def main_flags(): # Data & model config flags.DEFINE_string('data_config', 'datasets/multid_config.py', 'Path to a data config file.') flags.DEFINE_string('model_config', 'models/genesis_config.py', 'Path to a model config file.') # Logging config flags.DEFINE_string('results_dir', 'checkpoints', 'Top directory for all experimental results.') flags.DEFINE_string('run_name', 'test', 'Name of this job and name of results folder.') flags.DEFINE_integer( 'report_loss_every', 1000, 'Number of iterations between reporting minibatch loss.') flags.DEFINE_integer('run_validation_every', 10000, 'How many equally spaced validation runs to do.') flags.DEFINE_integer('num_checkpoints', 40, 'How many equally spaced model checkpoints to save.') flags.DEFINE_boolean('resume', False, 'Tries to resume a job if True.') flags.DEFINE_boolean( 'log_grads_and_weights', False, 'Log gradient and weight histograms - storage intensive!') flags.DEFINE_boolean( 'log_distributions', False, 'Log mu and sigma of posterior and prior distributions.') # Optimisation config flags.DEFINE_integer('train_iter', 2000000, 'Number of training iterations.') flags.DEFINE_integer('batch_size', 32, 'Mini-batch size.') flags.DEFINE_string('optimiser', 'adam', 'Optimiser for updating weights.') flags.DEFINE_float('learning_rate', 0.0001, 'Learning rate.') flags.DEFINE_integer('N_eval', 10000, 'Number of samples to run evaluation on.') # Loss config flags.DEFINE_float('beta', 0.5, 'KL weighting.') flags.DEFINE_boolean('beta_warmup', True, 'Warm up beta.') flags.DEFINE_boolean('geco', True, 'Use GECO objective.') flags.DEFINE_float('g_goal', 0.5655, 'GECO recon goal.') flags.DEFINE_float('g_lr', 1e-5, 'GECO learning rate.') flags.DEFINE_float('g_alpha', 0.99, 'GECO momentum for error.') flags.DEFINE_float('g_init', 1.0, 'GECO inital Lagrange factor.') flags.DEFINE_float('g_min', 1e-10, 'GECO min Lagrange factor.') flags.DEFINE_float('g_speedup', 10., 'Scale GECO lr if delta positive.') # Other flags.DEFINE_boolean('gpu', True, 'Use GPU if available.') flags.DEFINE_boolean('multi_gpu', False, 'Use multiple GPUs if available.') flags.DEFINE_boolean('debug', False, 'Debug flag.') flags.DEFINE_integer('seed', 0, 'Seed for random number generators.')
def main_flags(): # Data & model config flags.DEFINE_string('data_config', 'datasets/gqn_config.py', 'Path to a data config file.') flags.DEFINE_string('model_config', 'models/genesis_config.py', 'Path to a model config file.') # Trained model flags.DEFINE_string('model_dir', 'checkpoints/test/1', 'Path to model directory.') flags.DEFINE_string('model_file', 'model.ckpt-FINAL', 'Name of model file.') # FID flags.DEFINE_integer('feat_dim', 2048, 'Number of Incpetion features.') flags.DEFINE_integer('num_fid_images', 10000, 'Number of images to compute the FID on.') # Other flags.DEFINE_string('img_dir', '/tmp', 'Directory for saving pngs.') flags.DEFINE_integer('batch_size', 10, 'Mini-batch size.') flags.DEFINE_boolean('gpu', True, 'Use GPU if available.') flags.DEFINE_integer('seed', 0, 'Seed for random number generators.')
flags.DEFINE_string( "results_dir", "checkpoints/", "Top directory for all experimental results." ) # Configuration files to load flags.DEFINE_string( "data_config", "configs/molecule/qm9_data.py", "Path to a data config file." ) flags.DEFINE_string( "model_config", "configs/molecule/set_transformer.py", "Path to a model config file.", ) # Job management flags.DEFINE_string("run_name", "test", "Name of this job and name of results folder.") flags.DEFINE_boolean("resume", False, "Tries to resume a job if True.") # Logging flags.DEFINE_integer( "report_loss_every", 500, "Number of iterations between reporting minibatch loss." ) flags.DEFINE_integer( "evaluate_every", 10000, "Number of iterations between reporting validation loss." ) flags.DEFINE_integer( "save_check_points", 10, "frequency with which to save checkpoints, in number of epochs.", ) flags.DEFINE_boolean("log_train_values", True, "Logs train values if True.") flags.DEFINE_float(
from torch import nn import torch.nn.functional as F from eqv_transformer.classfier import Classifier from eqv_transformer.eqv_attention_se2_finite import EqvTransformer from forge import flags # flags.DEFINE_integer('input_dim', 2, 'Dimensionality of the input.') flags.DEFINE_integer('n_outputs', 4, 'Number of output vectors.') # flags.DEFINE_integer('output_dim', 3, 'Dimensionality of the output.') flags.DEFINE_string('content_type', 'pairwise_distances', 'How to initialize y') flags.DEFINE_integer('n_enc_layers', 4, 'Number of encoder layers.') flags.DEFINE_integer('n_dec_layers', 4, 'Number of encoder layers.') flags.DEFINE_integer('n_heads', 4, 'Number of attention heads.') flags.DEFINE_boolean('layer_norm', False, 'Uses layer-norm if True.') flags.DEFINE_integer('cn', 5, 'Size of rotation group.') flags.DEFINE_string('similarity_fn', 'softmax', 'Similarity function used to compute attention weights.') flags.DEFINE_string('arch', 'set_transf', 'Architecture.') flags.DEFINE_integer('num_moments', 5, 'When using pairwise distances as Y, number of moments.') def load(config, **unused_kwargs): del unused_kwargs # should not affect things #### number of moments # config.patterns_reps * 17 - 1 input_dim = None output_dim = config.patterns_reps + 1
import torch.nn as nn from torch.distributions.normal import Normal from forge import flags import modules.blocks as B import modules.seq_att as seq_att import modules.decoders as decoders from modules.component_vae import ComponentVAE import third_party.sylvester.VAE as sylvester import utils.misc as misc # Model type flags.DEFINE_boolean('two_stage', True, 'Use two stages if two, else only one.') # Priors flags.DEFINE_boolean('autoreg_prior', True, 'Autoregressive prior.') flags.DEFINE_boolean('comp_prior', True, 'Component prior.') # Attention VAE flags.DEFINE_integer('attention_latents', 64, 'Latent dimension.') flags.DEFINE_string('enc_norm', 'bn', '{bn, in} - norm type in encoder.') flags.DEFINE_string('dec_norm', 'bn', '{bn, in} - norm type in decoder.') # Component VAE flags.DEFINE_integer('comp_enc_channels', 32, 'Starting number of channels.') flags.DEFINE_integer('comp_ldim', 16, 'Latent dimension of the VAE.') flags.DEFINE_integer('comp_dec_channels', 32, 'Num channels in Broadcast Decoder.') flags.DEFINE_integer('comp_dec_layers', 4, 'Num layers in Broadcast Decoder.') flags.DEFINE_boolean('comp_symmetric', False, 'Use same encoder/decoder as in attention VAE.')
import numpy as np import tensorflow as tf import torch from torchvision import transforms # import ipdb from forge import flags import pickle import os import json flags.DEFINE_integer("train_size", 10000, "Number of training examples per epoch.") flags.DEFINE_integer("test_size", 1000, "Number of testing examples per epoch.") flags.DEFINE_integer("naug", 2, "Number of augmentation.") flags.DEFINE_float("corner_noise", 0.1, "See `create_constellations`.") flags.DEFINE_boolean("shuffle_corners", True, "See `create_constellations`.") flags.DEFINE_float("pattern_upscale", 0.0, "See `create_constellations`.") flags.DEFINE_float("max_rotation", 0.33, "See `create_constellations`.") flags.DEFINE_float("global_rotation_angle", 0.0, "See `create_constellations`.") flags.DEFINE_float("global_translation", 0.0, "See `create_constellations`.") flags.DEFINE_float("pattern_drop_prob", 0.5, "See `create_constellations`.") flags.DEFINE_integer("patterns_reps", 2, "See `create_constellations`.") flags.DEFINE_integer("data_seed", 0, "Seed for data generation.") def roots_of_unity(n): x_coors = np.cos(2 * np.pi / n * np.arange(n)[..., np.newaxis]) y_coors = np.sin(2 * np.pi / n * np.arange(n)[..., np.newaxis]) coors = np.concatenate([x_coors, y_coors, np.tile([[1]], (n, 1))], axis=1)
import numpy as np from PIL import Image from forge import flags from forge.experiment_tools import fprint from utils.misc import loader_throughput from third_party.shapestacks.shapestacks_provider import _get_filenames_with_labels flags.DEFINE_string('data_folder', 'data/shapestacks', 'Path to data folder.') flags.DEFINE_string('split_name', 'default', '{default, blocks_all, css_all}') flags.DEFINE_integer('img_size', 64, 'Dimension of images. Images are square.') flags.DEFINE_boolean('shuffle_test', False, 'Shuffle test set.') flags.DEFINE_integer('num_workers', 4, 'Number of threads for loading data.') flags.DEFINE_boolean('copy_to_tmp', False, 'Copy files to /tmp.') flags.DEFINE_integer('K_steps', 9, 'Number of recurrent steps.') MAX_SHAPES = 6 CENTRE_CROP = 196 def load(cfg, **unused_kwargs): del unused_kwargs if not os.path.exists(cfg.data_folder): raise Exception("Data folder does not exist.")
from attrdict import AttrDict import torch from torch import nn import torch.nn.functional as F from lie_conv.datasets import SE3aug from eqv_transformer.attention import SetTransformer from eqv_transformer.molecule_predictor import MoleculePredictor from forge import flags flags.DEFINE_boolean("data_augmentation", False, "Apply data augmentation to the input data or not") flags.DEFINE_integer("n_enc_layers", 4, "Number of encoder layers.") flags.DEFINE_integer("n_dec_layers", 4, "Number of encoder layers.") flags.DEFINE_integer("num_heads", 4, "Number of attention heads.") flags.DEFINE_integer( "n_inducing_points", 0, "Number of inducing points; does not use inducing points if 0.", ) flags.DEFINE_boolean("layer_norm", False, "Uses layer-norm if True.") flags.DEFINE_integer("hidden_dim", 128, "Hidden dimension between layers") class MolecueSetTransformer(SetTransformer): def __init__(self, num_species, charge_scale, aug=False, **kwargs): super().__init__(dim_input=3 + 3 * num_species, num_outputs=1,
import torch from eqv_transformer.classfier import Classifier from eqv_transformer.eqv_attention import EquivariantTransformer from lie_conv.lieGroups import SE3, SE2, SO3, T, Trivial # from lie_conv.datasets import SE3aug from forge import flags flags.DEFINE_boolean( "data_augmentation", False, "Apply data augmentation to the data before passing to the model", ) flags.DEFINE_integer("dim_hidden", 512, "Dimension of features to use in each layer") flags.DEFINE_string( "activation_function", "swish", "Activation function to use in the network" ) # flags.DEFINE_boolean("layer_norm", True, "Use layer norm in the layers") flags.DEFINE_boolean( "mean_pooling", True, "Use mean pooling insteave of sum pooling in the invariant layer", ) flags.DEFINE_integer("num_heads", 8, "Number of attention heads in each layer") flags.DEFINE_integer("kernel_dim", 16, "Hidden layer size to use in kernel MLPs") # flags.DEFINE_boolean("batch_norm", False, "Use batch norm in the kernel MLPs") flags.DEFINE_integer("num_layers", 6, "Number of ResNet layers to use") flags.DEFINE_string("group", "SE2", "Group to be invariant to")
"data_config", "configs/dynamics/spring_dynamics_data.py", "Path to a data config file.", ) flags.DEFINE_string( "model_config", "configs/dynamics/eqv_transformer_model.py", "Path to a model config file.", ) # Job management flags.DEFINE_string( "run_name", "test", "Name of this job and name of results folder.", ) flags.DEFINE_boolean("resume", False, "Tries to resume a job if True.") # Logging flags.DEFINE_integer("report_loss_every", 10, "Number of iterations between reporting minibatch loss.") flags.DEFINE_integer( "evaluate_every", 10000, "Number of iterations between reporting validation loss.") flags.DEFINE_integer( "save_check_points", 50, "frequency with which to save checkpoints, in number of epoches.", ) flags.DEFINE_boolean("log_train_values", True, "Logs train values if True.") # Optimization
from torch.utils.data import Dataset, DataLoader from torchvision import transforms import torch.nn.functional as F import numpy as np from forge import flags from utils.misc import loader_throughput flags.DEFINE_string('data_folder', 'data/multi_dsprites/processed', 'Path to data folder.') flags.DEFINE_integer('img_size', 64, 'Dimension of images. Images are square.') flags.DEFINE_integer('num_workers', 4, 'Number of threads for loading data.') flags.DEFINE_boolean('mem_map', False, 'Use memory mapping.') flags.DEFINE_integer('K_steps', 5, 'Number of recurrent steps.') def load(cfg, **unused_kwargs): """ Args: cfg (obj): Forge config Returns: (DataLoader, DataLoader, DataLoader): Tuple of data loaders for train, val, test """ del unused_kwargs if not os.path.exists(cfg.data_folder): raise Exception("Data folder does not exist.")
import torch from torch.utils.data import Dataset, DataLoader from torchvision import transforms import torch.nn.functional as F import numpy as np from forge import flags from utils.misc import loader_throughput flags.DEFINE_string('data_folder', 'data/multi_dsprites/processed', 'Path to data folder.') flags.DEFINE_boolean('load_instances', False, 'Load instances.') flags.DEFINE_integer('img_size', 64, 'Dimension of images. Images are square.') flags.DEFINE_integer('num_workers', 4, 'Number of threads for loading data.') flags.DEFINE_boolean('mem_map', False, 'Use memory mapping.') flags.DEFINE_integer('K_steps', 5, 'Number of recurrent steps.') def load(cfg, **unused_kwargs): """ Args: cfg (obj): Forge config Returns: (DataLoader, DataLoader, DataLoader): Tuple of data loaders for train, val, test
from corm_data.collate import collate_fn import forge from forge import flags flags.DEFINE_float( "subsample_trainset", 1.0, "Proportion or number of samples of the full trainset to use", ) flags.DEFINE_string( "task", "h**o", "Which task in the QM9 dataset to train on. Pass as a comma separated string", ) flags.DEFINE_boolean("recenter", False, "Recenter the positions of atoms with charge > 0") flags.DEFINE_integer("batch_fit", 0, "number of samples to fit to") flags.DEFINE_integer("data_seed", 0, "seed to pick data with") def load(config, **unused_kwargs): with FixedNumpySeed(config.data_seed): datasets, num_species, charge_scale = QM9datasets( os.path.join(config.data_dir, "qm9")) if config.subsample_trainset != 1.0: datasets.update( split_dataset(datasets["train"], {"train": config.subsample_trainset})) if config.batch_fit != 0: datasets.update(
from eqv_transformer.eqv_attention import EquivariantTransformer from lie_conv.dynamicsTrainer import HNet from lie_conv.hamiltonian import HamiltonianDynamics from lie_conv.lieGroups import T, SE2, SE2_canonical, SO2 from eqv_transformer.dynamics_predictor import DynamicsPredictor from forge import flags flags.DEFINE_string("group", "T(2)", "Group to be invariant to.") flags.DEFINE_integer("dim_hidden", 160, "Dimension of features to use in each layer") flags.DEFINE_string("activation_function", "swish", "Activation function to use in the network") flags.DEFINE_boolean( "mean_pooling", True, "Use mean pooling insteave of sum pooling in the invariant layer", ) flags.DEFINE_integer("num_heads", 8, "Number of attention heads in each layer") flags.DEFINE_integer("kernel_dim", 16, "Hidden layer size to use in kernel MLPs") flags.DEFINE_integer("num_layers", 5, "Number of ResNet layers to use") flags.DEFINE_integer( "lift_samples", 1, "Number of coset lift samples to use for non-trivial stabilisers.", ) flags.DEFINE_integer("model_seed", 0, "Model rng seed") flags.DEFINE_string("attention_fn", "dot_product", "How to form the attention weights from the 'logits'.")
flags.DEFINE_integer("n_test", 2000, "Number of testing datapoints.") flags.DEFINE_integer("n_val", 2000, "Number of validation datapoints.") flags.DEFINE_integer("n_systems", 10000, "Size of total dataset generated.") flags.DEFINE_string( "data_path", "./datasets/ODEDynamics/SpringDynamics/", "Dataset is loaded from and/or downloaded to this path.", ) flags.DEFINE_integer("sys_dim", 2, "[add description].") flags.DEFINE_integer("space_dim", 2, "Dimension of particle system.") flags.DEFINE_integer("data_seed", 0, "Data splits random seed.") flags.DEFINE_integer("num_particles", 6, "Number of particles in system.") flags.DEFINE_integer("chunk_len", 5, "Length of trajectories.") flags.DEFINE_boolean( "load_preprocessed", False, "Load data already preprocessed to avoid RAM memory spike. Ensure data exists first for the chunk_lun required.", ) def load(config): dataset = SpringDynamics( n_systems=config.n_systems, root_dir=config.data_path, space_dim=config.space_dim, num_particles=config.num_particles, chunk_len=config.chunk_len, load_preprocessed=config.load_preprocessed, )
from torch.distributions.normal import Normal from forge import flags import modules.blocks as B import modules.seq_att as seq_att import modules.decoders as decoders from modules.component_vae import ComponentVAE from third_party.sylvester.VAE import VAE import utils.misc as misc # Model type flags.DEFINE_boolean('two_stage', True, 'Use two stages if two, else only one.') # Priors flags.DEFINE_boolean('autoreg_prior', True, 'Autoregressive prior.') flags.DEFINE_boolean('comp_prior', True, 'Component prior.') # Attention VAE flags.DEFINE_integer('attention_latents', 64, 'Latent dimension.') flags.DEFINE_string('enc_norm', 'bn', '{bn, in} - norm type in encoder.') flags.DEFINE_string('dec_norm', 'bn', '{bn, in} - norm type in decoder.') # Component VAE flags.DEFINE_integer('comp_enc_channels', 32, 'Starting number of channels.') flags.DEFINE_integer('comp_ldim', 16, 'Latent dimension of the VAE.') flags.DEFINE_integer('comp_dec_channels', 32, 'Num channels in Broadcast Decoder.') flags.DEFINE_integer('comp_dec_layers', 4, 'Num layers in Broadcast Decoder.') # Losses flags.DEFINE_boolean('pixel_bound', True, 'Bound pixel values to [0, 1].') flags.DEFINE_float('pixel_std1', 0.7, 'StdDev of reconstructed pixels.')
from eqv_transformer.attention import SetTransformer from forge import flags flags.DEFINE_integer("input_dim", 2, "Dimensionality of the input.") flags.DEFINE_integer("n_outputs", 4, "Number of output vectors.") flags.DEFINE_integer("output_dim", 3, "Dimensionality of the output.") flags.DEFINE_integer("n_enc_layers", 4, "Number of encoder layers.") flags.DEFINE_integer("n_dec_layers", 4, "Number of encoder layers.") flags.DEFINE_integer("num_heads", 4, "Number of attention heads.") flags.DEFINE_integer( "n_inducing_points", 0, "Number of inducing points; does not use inducing points if 0.", ) flags.DEFINE_boolean("layer_norm", False, "Uses layer-norm if True.") def load(config, **unused_kwargs): del unused_kwargs encoder = SetTransformer( config.input_dim, config.n_outputs, config.output_dim, n_enc_layers=config.n_enc_layers, n_dec_layers=config.n_dec_layers, num_heads=config.num_heads, num_inducing_points=config.n_inducing_points, ln=config.layer_norm, )
import numpy as np from PIL import Image from forge import flags from forge.experiment_tools import fprint from utils.misc import loader_throughput, np_img_centre_crop from third_party.shapestacks.shapestacks_provider import _get_filenames_with_labels from third_party.shapestacks.segmentation_utils import load_segmap_as_matrix flags.DEFINE_string('data_folder', 'data/shapestacks', 'Path to data folder.') flags.DEFINE_string('split_name', 'default', '{default, blocks_all, css_all}') flags.DEFINE_integer('img_size', 64, 'Dimension of images. Images are square.') flags.DEFINE_boolean('shuffle_test', False, 'Shuffle test set.') flags.DEFINE_integer('num_workers', 4, 'Number of threads for loading data.') flags.DEFINE_boolean('load_instances', False, 'Load instances.') flags.DEFINE_boolean('copy_to_tmp', False, 'Copy files to /tmp.') flags.DEFINE_integer('K_steps', 9, 'Number of recurrent steps.') MAX_SHAPES = 6 CENTRE_CROP = 196 def load(cfg, **unused_kwargs): del unused_kwargs if not os.path.exists(cfg.data_folder): raise Exception("Data folder does not exist.")
import os import torch from torch.utils.data import Dataset, DataLoader from torchvision import transforms import torch.nn.functional as F import numpy as np from forge import flags from utils.misc import loader_throughput flags.DEFINE_string('data_folder', 'data/multi_dsprites/processed', 'Path to data folder.') flags.DEFINE_boolean('unique_colours', False, 'Dataset with unique colours.') flags.DEFINE_boolean('load_instances', True, 'Load instances.') flags.DEFINE_integer('img_size', 64, 'Dimension of images. Images are square.') flags.DEFINE_integer('num_workers', 4, 'Number of threads for loading data.') flags.DEFINE_boolean('mem_map', False, 'Use memory mapping.') flags.DEFINE_integer('K_steps', 5, 'Number of recurrent steps.') def load(cfg, **unused_kwargs): """ Args: cfg (obj): Forge config Returns: (DataLoader, DataLoader, DataLoader):
from attrdict import AttrDict import torch import torch.nn as nn from torch.distributions.normal import Normal from forge import flags from modules.blocks import Flatten from modules.decoders import BroadcastDecoder from third_party.sylvester.VAE import VAE # GatedConvVAE flags.DEFINE_integer('latent_dimension', 64, 'Latent channels.') flags.DEFINE_boolean('broadcast_decoder', False, 'Use broadcast decoder instead of deconv.') # Losses flags.DEFINE_boolean('pixel_bound', True, 'Bound pixel values to [0, 1].') flags.DEFINE_float('pixel_std', 0.7, 'StdDev of reconstructed pixels.') def load(cfg): return BaselineVAE(cfg) class BaselineVAE(nn.Module): def __init__(self, cfg): super(BaselineVAE, self).__init__() cfg.K_steps = None # Configuration self.ldim = cfg.latent_dimension
from eqv_transformer.molecule_predictor import MoleculePredictor from lie_conv.lieGroups import SE3, SO3, T, Trivial from forge import flags flags.DEFINE_bool( "data_augmentation", False, "Apply data augmentation to the data before passing to the model", ) flags.DEFINE_integer( "nbhd_size", 25, "The number of samples to use for Monte Carlo estimation") flags.DEFINE_string("activation_function", "swish", "Activation function to use in the network") flags.DEFINE_boolean("batch_norm", True, "Use batch norm in the layers") flags.DEFINE_bool( "mean_pooling", True, "Use mean pooling insteave of sum pooling in the invariant layer", ) flags.DEFINE_integer("num_layers", 6, "Number of ResNet layers to use") flags.DEFINE_string("group", "SE3", "Group to be invariant to") flags.DEFINE_integer("channels", 1536, "Number of channels in the conv layers") flags.DEFINE_float( "fill", 1.0, "specifies the fraction of the input which is included in local neighborhood. (can be array to specify a different value for each layer", ) flags.DEFINE_integer( "lift_samples", 4,
import torch.optim as optim import forge from forge import flags import forge.experiment_tools as fet # Job config flags.DEFINE_string('data_config', 'configs/mnist_data.py', 'Path to a data config file.') flags.DEFINE_string('model_config', 'configs/mnist_mlp.py', 'Path to a model config file.') flags.DEFINE_string('results_dir', 'checkpoints', 'Top directory for all experimental results.') flags.DEFINE_string('run_name', 'mnist', 'Name of this job and name of results folder.') flags.DEFINE_boolean('resume', False, 'Tries to resume a job if True.') # Logging config flags.DEFINE_integer('report_loss_every', 100, 'Number of iterations between reporting minibatch loss.') flags.DEFINE_integer('train_epochs', 20, 'Maximum number of training epochs.') # Experiment config flags.DEFINE_integer('batch_size', 32, 'Mini-batch size.') flags.DEFINE_float('learning_rate', 1e-5, 'SGD learning rate.') # Parse flags config = forge.config() # Prepare enviornment logdir = osp.join(config.results_dir, config.run_name)
# Configuration files to load flags.DEFINE_string( "data_config", "configs/constellation/constellation.py", "Path to a data config file.", ) flags.DEFINE_string( "model_config", "configs/constellation/eqv_transformer_model.py", "Path to a model config file.", ) # Job management flags.DEFINE_string("run_name", "main", "Name of this job and name of results folder.") flags.DEFINE_boolean("resume", False, "Tries to resume a job if True.") # Logging flags.DEFINE_integer("report_loss_every", 500, "Number of iterations between reporting minibatch loss.") flags.DEFINE_integer( "evaluate_every", 10000, "Number of iterations between reporting validation loss.") flags.DEFINE_integer( "save_check_points", 50, "frequency with which to save checkpoints, in number of epoches.", ) flags.DEFINE_boolean("log_train_values", True, "Logs train values if True.") flags.DEFINE_float( "ema_alpha", 0.99,