<s> import theano <EOL> import theano . tensor as T <EOL> from theano . sandbox . rng_mrg import MRG_RandomStreams <EOL> from theano . tensor . nnet . conv import conv2d <EOL> from theano . tensor . signal . downsample import max_pool_2d <EOL> from theano . tensor . shared_randomstreams import RandomStreams <EOL> import numpy as np <EOL> import scipy . io <EOL> import time <EOL> import sys <EOL> import logging <EOL> import copy <EOL> from toolbox import * <EOL> from modelbase import * <EOL> class CNN ( ModelSLBase ) : <EOL> def __init__ ( self , data , hp ) : <EOL> super ( CNN , self ) . __init__ ( self . __class__ . __name__ , data , hp ) <EOL> self . n_h = <NUM_LIT> <EOL> self . params = Parameters ( ) <EOL> n_x = self . data [ '<STR_LIT>' ] <EOL> n_y = self . data [ '<STR_LIT>' ] <EOL> n_h = self . n_h <EOL> scale = hp . init_scale <EOL> if hp . load_model and os . path . isfile ( self . filename ) : <EOL> self . params . load ( self . filename ) <EOL> else : <EOL> with self . params : <EOL> w = shared_normal ( ( <NUM_LIT:32> , <NUM_LIT:1> , <NUM_LIT:3> , <NUM_LIT:3> ) , scale = scale ) <EOL> w2 = shared_normal ( ( <NUM_LIT:64> , <NUM_LIT:32> , <NUM_LIT:3> , <NUM_LIT:3> ) , scale = scale ) <EOL> w3 = shared_normal ( ( <NUM_LIT> , <NUM_LIT:64> , <NUM_LIT:3> , <NUM_LIT:3> ) , scale = scale ) <EOL> w4 = shared_normal ( ( <NUM_LIT> * <NUM_LIT:3> * <NUM_LIT:3> , <NUM_LIT> ) , scale = scale ) <EOL> w_o = shared_normal ( ( <NUM_LIT> , n_y ) , scale = scale ) <EOL> def model ( X , params , p_drop_conv , p_drop_hidden ) : <EOL> l1a = rectify ( conv2d ( X , params . w , border_mode = '<STR_LIT>' ) ) <EOL> l1 = max_pool_2d ( l1a , ( <NUM_LIT:2> , <NUM_LIT:2> ) ) <EOL> l1 = dropout ( l1 , p_drop_conv ) <EOL> l2a = rectify ( conv2d ( l1 , params . w2 ) ) <EOL> l2 = max_pool_2d ( l2a , ( <NUM_LIT:2> , <NUM_LIT:2> ) ) <EOL> l2 = dropout ( l2 , p_drop_conv ) <EOL> l3a = rectify ( conv2d ( l2 , params . w3 ) ) <EOL> l3b = max_pool_2d ( l3a , ( <NUM_LIT:2> , <NUM_LIT:2> ) ) <EOL> l3 = T . flatten ( l3b , outdim = <NUM_LIT:2> ) <EOL> l3 = dropout ( l3 , p_drop_conv ) <EOL> l4 = rectify ( T . dot ( l3 , params . w4 ) ) <EOL> l4 = dropout ( l4 , p_drop_hidden ) <EOL> py_x = softmax ( T . dot ( l4 , params . w_o ) ) <EOL> return py_x <EOL> x = T . reshape ( self . X , ( - <NUM_LIT:1> , <NUM_LIT:1> , <NUM_LIT> , <NUM_LIT> ) ) <EOL> noise_py_x = model ( x , self . params , <NUM_LIT> , <NUM_LIT:0.5> ) <EOL> cost = T . sum ( T . nnet . categorical_crossentropy ( noise_py_x , self . Y ) ) <EOL> pyx = model ( x , self . params , <NUM_LIT:0.> , <NUM_LIT:0.> ) <EOL> map_pyx = T . argmax ( pyx , axis = <NUM_LIT:1> ) <EOL> error_map_pyx = T . sum ( T . neq ( map_pyx , T . argmax ( self . Y , axis = <NUM_LIT:1> ) ) ) <EOL> self . compile ( cost , error_map_pyx ) </s>
