Example of ParametersΒΆ

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)