {off,auto,required} Mode to restart from a saved checkpoint file, choices are 'off', 'auto', 'required'. (default: auto) --ckpt_checksum CKPT_CHECKSUM Checksum the restart file after read+write. (default: False) --ckpt_skip_epochs CKPT_SKIP_EPOCHS Number of epochs to skip before saving epochs. (default: 0) --ckpt_directory CKPT_DIRECTORY Base directory in which to save checkpoints. (default: ./save) --ckpt_save_best CKPT_SAVE_BEST Toggle saving best model. (default: True) --ckpt_save_best_metric CKPT_SAVE_BEST_METRIC Metric for determining when to save best model. (default: val_loss) --ckpt_save_weights_only CKPT_SAVE_WEIGHTS_ONLY Toggle saving only weights (not optimizer) (NYI). (default: False) --ckpt_save_interval CKPT_SAVE_INTERVAL Interval to save checkpoints. (default: 0) --ckpt_keep_mode {linear,exponential} Checkpoint saving mode, choices are 'linear' or 'exponential'. (default: linear) --ckpt_keep_limit CKPT_KEEP_LIMIT Limit checkpoints to keep. (default: 1000000) ::