Python filter_acts.FilterActs方法代碼示例

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def grad(self, inputs, g_outputs):
        """
        .. todo::

            WRITEME
        """
        hid_acts, filters, output_shape = inputs
        g_images, = g_outputs
        g_images = as_cuda_ndarray_variable(g_images)
        assert not isinstance(g_images, list)

        global FilterActs
        global WeightActs
        if FilterActs is None:
            from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs
            from pylearn2.sandbox.cuda_convnet.weight_acts import WeightActs

        g_filters = WeightActs(stride=self.stride,
                partial_sum=self.partial_sum, pad=self.pad)(
                        g_images, hid_acts, filters.shape[1:3])[0]
        assert not isinstance(g_filters, list)
        g_hid_acts = FilterActs(stride=self.stride, pad=self.pad,
                partial_sum=self.partial_sum)(g_images, filters)

        return [g_hid_acts, g_filters, DisconnectedType()()] 

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def __init__(self, input_layer, n_filters, filter_size, weights_std, init_bias_value, stride=1, nonlinearity=layers.rectify, dropout=0., partial_sum=None, pad=0, untie_biases=False):
        """
        Only the valid border mode is supported.

        n_filters should be a multiple of 16
        """
        self.input_layer = input_layer
        self.n_filters = n_filters
        self.filter_size = filter_size
        self.weights_std = np.float32(weights_std)
        self.init_bias_value = np.float32(init_bias_value)
        self.stride = stride
        self.nonlinearity = nonlinearity
        self.dropout = dropout
        self.partial_sum = partial_sum
        self.pad = pad
        self.untie_biases = untie_biases
        # if untie_biases == True, each position in the output map has its own bias (as opposed to having the same bias everywhere for a given filter)
        self.mb_size = self.input_layer.mb_size

        self.input_shape = self.input_layer.get_output_shape()

        self.filter_shape = (self.input_shape[0], filter_size, filter_size, n_filters)

        self.W = layers.shared_single(4) # theano.shared(np.random.randn(*self.filter_shape).astype(np.float32) * self.weights_std)

        if self.untie_biases:
            self.b = layers.shared_single(3)
        else:
            self.b = layers.shared_single(1) # theano.shared(np.ones(n_filters).astype(np.float32) * self.init_bias_value)

        self.params = [self.W, self.b]
        self.bias_params = [self.b]
        self.reset_params()

        self.filter_acts_op = FilterActs(stride=self.stride, partial_sum=self.partial_sum, pad=self.pad) 

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def test_reject_rect():
    for cls in (FilterActs, ImageActs):
        # Tests that running FilterActs with a non-square
        # kernel is an error
        rng = np.random.RandomState([2012, 10, 9])
        batch_size = 5
        rows = 10
        cols = 9
        channels = 3
        filter_rows = 4
        filter_cols = filter_rows + 1
        num_filters = 6

        images = shared(rng.uniform(-1., 1., (channels, rows, cols,
            batch_size)).astype('float32'), name='images')
        filters = shared(rng.uniform(-1., 1., (channels, filter_rows,
            filter_cols, num_filters)).astype('float32'), name='filters')

        gpu_images = gpu_from_host(images)
        gpu_filters = gpu_from_host(filters)

        if cls is ImageActs:
            output = cls()(gpu_images, gpu_filters,
                        as_tensor_variable((rows, cols)))
        else:
            output = cls()(gpu_images, gpu_filters)

        f = function([], output)
        try:
            output = f()
        except ValueError:
            continue
        assert False 

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def test_reject_bad_filt_number():
    for cls in (FilterActs, ImageActs):
        # Tests that running FilterActs with a # of filters per
        # group that is not 16 is an error
        rng = np.random.RandomState([2012, 10, 9])
        batch_size = 5
        rows = 10
        cols = 9
        channels = 3
        filter_rows = 4
        filter_cols = filter_rows
        num_filters = 6

        images = shared(rng.uniform(-1., 1., (channels, rows, cols,
            batch_size)).astype('float32'), name='images')
        filters = shared(rng.uniform(-1., 1., (channels, filter_rows,
            filter_cols, num_filters)).astype('float32'), name='filters')

        gpu_images = gpu_from_host(images)
        gpu_filters = gpu_from_host(filters)

        if cls is ImageActs:
            output = cls()(gpu_images, gpu_filters,
                           as_tensor_variable((rows, cols)))
        else:
            output = cls()(gpu_images, gpu_filters)
        f = function([], output)
        try:
            output = f()
        except ValueError:
            continue
        assert False 

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def make_funcs(batch_size, rows, cols, channels, filter_rows,
        num_filters):
    rng = np.random.RandomState([2012,10,9])

    filter_cols = filter_rows

    base_image_value = rng.uniform(-1., 1., (channels, rows, cols,
        batch_size)).astype('float32')
    base_filters_value = rng.uniform(-1., 1., (channels, filter_rows,
        filter_cols, num_filters)).astype('float32')
    images = shared(base_image_value)
    filters = shared(base_filters_value, name='filters')

    # bench.py should always be run in gpu mode so we should not need a gpu_from_host here
    output = FilterActs()(images, filters)

    output_shared = shared( output.eval() )

    cuda_convnet = function([], updates = { output_shared : output } )
    cuda_convnet.name = 'cuda_convnet'

    images_bc01v = base_image_value.transpose(3,0,1,2)
    filters_bc01v = base_filters_value.transpose(3,0,1,2)
    filters_bc01v = filters_bc01v[:,:,::-1,::-1]

    images_bc01 = shared(images_bc01v)
    filters_bc01 = shared(filters_bc01v)

    output_conv2d = conv2d(images_bc01, filters_bc01,
            border_mode='valid', image_shape = images_bc01v.shape,
            filter_shape = filters_bc01v.shape)

    output_conv2d_shared = shared(output_conv2d.eval())

    baseline = function([], updates = { output_conv2d_shared : output_conv2d } )
    baseline.name = 'baseline'

    return cuda_convnet, baseline 

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def fprop(self, input):
        
        # we reduce the precision of parameters for the computations
        self.w_comp = apply_format(self.format, self.W, self.comp_precision, self.w_range)
        self.b_comp = apply_format(self.format, self.b, self.comp_precision, self.b_range)
        
        input = input.reshape(self.image_shape)
        
        # convolution
        input_shuffled = input.dimshuffle(1, 2, 3, 0) # bc01 to c01b
        filters_shuffled = self.w_comp.dimshuffle(1, 2, 3, 0) *self.scale # bc01 to c01b
        conv_op = FilterActs(stride=self.filter_stride, partial_sum=self.partial_sum,pad = self.zero_pad)
        contiguous_input = gpu_contiguous(input_shuffled)
        contiguous_filters = gpu_contiguous(filters_shuffled)
        conv_out_shuffled = conv_op(contiguous_input, contiguous_filters)
        
        # downsample each feature map individually, using maxpooling
        # pooled_out = downsample.max_pool_2d(input=conv_out,
        #                                     ds=poolsize, ignore_border=True)
        pool_op = MaxPool(ds=self.pool_shape, stride=self.pool_stride)
        pooled_out_shuffled = pool_op(conv_out_shuffled)
        pooled_out = pooled_out_shuffled.dimshuffle(3, 0, 1, 2) # c01b to bc01
        
        # bias
        pooled_out = apply_format(self.format, pooled_out + self.b_comp.dimshuffle('x', 0, 'x', 'x')*self.scale, self.comp_precision, self.z_range)
        
        # activation
        pooled_out = self.activation(pooled_out)
        pooled_out = apply_format(self.format, pooled_out.flatten(2), self.comp_precision, self.y_range)
        
        return pooled_out 

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def __init__(self, numpy_rng=None, input = None, filter_shape=(2, 1, 5, 5),
                 poolsize=(1, 1), activation=T.nnet.sigmoid,
                 flatten = False, use_fast = False):

        self.type = 'conv'

        self.input = input

        self.filter_shape = filter_shape
        self.poolsize = poolsize

        self.activation = activation
        self.flatten = flatten

        fan_in = numpy.prod(filter_shape[1:])
        fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) /
                   numpy.prod(poolsize))
        # initialize weights with random weights
        W_bound = numpy.sqrt(6. / (fan_in + fan_out))
        initial_W = numpy.asarray( numpy_rng.uniform(
                                   low=-W_bound, high=W_bound,
                                   size=filter_shape),
                                   dtype=theano.config.floatX)

        if activation == T.nnet.sigmoid:
            initial_W *= 4
        W = theano.shared(value = initial_W, name = 'W')

        self.W = W
        # the bias is a 1D tensor -- one bias per output feature map
        b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
        self.b = theano.shared(value=b_values, name='b')

        # convolve input feature maps with filters
        if use_fast:
            from theano.sandbox.cuda.basic_ops import gpu_contiguous
            from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs
            from pylearn2.sandbox.cuda_convnet.pool import MaxPool
       
            input_shuffled = self.input.dimshuffle(1, 2, 3, 0) # bc01 to c01b
            filters_shuffled = self.W.dimshuffle(1, 2, 3, 0) # bc01 to c01b
            conv_op = FilterActs()
            contiguous_input = gpu_contiguous(input_shuffled)
            contiguous_filters = gpu_contiguous(filters_shuffled)
            conv_out_shuffled = conv_op(contiguous_input, contiguous_filters)
            y_out_shuffled = activation(conv_out_shuffled + self.b.dimshuffle(0, 'x', 'x', 'x'))
            pool_op = MaxPool(ds=poolsize[0], stride=poolsize[0])
            self.output = pool_op(y_out_shuffled).dimshuffle(3, 0, 1, 2)
        else: 
            conv_out = conv.conv2d(input=self.input, filters=self.W,
                filter_shape=filter_shape)

            y_out = activation(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
            # downsample each feature map individually, using maxpooling
            self.output = downsample.max_pool_2d(input=y_out,
                                             ds=poolsize, ignore_border=True)
        if self.flatten:
            self.output = self.output.flatten(2)

        self.params = [self.W, self.b] 

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def make_funcs(batch_size, rows, cols, channels, filter_rows,
        num_filters):
    rng = np.random.RandomState([2012,10,9])

    filter_cols = filter_rows

    base_image_value = rng.uniform(-1., 1., (channels, rows, cols,
        batch_size)).astype('float32')
    base_filters_value = rng.uniform(-1., 1., (channels, filter_rows,
        filter_cols, num_filters)).astype('float32')
    images = shared(base_image_value)
    filters = shared(base_filters_value, name='filters')

    # bench.py should always be run in gpu mode so we should not need a gpu_from_host here
    layer_1_detector = FilterActs()(images, filters)

    layer_1_pooled_fake = layer_1_detector[:,0:layer_1_detector.shape[0]:2,
            0:layer_1_detector.shape[1]:2, :]

    base_filters2_value = rng.uniform(-1., 1., (num_filters, filter_rows,
        filter_cols, num_filters)).astype('float32')
    filters2 = shared(base_filters_value, name='filters')

    layer_2_detector = FilterActs()(images, filters2)

    output = layer_2_detector

    output_shared = shared( output.eval() )

    cuda_convnet = function([], updates = { output_shared : output } )
    cuda_convnet.name = 'cuda_convnet'

    images_bc01 = base_image_value.transpose(3,0,1,2)
    filters_bc01 = base_filters_value.transpose(3,0,1,2)
    filters_bc01 = filters_bc01[:,:,::-1,::-1]

    images_bc01 = shared(images_bc01)
    filters_bc01 = shared(filters_bc01)

    output_conv2d = conv2d(images_bc01, filters_bc01,
            border_mode='valid')

    output_conv2d_shared = shared(output_conv2d.eval())

    baseline = function([], updates = { output_conv2d_shared : output_conv2d } )
    baseline.name = 'baseline'

    return cuda_convnet, baseline 

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def test_filter_acts_strided():

    # Tests that FilterActs with all possible strides 

    rng = np.random.RandomState([2012,10,9])

    #Each list in shape_list : 
    #[img_shape,filter_shape]
    #[(channels, rows, cols, batch_size),(channels, filter_rows, filter_cols, num_filters)]
    shape_list = [[(1, 7, 8, 5),     (1, 2, 2, 16)],
                  [(3, 7, 8, 5),     (3, 3, 3, 16)],
                  [(16, 11, 11, 4),  (16, 4, 4, 16)], 
                  [(3, 20, 20, 3),   (3, 5, 5, 16)],
                  [(3, 21, 21, 3),   (3, 6, 6, 16)],
                  ]

    for test_idx in xrange(len(shape_list)):
        images = rng.uniform(-1., 1., shape_list[test_idx][0]).astype('float32')
        filters = rng.uniform(-1., 1., shape_list[test_idx][1]).astype('float32')
        gpu_images = float32_shared_constructor(images,name='images')
        gpu_filters = float32_shared_constructor(filters,name='filters')
        print("test case %d..."%(test_idx+1))
        
        for ii in xrange(filters.shape[1]):
            stride = ii + 1
            
            output = FilterActs(stride=stride)(gpu_images, gpu_filters)
            output = host_from_gpu(output)
            f = function([], output)
            output_val = f()
        
            output_python = FilterActs_python(images,filters,stride)
                        
            if np.abs(output_val - output_python).max() > 8.6e-6:
                assert type(output_val) == type(output_python)
                assert output_val.dtype == output_python.dtype
                if output_val.shape != output_python.shape:
                    print('cuda-convnet shape: ',output_val.shape)
                    print('python conv shape: ',output_python.shape)
                    assert False
                err = np.abs(output_val - output_python)
                print('stride %d'%stride)
                print('absolute error range: ', (err.min(), err.max()))
                print('mean absolute error: ', err.mean())
                print('cuda-convnet value range: ', (output_val.min(), output_val.max()))
                print('python conv value range: ', (output_python.min(), output_python.max()))
                #assert False 
        #print "pass" 

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def test_match_valid_conv_strided():

    # Tests that running FilterActs with stride is the same as running
    # theano's conv2D in valid mode and then downsampling

    rng = np.random.RandomState([2012,10,9])

    batch_size = 5
    rows = 9
    cols = 9
    channels = 3
    filter_rows = 3
    filter_cols = filter_rows
    stride = 3
    num_filters = 16

    images = shared(rng.uniform(-1., 1., (channels, rows, cols,
        batch_size)).astype('float32'), name='images')
    filters = shared(rng.uniform(-1., 1., (channels, filter_rows,
        filter_cols, num_filters)).astype('float32'), name='filters')

    gpu_images = gpu_from_host(images)
    gpu_filters = gpu_from_host(filters)

    output = FilterActs(stride=stride)(gpu_images, gpu_filters)
    output = host_from_gpu(output)

    images_bc01 = images.dimshuffle(3,0,1,2)
    filters_bc01 = filters.dimshuffle(3,0,1,2)
    filters_bc01 = filters_bc01[:,:,::-1,::-1]

    output_conv2d = conv2d(images_bc01, filters_bc01,
            border_mode='valid', subsample=(stride, stride))

    output_conv2d_orig = output_conv2d.dimshuffle(1,2,3,0)
    output_conv2d = output_conv2d_orig  # [:, ::stride, ::stride, :]
    f = function([], [output, output_conv2d, output_conv2d_orig])

    output, output_conv2d, output_conv2d_orig = f()

    warnings.warn("""test_match_valid_conv success criterion is not very strict. Can we verify that this is OK?
                     One possibility is that theano is numerically unstable and Alex's code is better.
                     Probably theano CPU 64 bit is OK but it's worth checking the others.""")
    if np.abs(output - output_conv2d).max() > 2.4e-6:
        assert type(output) == type(output_conv2d)
        assert output.dtype == output_conv2d.dtype
        if output.shape != output_conv2d.shape:
            print('cuda-convnet shape: ',output.shape)
            print('theano shape: ',output_conv2d.shape)
            assert False
        err = np.abs(output - output_conv2d)
        print('absolute error range: ', (err.min(), err.max()))
        print('mean absolute error: ', err.mean())
        print('cuda-convnet value range: ', (output.min(), output.max()))
        print('theano value range: ', (output_conv2d.min(), output_conv2d.max()))
        assert False 

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def test_image_acts_strided():

    # Tests that running FilterActs with all possible strides 

    rng = np.random.RandomState([2012,10,9])

    #Each list in shape_list : 
    #[img_shape,filter_shape]
    #[(channels, rows, cols, batch_size),(channels, filter_rows, filter_cols, num_filters)]
    shape_list = [[(1, 7, 8, 5),     (1, 2, 2, 16)],
                  [(3, 7, 8, 5),     (3, 3, 3, 16)],
                  [(16, 11, 11, 4),  (16, 4, 4, 16)], 
                  [(3, 20, 20, 3),   (3, 5, 5, 16)],
                  [(3, 21, 21, 3),   (3, 6, 6, 16)],
                  ]

    for test_idx in xrange(len(shape_list)):
        images = rng.uniform(-1., 1., shape_list[test_idx][0]).astype('float32')
        filters = rng.uniform(-1., 1., shape_list[test_idx][1]).astype('float32')
        gpu_images = float32_shared_constructor(images,name='images')
        gpu_filters = float32_shared_constructor(filters,name='filters')
        print("test case %d..."%(test_idx+1))
        
        for ii in xrange(filters.shape[1]):
            stride = ii + 1
                   
            output_python = FilterActs_python(images,filters,stride)
            hidacts = rng.uniform(-1., 1., output_python.shape).astype('float32')
            gpu_hidacts = float32_shared_constructor(hidacts,name='hidacts')
            Img_output_python = ImageActs_python(filters,hidacts,stride,(images.shape[1], images.shape[2]))            
            
            Img_output = ImageActs(stride=stride)(gpu_hidacts, gpu_filters, as_tensor_variable((images.shape[1], images.shape[2])))
            Img_output = host_from_gpu(Img_output)
            f = function([], Img_output)
            Img_output_val = f()
            
            warnings.warn("""test_image_acts_strided success criterion is not very strict.""")
            
            if np.abs(Img_output_val - Img_output_python).max() > 2.1e-5:
                assert type(Img_output_val) == type(Img_output_python)
                assert Img_output_val.dtype == Img_output_python.dtype
                if Img_output_val.shape != Img_output_python.shape:
                    print('cuda-convnet shape: ',Img_output_val.shape)
                    print('python conv shape: ',Img_output_python.shape)
                    assert False
                err = np.abs(Img_output_val - Img_output_python)
                print('stride %d'%stride)
                print('absolute error range: ', (err.min(), err.max()))
                print('mean absolute error: ', err.mean())
                print('cuda-convnet value range: ', (Img_output_val.min(), Img_output_val.max()))
                print('python conv value range: ', (Img_output_python.min(), Img_output_python.max()))
                #assert False 
        #print "pass" 

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def lmul(self, x):
        """
        .. todo::

            WRITEME properly

        dot(x, A)
        aka, do convolution with input image x
        """

        check_cuda(str(type(self)) + ".lmul")

        cpu = 'Cuda' not in str(type(x))

        if cpu:
            x = gpu_from_host(x)

        # x must be formatted as channel, topo dim 0, topo dim 1, batch_index
        # for use with FilterActs
        assert x.ndim == 4
        x_axes = self.input_axes
        assert len(x_axes) == 4

        op_axes = ('c', 0, 1, 'b')

        if tuple(x_axes) != op_axes:
            x = x.dimshuffle(*[x_axes.index(axis) for axis in op_axes])

        x = gpu_contiguous(x)

        # Patch old pickle files.
        if not hasattr(self, 'kernel_stride'):
            self.kernel_stride = (1, 1)
        rval = FilterActs(self.pad, self.partial_sum, self.kernel_stride[0])(
            x,
            self._filters
        )

        # Format the output based on the output space
        rval_axes = self.output_axes
        assert len(rval_axes) == 4

        if cpu:
            rval = host_from_gpu(rval)

        if tuple(rval_axes) != op_axes:
            rval = rval.dimshuffle(*[op_axes.index(axis)
                                     for axis in rval_axes])

        return rval 

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def __init__(self,
                 input_layer,
                 n_filters,
                 filter_size,
                 weights_std,
                 stride=1,
                 nonlinearity=layers.rectify,
                 dropout=0.,
                 partial_sum=None,
                 pad=0,
                 trainable=True):
        """
        Only the valid border mode is supported.

        n_filters should be a multiple of 16
        """
        self.input_layer = input_layer
        self.input_shape = self.input_layer.get_output_shape()
        self.n_filters = n_filters
        n_channels = self.input_shape[0]
        self.n_channels = n_channels
        self.filter_size = filter_size
        self.weights_std = numpy.float32(weights_std)
        self.stride = stride
        self.nonlinearity = nonlinearity
        self.dropout = dropout
        self.partial_sum = partial_sum
        self.pad = pad
        self.mb_size = self.input_layer.mb_size

        self.data_order = layers.data_order.type2

        assert (len(self.input_layer.get_output_shape()) == 4), \
            'Input must have 4 dimensions.'

        assert (self.input_layer.data_order == self.data_order), \
            'Input data order does not match this layer\'s data order.'

        self.filter_shape = (n_channels, filter_size, filter_size, n_filters)

        self.trainable = trainable
        self.W = layers.shared_single(4)

        self.params = [self.W]
        self.reset_params()

        self.filter_acts_op = FilterActs(stride=self.stride,
                                         partial_sum=self.partial_sum,
                                         pad=self.pad) 

# 需要導入模塊: from pylearn2.sandbox.cuda_convnet import filter_acts [as 別名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 別名]
def dropout_fprop(self, input):
        
        # we reduce the precision of parameters for the computations
        self.fixed_W = apply_format(self.format, self.W, self.comp_precision, self.w_range)
        self.fixed_b = apply_format(self.format, self.b, self.comp_precision, self.b_range)
        
        # create the dropout mask
        # The cast is important because
        # int * float32 = float64 which pulls things off the gpu
        
        srng = T.shared_randomstreams.RandomStreams(self.rng.randint(999999))
        self.mask = T.cast(srng.binomial(n=1, p=self.p, size=T.shape(input)), theano.config.floatX)
        input = input * self.mask
        
        self.fixed_x = input.reshape(self.image_shape)

        # convolution
        input_shuffled = self.fixed_x.dimshuffle(1, 2, 3, 0) # bc01 to c01b
        filters_shuffled = self.fixed_W.dimshuffle(1, 2, 3, 0) # bc01 to c01b
        conv_op = FilterActs(stride=self.filter_stride, partial_sum=self.partial_sum,pad = self.zero_pad) # augment partial sum -> use less memory but slower
        contiguous_input = gpu_contiguous(input_shuffled)
        contiguous_filters = gpu_contiguous(filters_shuffled)
        conv_out_shuffled = conv_op(contiguous_input, contiguous_filters)
        
        self.z = conv_out_shuffled.dimshuffle(3, 0, 1, 2) # c01b to bc01
        self.fixed_z = apply_format(self.format, self.z, self.comp_precision, self.z_range) 
        
        conv_out_shuffled = self.fixed_z.dimshuffle(1, 2, 3, 0) # bc01 to c01b
        conv_out_shuffled = gpu_contiguous(conv_out_shuffled)
        
        # downsample each feature map individually, using maxpooling
        # pooled_out = downsample.max_pool_2d(input=conv_out,
        #                                     ds=poolsize, ignore_border=True)
        pool_op = MaxPool(ds=self.pool_shape, stride=self.pool_stride)
        pooled_out_shuffled = pool_op(conv_out_shuffled)
        pooled_out = pooled_out_shuffled.dimshuffle(3, 0, 1, 2) # c01b to bc01
        
        # bias
        self.u = pooled_out + self.fixed_b.dimshuffle('x', 0, 'x', 'x')
        self.fixed_u =  apply_format(self.format, self.u, self.comp_precision, self.z_range)
        
        # activation
        self.y = self.activation(self.fixed_u).flatten(2)
        self.fixed_y = apply_format(self.format, self.y, self.comp_precision, self.y_range)
        
        return self.fixed_y