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src/p/y/pylearn2-HEAD/pylearn2/models/dbm/layer.py   pylearn2(Download)
 
from pylearn2.expr.nnet import sigmoid_numpy
from pylearn2.expr.probabilistic_max_pooling import max_pool_channels, max_pool_b01c, max_pool, max_pool_c01b
from pylearn2.linear.conv2d import make_random_conv2D, make_sparse_random_conv2D
from pylearn2.linear.conv2d_c01b import setup_detector_layer_c01b
 
        z = self.transformer.lmul(state_below) + self.b
        p, h, p_sample, h_sample = max_pool_channels(z,
                self.pool_size, msg, theano_rng)
 
        z = T.alloc(0., self.dbm.batch_size, self.detector_layer_dim).astype(self.b.dtype) + \
                self.b.dimshuffle('x', 0)
        rval = max_pool_channels(z = z,
                pool_size = self.pool_size)
        return rval
 
        p_exp, h_exp, p_sample, h_sample = max_pool_channels(
                z = default_z,
                pool_size = self.pool_size,
                theano_rng = theano_rng)
        default_z = T.alloc(self.b, num_examples, self.detector_layer_dim)
 
        p_exp, h_exp, p_sample, h_sample = max_pool_channels(z=default_z,
                                                             pool_size=self.pool_size,
                                                             theano_rng=theano_rng)

src/p/y/pylearn2-HEAD/pylearn2/models/mlp.py   pylearn2(Download)
 
from pylearn2.costs.mlp import Default
from pylearn2.expr.probabilistic_max_pooling import max_pool_channels
from pylearn2.linear import conv2d
from pylearn2.linear.matrixmul import MatrixMul
        if self.layer_name is not None:
            z.name = self.layer_name + '_z'
        p, h = max_pool_channels(z, self.pool_size)
 
        p.name = self.layer_name + '_p_'

src/p/y/pylearn2-HEAD/pylearn2/expr/tests/test_probabilistic_max_pooling.py   pylearn2(Download)
from pylearn2.expr.probabilistic_max_pooling import max_pool_channels_python
from pylearn2.expr.probabilistic_max_pooling import max_pool
from pylearn2.expr.probabilistic_max_pooling import max_pool_channels
from pylearn2.expr.probabilistic_max_pooling import max_pool_b01c
from pylearn2.expr.probabilistic_max_pooling import max_pool_c01b