def __init__(self, image_nr, image_resize, model_name): super().__init__() self.model_name = model_name self.ctx = neptune.Context() self.epoch_loss_averager = Averager() self.image_nr = image_nr self.image_resize = image_resize
def __init__(self, min_lr=1e-8, multipy_factor=1.05, add_factor=0.0): super().__init__() self.ctx = neptune.Context() self.ctx.channel_reset('Learning Rate Finder') self.min_lr = min_lr self.multipy_factor = multipy_factor self.add_factor = add_factor
def __init__(self, metric_name, minimize, reduce_factor, reduce_patience, min_lr): super().__init__() self.ctx = neptune.Context() self.ctx.channel_reset('Learning Rate') self.metric_name = metric_name self.minimize = minimize self.reduce_factor = reduce_factor self.reduce_patience = reduce_patience self.min_lr = min_lr
def __init__(self, image_nr, image_resize, image_every, model_name, use_depth): super().__init__() self.model_name = model_name self.ctx = neptune.Context() self.epoch_loss_averager = Averager() self.image_resize = image_resize self.image_every = image_every self.image_nr = image_nr self.use_depth = use_depth
def __init__(self, number_of_batches_per_full_cycle, max_lr, enabled=1, momentum_range=(0.95, 0.8), prcnt_annihilate=10, div=10): super().__init__() self.enabled = enabled self.number_of_batches_per_full_cycle = number_of_batches_per_full_cycle self.max_lr = max_lr self.momentum_range = momentum_range self.prcnt_annihilate = prcnt_annihilate self.div = div self.ctx = neptune.Context()
def main(): """ Load data and train a model on it. """ context = neptune.Context() context.integrate_with_tensorflow() final_train_channel = context.create_channel('final_train_accuracy', neptune.ChannelType.NUMERIC) final_test_channel = context.create_channel('final_test_accuracy', neptune.ChannelType.NUMERIC) args = neptune_args(context) print('args:\n', args) random.seed(args.seed) train_set, test_set = split_dataset(read_dataset(args.omniglot_src)) train_set = list(augment_dataset(train_set)) test_set = list(test_set) model = ProgressiveOmniglotModel(args.classes, **model_kwargs(args)) config = tf.ConfigProto() config.gpu_options.allow_growth = args.allow_growth with tf.Session(config=config) as sess: if not args.pretrained: print('Training...') train(sess, model, train_set, test_set, args.checkpoint, **train_kwargs(args)) else: print('Restoring from checkpoint...') tf.train.Saver().restore( sess, tf.train.latest_checkpoint(args.checkpoint)) print('Evaluating...') eval_kwargs = evaluate_kwargs(args) final_train_accuracy = evaluate(sess, model, train_set, **eval_kwargs) print('final_train_accuracy:', final_train_accuracy) final_train_channel.send(final_train_accuracy) final_test_accuracy = evaluate(sess, model, test_set, **eval_kwargs) print('final_test_accuracy:', final_test_accuracy) final_test_channel.send(final_test_accuracy)
from attrdict import AttrDict import neptune import numpy as np import pandas as pd from sklearn.externals import joblib from steppy.base import Step, IdentityOperation from steppy.adapter import Adapter, E from common_blocks import augmentation as aug from common_blocks import metrics from common_blocks import models from common_blocks import pipelines from common_blocks import utils from common_blocks import postprocessing CTX = neptune.Context() LOGGER = utils.init_logger() # ______ ______ .__ __. _______ __ _______ _______. # / | / __ \ | \ | | | ____|| | / _____| / | # | ,----'| | | | | \| | | |__ | | | | __ | (----` # | | | | | | | . ` | | __| | | | | |_ | \ \ # | `----.| `--' | | |\ | | | | | | |__| | .----) | # \______| \______/ |__| \__| |__| |__| \______| |_______/ # EXPERIMENT_DIR = '/output/experiment' CLONE_EXPERIMENT_DIR_FROM = '' # When running eval in the cloud specify this as for example /input/SAL-14/output/experiment OVERWRITE_EXPERIMENT_DIR = False DEV_MODE = False
def __init__(self): super().__init__() self.ctx = neptune.Context() self.best_loss = None
import neptune ctx = neptune.Context() def neptune_send_plot(logs): epoch_data = logs[-1] for metrics, value in epoch_data.items(): ctx.channel_send(name=metrics, y=value)