Python layers.NonlinearityLayer方法代码示例

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def instance_norm(layer, **kwargs):
	"""
	The equivalent of Lasagne's `batch_norm()` convenience method, but for instance normalization.
	Refer: http://lasagne.readthedocs.io/en/latest/modules/layers/normalization.html#lasagne.layers.batch_norm
	"""
	nonlinearity = getattr(layer, 'nonlinearity', None)
	if nonlinearity is not None:
		layer.nonlinearity = identity
	if hasattr(layer, 'b') and layer.b is not None:
		del layer.params[layer.b]
		layer.b = None
	bn_name = (kwargs.pop('name', None) or
			   (getattr(layer, 'name', None) and layer.name + '_bn'))
	layer = InstanceNormLayer(layer, name=bn_name, **kwargs)
	if nonlinearity is not None:
		nonlin_name = bn_name and bn_name + '_nonlin'
		layer = NonlinearityLayer(layer, nonlinearity, name=nonlin_name)
	return layer

# TODO: Add normalization 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def residual_block(l, increase_dim=False, projection=True, first=False):
    """
    Create a residual learning building block with two stacked 3x3 convlayers as in paper
    'Identity Mappings in Deep Residual Networks', Kaiming He et al. 2016 (https://arxiv.org/abs/1603.05027)
    """
    input_num_filters = l.output_shape[1]
    if increase_dim:
        first_stride = (2, 2)
        out_num_filters = input_num_filters * 2
    else:
        first_stride = (1, 1)
        out_num_filters = input_num_filters

    if first:
        # hacky solution to keep layers correct
        bn_pre_relu = l
    else:
        # contains the BN -> ReLU portion, steps 1 to 2
        bn_pre_conv = BatchNormLayer(l)
        bn_pre_relu = NonlinearityLayer(bn_pre_conv, rectify)

    # contains the weight -> BN -> ReLU portion, steps 3 to 5
    conv_1 = batch_norm(ConvLayer(bn_pre_relu, num_filters=out_num_filters, filter_size=(3, 3), stride=first_stride,
                                  nonlinearity=rectify, pad='same', W=he_norm))

    # contains the last weight portion, step 6
    conv_2 = ConvLayer(conv_1, num_filters=out_num_filters, filter_size=(3, 3), stride=(1, 1), nonlinearity=None,
                       pad='same', W=he_norm)

    # add shortcut connections
    if increase_dim:
        # projection shortcut, as option B in paper
        projection = ConvLayer(l, num_filters=out_num_filters, filter_size=(1, 1), stride=(2, 2), nonlinearity=None,
                               pad='same', b=None)
        block = ElemwiseSumLayer([conv_2, projection])
    else:
        block = ElemwiseSumLayer([conv_2, l])

    return block 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def ResNet_FullPreActivation(input_shape=(None, 3, PIXELS, PIXELS), input_var=None, n_classes=10, n=18):
    """
    Adapted from https://github.com/Lasagne/Recipes/tree/master/papers/deep_residual_learning.
    Tweaked to be consistent with 'Identity Mappings in Deep Residual Networks', Kaiming He et al. 2016 (https://arxiv.org/abs/1603.05027)

    Formula to figure out depth: 6n + 2
    """

    # Building the network
    l_in = InputLayer(shape=input_shape, input_var=input_var)

    # first layer, output is 16 x 32 x 32
    l = batch_norm(ConvLayer(l_in, num_filters=16, filter_size=(3, 3), stride=(1, 1), nonlinearity=rectify, pad='same', W=he_norm))

    # first stack of residual blocks, output is 16 x 32 x 32
    l = residual_block(l, first=True)
    for _ in range(1, n):
        l = residual_block(l)

    # second stack of residual blocks, output is 32 x 16 x 16
    l = residual_block(l, increase_dim=True)
    for _ in range(1, n):
        l = residual_block(l)

    # third stack of residual blocks, output is 64 x 8 x 8
    l = residual_block(l, increase_dim=True)
    for _ in range(1, n):
        l = residual_block(l)

    bn_post_conv = BatchNormLayer(l)
    bn_post_relu = NonlinearityLayer(bn_post_conv, rectify)

    # average pooling
    avg_pool = GlobalPoolLayer(bn_post_relu)

    # fully connected layer
    network = DenseLayer(avg_pool, num_units=n_classes, W=HeNormal(), nonlinearity=softmax)

    return network 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def ResLayer(incoming, IB):
    return NL(ESL([IB,incoming]),elu) 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def get_output_for(self, input, deterministic=False, **kwargs):
        if deterministic:
            return self.p*input
        else:
            return theano.ifelse.ifelse(
                T.lt(self._srng.uniform( (1,), 0, 1)[0], self.p),
                input,
                T.zeros(input.shape)
            ) 

# def ResDrop(incoming, IB, p):
    # return NL(ESL([IfElseDropLayer(IB,survival_p=p),incoming]),elu) 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def ResDropNoPre(incoming, IB, p):
    return NL(ESL([IfElseDropLayer(IB,survival_p=p),incoming]),elu) 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def ResDrop(incoming, IB, p):
    return NL(ESL([IfElseDropLayer(IB,survival_p=p),incoming]),elu) 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def ResLayer(incoming, IB):
    return NL(ESL([IB,incoming]),elu)
    
   
# If-else Drop Layer, adopted from Christopher Beckham's recipe:
#  https://github.com/Lasagne/Recipes/pull/67 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def batch_nmsp(layer, beta=init.Constant(-3.0), **kwargs):
    nonlinearity = getattr(layer, 'nonlinearity', None)
    if nonlinearity is not None:
        layer.nonlinearity = nonlinearities.identity
    if hasattr(layer, 'b') and layer.b is not None:
        del layer.params[layer.b]
        layer.b = None
    layer = BatchNormSparseLayer(layer, beta=beta, **kwargs)
    if nonlinearity is not None:
        from lasagne.layers import NonlinearityLayer
        layer = NonlinearityLayer(layer, nonlinearity)
    return layer 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def build_model(input_var):
    net = {}
    net['input'] = InputLayer((None, 3, 224, 224), input_var=input_var)
    net['conv1_1'] = ConvLayer(net['input'], 64, 3, pad=1, flip_filters=False)
    net['conv1_2'] = ConvLayer(net['conv1_1'], 64, 3, pad=1, flip_filters=False)
    net['pool1'] = PoolLayer(net['conv1_2'], 2)
    net['conv2_1'] = ConvLayer(net['pool1'], 128, 3, pad=1, flip_filters=False)
    net['conv2_2'] = ConvLayer(net['conv2_1'], 128, 3, pad=1, flip_filters=False)
    net['pool2'] = PoolLayer(net['conv2_2'], 2)
    net['conv3_1'] = ConvLayer(net['pool2'], 256, 3, pad=1, flip_filters=False)
    net['conv3_2'] = ConvLayer(net['conv3_1'], 256, 3, pad=1, flip_filters=False)
    net['conv3_3'] = ConvLayer(net['conv3_2'], 256, 3, pad=1, flip_filters=False)
    net['pool3'] = PoolLayer(net['conv3_3'], 2)
    net['conv4_1'] = ConvLayer(net['pool3'], 512, 3, pad=1, flip_filters=False)
    net['conv4_2'] = ConvLayer(net['conv4_1'], 512, 3, pad=1, flip_filters=False)
    net['conv4_3'] = ConvLayer(net['conv4_2'], 512, 3, pad=1, flip_filters=False)
    net['pool4'] = PoolLayer(net['conv4_3'], 2)
    net['conv5_1'] = ConvLayer(net['pool4'], 512, 3, pad=1, flip_filters=False)
    net['conv5_2'] = ConvLayer(net['conv5_1'], 512, 3, pad=1, flip_filters=False)
    net['conv5_3'] = ConvLayer(net['conv5_2'], 512, 3, pad=1, flip_filters=False)
    net['pool5'] = PoolLayer(net['conv5_3'], 2)
    net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
    net['fc6_dropout'] = DropoutLayer(net['fc6'], p=0.5)
    net['fc7'] = DenseLayer(net['fc6_dropout'], num_units=4096)
    net['fc7_dropout'] = DropoutLayer(net['fc7'], p=0.5)
    net['fc8'] = DenseLayer(net['fc7_dropout'], num_units=1000, nonlinearity=None)
    net['prob'] = NonlinearityLayer(net['fc8'], softmax)

    return net 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def MDBLOCK(incoming,num_filters,scales,name,nonlinearity):
    return NL(BN(ESL([incoming,
         MDCL(NL(BN(MDCL(NL(BN(incoming,name=name+'bnorm0'),nonlinearity),num_filters,scales,name),name=name+'bnorm1'),nonlinearity),
              num_filters,
              scales,
              name+'2')]),name=name+'bnorm2'),nonlinearity)  
              
# Gaussian Sample Layer for VAE from Tencia Lee 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def InceptionUpscaleLayer(incoming,param_dict,block_name):
    branch = [0]*len(param_dict)
    # Loop across branches
    for i,dict in enumerate(param_dict):
        for j,style in enumerate(dict['style']): # Loop up branch
            branch[i] = TC2D(
                incoming = branch[i] if j else incoming,
                num_filters = dict['num_filters'][j],
                filter_size = dict['filter_size'][j],
                crop = dict['pad'][j] if 'pad' in dict else None,
                stride = dict['stride'][j],
                W = initmethod('relu'),
                nonlinearity = dict['nonlinearity'][j],
                name = block_name+'_'+str(i)+'_'+str(j)) if style=='convolutional'\
            else NL(
                    incoming = lasagne.layers.dnn.Pool2DDNNLayer(
                        incoming = lasagne.layers.Upscale2DLayer(
                            incoming=incoming if j == 0 else branch[i],
                            scale_factor = dict['stride'][j]),
                        pool_size = dict['filter_size'][j],
                        stride = [1,1],
                        mode = dict['mode'][j],
                        pad = dict['pad'][j],
                        name = block_name+'_'+str(i)+'_'+str(j)),
                    nonlinearity = dict['nonlinearity'][j])
                # Apply Batchnorm    
            branch[i] = BN(branch[i],name = block_name+'_bnorm_'+str(i)+'_'+str(j)) if dict['bnorm'][j] else branch[i]
        # Concatenate Sublayers        
            
    return CL(incomings=branch,name=block_name)

# Convenience function to efficiently generate param dictionaries for use with InceptioNlayer 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def ResLayer(incoming, IB,nonlinearity):
    return NL(ESL([IB,incoming]),nonlinearity)


# Inverse autoregressive flow layer 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def build_model():
    '''
    Builds C3D model

    Returns
    -------
    dict
        A dictionary containing the network layers, where the output layer is at key 'prob'
    '''
    net = {}
    net['input'] = InputLayer((None, 3, 16, 112, 112))

    # ----------- 1st layer group ---------------
    net['conv1a'] = Conv3DDNNLayer(net['input'], 64, (3,3,3), pad=1,nonlinearity=lasagne.nonlinearities.rectify,flip_filters=False)
    net['pool1']  = MaxPool3DDNNLayer(net['conv1a'],pool_size=(1,2,2),stride=(1,2,2))

    # ------------- 2nd layer group --------------
    net['conv2a'] = Conv3DDNNLayer(net['pool1'], 128, (3,3,3), pad=1,nonlinearity=lasagne.nonlinearities.rectify)
    net['pool2']  = MaxPool3DDNNLayer(net['conv2a'],pool_size=(2,2,2),stride=(2,2,2))

    # ----------------- 3rd layer group --------------
    net['conv3a'] = Conv3DDNNLayer(net['pool2'], 256, (3,3,3), pad=1,nonlinearity=lasagne.nonlinearities.rectify)
    net['conv3b'] = Conv3DDNNLayer(net['conv3a'], 256, (3,3,3), pad=1,nonlinearity=lasagne.nonlinearities.rectify)
    net['pool3']  = MaxPool3DDNNLayer(net['conv3b'],pool_size=(2,2,2),stride=(2,2,2))

    # ----------------- 4th layer group --------------
    net['conv4a'] = Conv3DDNNLayer(net['pool3'], 512, (3,3,3), pad=1,nonlinearity=lasagne.nonlinearities.rectify)
    net['conv4b'] = Conv3DDNNLayer(net['conv4a'], 512, (3,3,3), pad=1,nonlinearity=lasagne.nonlinearities.rectify)
    net['pool4']  = MaxPool3DDNNLayer(net['conv4b'],pool_size=(2,2,2),stride=(2,2,2))

    # ----------------- 5th layer group --------------
    net['conv5a'] = Conv3DDNNLayer(net['pool4'], 512, (3,3,3), pad=1,nonlinearity=lasagne.nonlinearities.rectify)
    net['conv5b'] = Conv3DDNNLayer(net['conv5a'], 512, (3,3,3), pad=1,nonlinearity=lasagne.nonlinearities.rectify)
    # We need a padding layer, as C3D only pads on the right, which cannot be done with a theano pooling layer
    net['pad']    = PadLayer(net['conv5b'],width=[(0,1),(0,1)], batch_ndim=3)
    net['pool5']  = MaxPool3DDNNLayer(net['pad'],pool_size=(2,2,2),pad=(0,0,0),stride=(2,2,2))
    net['fc6-1']  = DenseLayer(net['pool5'], num_units=4096,nonlinearity=lasagne.nonlinearities.rectify)
    net['fc7-1']  = DenseLayer(net['fc6-1'], num_units=4096,nonlinearity=lasagne.nonlinearities.rectify)
    net['fc8-1']  = DenseLayer(net['fc7-1'], num_units=487, nonlinearity=None)
    net['prob']  = NonlinearityLayer(net['fc8-1'], softmax)

    return net 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import NonlinearityLayer [as 别名]
def build_model():
    net = {}

    net['input'] = InputLayer((None, 3, 224, 224))
    net['conv1'] = ConvLayer(net['input'],
                             num_filters=96,
                             filter_size=7,
                             stride=2,
                             flip_filters=False)
    # caffe has alpha = alpha * pool_size
    net['norm1'] = NormLayer(net['conv1'], alpha=0.0001)
    net['pool1'] = PoolLayer(net['norm1'],
                             pool_size=3,
                             stride=3,
                             ignore_border=False)
    net['conv2'] = ConvLayer(net['pool1'],
                             num_filters=256,
                             filter_size=5,
                             flip_filters=False)
    net['pool2'] = PoolLayer(net['conv2'],
                             pool_size=2,
                             stride=2,
                             ignore_border=False)
    net['conv3'] = ConvLayer(net['pool2'],
                             num_filters=512,
                             filter_size=3,
                             pad=1,
                             flip_filters=False)
    net['conv4'] = ConvLayer(net['conv3'],
                             num_filters=512,
                             filter_size=3,
                             pad=1,
                             flip_filters=False)
    net['conv5'] = ConvLayer(net['conv4'],
                             num_filters=512,
                             filter_size=3,
                             pad=1,
                             flip_filters=False)
    net['pool5'] = PoolLayer(net['conv5'],
                             pool_size=3,
                             stride=3,
                             ignore_border=False)
    net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
    net['drop6'] = DropoutLayer(net['fc6'], p=0.5)
    net['fc7'] = DenseLayer(net['drop6'], num_units=4096)
    net['drop7'] = DropoutLayer(net['fc7'], p=0.5)
    net['fc8'] = DenseLayer(net['drop7'], num_units=1000, nonlinearity=None)
    net['prob'] = NonlinearityLayer(net['fc8'], softmax)

    return net