<s> import numpy as np <EOL> import theano as th <EOL> from kaggle_utils import multiclass_log_loss <EOL> from examples . utils import make_progressbar <EOL> def validate ( dataset_x , dataset_y , model , epoch , batch_size ) : <EOL> progress = make_progressbar ( '<STR_LIT>' . format ( epoch ) , len ( dataset_x ) ) <EOL> progress . start ( ) <EOL> logloss = <NUM_LIT:0.> <EOL> for j in range ( ( dataset_x . shape [ <NUM_LIT:0> ] + batch_size - <NUM_LIT:1> ) // batch_size ) : <EOL> mini_batch_input = dataset_x [ j * batch_size : ( j + <NUM_LIT:1> ) * batch_size ] . astype ( th . config . floatX ) <EOL> mini_batch_targets = dataset_y [ j * batch_size : ( j + <NUM_LIT:1> ) * batch_size ] . astype ( th . config . floatX ) <EOL> mini_batch_prediction = model . forward ( mini_batch_input ) <EOL> logloss += multiclass_log_loss ( mini_batch_targets , mini_batch_prediction , normalize = False ) <EOL> progress . update ( j * batch_size + len ( mini_batch_input ) ) <EOL> progress . finish ( ) <EOL> print ( "<STR_LIT>" . format ( epoch , logloss / dataset_x . shape [ <NUM_LIT:0> ] ) ) </s>
