<s> from __future__ import print_function <EOL> from pylearn2 . utils import py_integer_types <EOL> from pylearn2 . utils import serial <EOL> from pylearn2 . config import yaml_parse <EOL> import sys <EOL> _ , model_path = sys . argv <EOL> model = serial . load ( model_path ) <EOL> src = model . dataset_yaml_src <EOL> batch_size = <NUM_LIT:100> <EOL> model . set_batch_size ( batch_size ) <EOL> assert src . find ( '<STR_LIT:train>' ) != - <NUM_LIT:1> <EOL> test = yaml_parse . load ( src ) <EOL> test = test . get_test_set ( ) <EOL> assert test . X . shape [ <NUM_LIT:0> ] == <NUM_LIT> <EOL> test . X = test . X . astype ( '<STR_LIT>' ) <EOL> import theano . tensor as T <EOL> Xb = model . get_input_space ( ) . make_batch_theano ( ) <EOL> Xb . name = '<STR_LIT>' <EOL> yb = model . get_output_space ( ) . make_batch_theano ( ) <EOL> yb . name = '<STR_LIT>' <EOL> ymf = model . fprop ( Xb ) <EOL> ymf . name = '<STR_LIT>' <EOL> from theano import function <EOL> yl = T . argmax ( yb , axis = <NUM_LIT:1> ) <EOL> mf1acc = <NUM_LIT:1.> - T . neq ( yl , T . argmax ( ymf , axis = <NUM_LIT:1> ) ) . mean ( ) <EOL> batch_acc = function ( [ Xb , yb ] , [ mf1acc ] ) <EOL> assert test . X . shape [ <NUM_LIT:0> ] % batch_size == <NUM_LIT:0> <EOL> def accs ( ) : <EOL> mf1_accs = [ ] <EOL> assert isinstance ( test . X . shape [ <NUM_LIT:0> ] , py_integer_types ) <EOL> assert isinstance ( batch_size , py_integer_types ) <EOL> iterator = test . iterator ( mode = '<STR_LIT>' , <EOL> batch_size = batch_size , <EOL> data_specs = model . cost_from_X_data_specs ( ) ) <EOL> for item in iterator : <EOL> x_arg , y_arg = item <EOL> mf1_accs . append ( batch_acc ( x_arg , y_arg ) [ <NUM_LIT:0> ] ) <EOL> return sum ( mf1_accs ) / float ( len ( mf1_accs ) ) <EOL> result = accs ( ) <EOL> print ( <NUM_LIT:1.> - result ) </s>
