Package: gpkg.slim.models
gpkg.slim.models################*TF-Slim models (Guild AI)*Models######images======*Generic images dataset*Operations^^^^^^^^^^prepare-------*Prepare images for training*Flags`````**images***Directory containing images to prepare (required)***random-seed***Seed used for train/validation split (randomly generated)***val-split***Percentage of images reserved for validation (30)*inception=========*TF-Slim Inception v1 classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*inception-resnet-v2===================*TF-Slim Inception ResNet v2 classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*inception-v2============*TF-Slim Inception v2 classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*inception-v3============*TF-Slim Inception v3 classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*inception-v4============*TF-Slim Inception v4 classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*mobilenet=========*TF-Slim Mobilenet v1 classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*mobilenet-v2-1.4================*TF-Slim Mobilenet v2 classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*nasnet-large============*TF-Slim NASNet large classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*nasnet-mobile=============*TF-Slim NASNet mobile classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*pnasnet-large=============*TF-Slim PNASNet classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*pnasnet-mobile==============*TF-Slim PNASNet mobile classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*resnet-101==========*TF-Slim ResNet v1 101 layer classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*resnet-152==========*TF-Slim ResNet v1 152 layer classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*resnet-50=========*TF-Slim ResNet v1 50 layer classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*resnet-v2-101=============*TF-Slim ResNet v2 101 layer classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*resnet-v2-152=============*TF-Slim ResNet v2 152 layer classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*resnet-v2-50============*TF-Slim ResNet v2 50 layer classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*vgg-16======*TF-Slim VGG 16 classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*vgg-19======*TF-Slim VGG 19 classifier*Operations^^^^^^^^^^evaluate--------*Evaluate a trained model*Flags`````**batch-size***Number of examples in each evaluated batch (100)***eval-batches***Number of batches to evaluate (all available)***step***Checkpoint step to evaluate (latest checkpoint)*export-and-freeze-----------------*Export an inference graph with checkpoint weights*Flags`````**step***Checkpoint step to use for the frozen graph (latest checkpoint)*finetune--------*Finetune a trained model*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.0001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*label-----*Classify an image using a trained model*Flags`````**image***Path to image to classify (required)*tflite------*Generate a TFLite file from a frozen graph*Flags`````**output-format***TF Lite output format (tflite)Choices:tflitegraphviz_dot***quantized***Whether or not output arrays are quantized (no)Choices:yesno***quantized-inputs***Whether or not input arrays are quantized (no)Choices:yesno*train-----*Train model from scratch*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*transfer-learn--------------*Train model using transfer learning*Flags`````**auto-scale***Adjust applicable flags for multi-GPU systems (yes)Set to 'no' to disable any flag value adjustments.When this value is 'yes' (the default) the following flags are adjusted onmulti-GPU systems:- clones- learning-rate`clones` is set to the number of available GPUs.`learning-rate` is adjusted by multiplying its specified value by thenumber of GPUs.Flags are not adjusted on single GPU or CPU only systems.***batch-size***Number of examples in each training batch (32)***clones***Number of model clones (calculated)This value is automatically set to the number of available GPUs if `auto-scale` is 'yes'.When `auto-scale` is 'no' this value can be increased from 1 to train themodel in parallel on multiple GPUs.***learning-rate***Initial learning rate (0.001)***learning-rate-decay-epochs***Number of epochs after which learning rate decays (2.0)***learning-rate-decay-factor***Learning rate decay factor (0.94)***learning-rate-decay-type***Method used to decay the learning rate (exponential)Choices:exponentialfixedpolynomial***learning-rate-end***Minimal learning rate used by polynomial learning rate decay (0.0001)***log-save-seconds***Frequency of log summary saves in seconds (60)***log-steps***Frequency of summary logs in steps (100)***model-save-seconds***Frequency of model saves (checkpoints) in seconds (600)***optimizer***Optimizer used to train (rmsprop)Choices:adadeltaadagradadamftrlmomentumrmspropsgd***preprocessing***Preprocessing to use (default for model)***preprocessors***Number of preprocessing threads (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize thepreprocessor thread count for the system.***readers***Number of parallel data readers (calculated)This value is automatically set to logical CPU count / 2 if `auto-scale`is 'yes'.When `auto-scale` is 'no' this value can be set to optimize data readerperformance for the system.***train-steps***Number of steps to train (train indefinitely)***weight-decay***Decay on the model weights (4e-05)*