#!/usr/bin/env python
# coding:utf-8
# vi:tabstop=4:shiftwidth=4:expandtab:sts=4

import theano
import theano.tensor as T
import lasagne
from deepstacks.lasagne import curr_batchsize

def classify(target,r=1.0):
    return (
            (0,0,0,0,0,0,{
                'equal':[target,'classify',lambda x,y:r*lasagne.objectives.categorical_crossentropy(x,y),],
                }),
            (0,0,0,0,0,0,{
                'nonlinearity':lambda x:T.argmax(x, axis=1),'shape':(curr_batchsize,),
                'watch':[target,'val:accuracy',T.eq],
                }),
            )


642dc1a187b4e3b695491f0b00d16921665ed83a
<mask token>

<mask token>


def classify(target, r=1.0):
    return (0, 0, 0, 0, 0, 0, {'equal': [target, 'classify', lambda x, y: r *
        lasagne.objectives.categorical_crossentropy(x, y)]}), (0, 0, 0, 0, 
        0, 0, {'nonlinearity': lambda x: T.argmax(x, axis=1), 'shape': (
        curr_batchsize,), 'watch': [target, 'val:accuracy', T.eq]})

import theano
import theano.tensor as T
import lasagne
from deepstacks.lasagne import curr_batchsize


def classify(target, r=1.0):
    return (0, 0, 0, 0, 0, 0, {'equal': [target, 'classify', lambda x, y: r *
        lasagne.objectives.categorical_crossentropy(x, y)]}), (0, 0, 0, 0, 
        0, 0, {'nonlinearity': lambda x: T.argmax(x, axis=1), 'shape': (
        curr_batchsize,), 'watch': [target, 'val:accuracy', T.eq]})

#!/usr/bin/env python
# coding:utf-8
# vi:tabstop=4:shiftwidth=4:expandtab:sts=4

import theano
import theano.tensor as T
import lasagne
from deepstacks.lasagne import curr_batchsize

def classify(target,r=1.0):
    return (
            (0,0,0,0,0,0,{
                'equal':[target,'classify',lambda x,y:r*lasagne.objectives.categorical_crossentropy(x,y),],
                }),
            (0,0,0,0,0,0,{
                'nonlinearity':lambda x:T.argmax(x, axis=1),'shape':(curr_batchsize,),
                'watch':[target,'val:accuracy',T.eq],
                }),
            )

