royalzuloo.blogg.se - Ups logger soccer

Makes it easy to save diagnostics, hyperparameter configurations, the Logger ( output_dir=None, output_fname='progress.txt', exp_name=None ) ¶ Note that the keys passed into logger.log_tabular are the same as the keys passed into logger.store.

On lines 61-66, logger.log_tabular and logger.dump_tabular are used to write the epoch diagnostics to file.

On line 58, the computation graph is saved once per epoch via logger.save_state.

On line 54, diagnostics are saved to the logger’s internal state via logger.store.

On lines 42 and 43, tup_tf_saver is used to prepare the logger to save the key elements of the computation graph.

On line 19, logger.save_config is used to save the hyperparameter configuration to a JSON file.

dump_tabular () if _name_ = '_main_' : train_mnist () log_tabular ( 'TotalGradientSteps', ( epoch + 1 ) * steps_per_epoch ) logger. log_tabular ( 'Loss', average_only = True ) logger. log_tabular ( 'Acc', with_min_and_max = True ) logger. save_state ( state_dict = dict (), itr = None ) # Log info about epoch logger. store ( Loss = outs, Acc = outs ) # Save model if ( epoch % save_freq = 0 ) or ( epoch = epochs - 1 ): logger.

setup_tf_saver ( sess, inputs = outs = sess. global_variables_initializer ()) # Setup model saving logger. minimize ( loss ) # Prepare session sess = tf. softmax_cross_entropy ( y, logits ) acc = tf. int32 ) # Define loss function, accuracy, and training op y = tf. argmax ( logits, axis = 1, output_type = tf. int32, shape = ( None ,)) logits = mlp ( x_ph, hidden_sizes = * layers +, activation = tf. reshape ( - 1, 28 * 28 ) / 255.0 # Define inputs & main outputs from computation graph x_ph = tf. save_config ( locals ()) # Load and preprocess MNIST data ( x_train, y_train ), _ = tf. def train_mnist ( steps_per_epoch = 100, epochs = 5, lr = 1e-3, layers = 2, hidden_size = 64, logger_kwargs = dict (), save_freq = 1 ): logger = EpochLogger ( ** logger_kwargs ) logger. dense ( x, units = hidden_sizes, activation = output_activation ) # Simple script for training an MLP on MNIST. dense ( x, units = h, activation = activation ) return tf. tanh, output_activation = None ): for h in hidden_sizes : x = tf. Both contests are scheduled to begin at 1:30 p.m.Import numpy as np import tensorflow as tf import time from import EpochLogger def mlp ( x, hidden_sizes = ( 32 ,), activation = tf. PLU will remain on the road next weekend when they head to Whitman College to take on the Blues for a pair of matches. PLU held a small 12-11 advantage in shot attempts and an 8-3 edge in corner kicks. Keeton Heggerness put the visitors up for good in the 49 th minute on a pass from Robby Guyer and Brandt Kelley scored his second goal in as many days to add some insurance with just six minutes to play.Ī tough, physical game saw the Lutes and Loggers combine for 38 fouls.

PLU's 2-1 lead would hold until halftime, but the Loggers wouldn't go away quietly as they tied the score just two minutes after the break. UPS knotted the game seven minutes later, but it was Schlekewey again in the 29 th minute, this time on an assist from Ryan Griffith.

The Lutes opened the scoring when Rainier Schlekewey took passes from Keeton Heggerness and Archie Caldwell to put the Lutes up 1-0. We'll look to build on this performance as we prepare for Whitman next week." "The level of competition in the crosstown rivalry is always high and our guys were up for the challenge. "The team responded well to a difficult result yesterday," said PLU assistant coach Derek Johnson. – The Pacific Lutheran University men's soccer team finally broke out of its early season funk on Sunday, doubling its goal total through the first three matches and knocking off its rival University of Puget Sound by a 4-2 score.