Python layers.batch_norm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 batch_norm [as 别名]
def build_discriminator_toy(image=None, nd=512, GP_norm=None):
    Input = InputLayer(shape=(None, 2), input_var=image)
    print ("Dis input:", Input.output_shape)
    dis0 = DenseLayer(Input, nd, W=Normal(0.02), nonlinearity=relu)
    print ("Dis fc0:", dis0.output_shape)
    if GP_norm is True:
        dis1 = DenseLayer(dis0, nd, W=Normal(0.02), nonlinearity=relu)
    else:
        dis1 = batch_norm(DenseLayer(dis0, nd, W=Normal(0.02), nonlinearity=relu))
    print ("Dis fc1:", dis1.output_shape)
    if GP_norm is True:
        dis2 = batch_norm(DenseLayer(dis1, nd, W=Normal(0.02), nonlinearity=relu))
    else:
        dis2 = DenseLayer(dis1, nd, W=Normal(0.02), nonlinearity=relu)
    print ("Dis fc2:", dis2.output_shape)
    disout = DenseLayer(dis2, 1, W=Normal(0.02), nonlinearity=sigmoid)
    print ("Dis output:", disout.output_shape)
    return disout 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import batch_norm [as 别名]
def build_discriminator_32(image=None,ndf=128):
    lrelu = LeakyRectify(0.2)
    # input: images
    InputImg = InputLayer(shape=(None, 3, 32, 32), input_var=image)
    print ("Dis Img_input:", InputImg.output_shape)
    # Conv Layer
    dis1 = Conv2DLayer(InputImg, ndf, (4,4), (2,2), pad=1, W=Normal(0.02), nonlinearity=lrelu)
    print ("Dis conv1:", dis1.output_shape)
    # Conv Layer
    dis2 = batch_norm(Conv2DLayer(dis1, ndf*2, (4,4), (2,2), pad=1, W=Normal(0.02), nonlinearity=lrelu))
    print ("Dis conv2:", dis2.output_shape)
    # Conv Layer
    dis3 = batch_norm(Conv2DLayer(dis2, ndf*4, (4,4), (2,2), pad=1, W=Normal(0.02), nonlinearity=lrelu))
    print ("Dis conv3:", dis3.output_shape)
    # Conv Layer
    dis4 = DenseLayer(dis3, 1, W=Normal(0.02), nonlinearity=sigmoid)
    print ("Dis output:", dis4.output_shape)
    return dis4 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import batch_norm [as 别名]
def build_discriminator_64(image=None,ndf=128):
    lrelu = LeakyRectify(0.2)
    # input: images
    InputImg = InputLayer(shape=(None, 3, 64, 64), input_var=image)
    print ("Dis Img_input:", InputImg.output_shape)
    # Conv Layer
    dis1 = Conv2DLayer(InputImg, ndf, (4,4), (2,2), pad=1, W=Normal(0.02), nonlinearity=lrelu)
    print ("Dis conv1:", dis1.output_shape)
    # Conv Layer
    dis2 = batch_norm(Conv2DLayer(dis1, ndf*2, (4,4), (2,2), pad=1, W=Normal(0.02), nonlinearity=lrelu))
    print ("Dis conv2:", dis2.output_shape)
    # Conv Layer
    dis3 = batch_norm(Conv2DLayer(dis2, ndf*4, (4,4), (2,2), pad=1, W=Normal(0.02), nonlinearity=lrelu))
    print ("Dis conv3:", dis3.output_shape)
    # Conv Layer
    dis4 = batch_norm(Conv2DLayer(dis3, ndf*8, (4,4), (2,2), pad=1, W=Normal(0.02), nonlinearity=lrelu))
    print ("Dis conv3:", dis4.output_shape)
    # Conv Layer
    dis5 = DenseLayer(dis4, 1, W=Normal(0.02), nonlinearity=sigmoid)
    print ("Dis output:", dis5.output_shape)
    return dis5 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import batch_norm [as 别名]
def build_discriminator_128(image=None,ndf=128):
    lrelu = LeakyRectify(0.2)
    # input: images
    InputImg = InputLayer(shape=(None, 3, 128, 128), input_var=image)
    print ("Dis Img_input:", InputImg.output_shape)
    # Conv Layer
    dis1 = Conv2DLayer(InputImg, ndf, (4,4), (2,2), pad=1, W=Normal(0.02), nonlinearity=lrelu)
    print ("Dis conv1:", dis1.output_shape)
    # Conv Layer
    dis2 = batch_norm(Conv2DLayer(dis1, ndf*2, (4,4), (2,2), pad=1, W=Normal(0.02), nonlinearity=lrelu))
    print ("Dis conv2:", dis2.output_shape)
    # Conv Layer
    dis3 = batch_norm(Conv2DLayer(dis2, ndf*4, (4,4), (2,2), pad=1, W=Normal(0.02), nonlinearity=lrelu))
    print ("Dis conv3:", dis3.output_shape)
    # Conv Layer
    dis4 = batch_norm(Conv2DLayer(dis3, ndf*8, (4,4), (2,2), pad=1, W=Normal(0.02), nonlinearity=lrelu))
    print ("Dis conv3:", dis4.output_shape)
    # Conv Layer
    dis5 = batch_norm(Conv2DLayer(dis4, ndf*16, (4,4), (2,2), pad=1, W=Normal(0.02), nonlinearity=lrelu))
    print ("Dis conv4:", dis5.output_shape)
    # Conv Layer
    dis6 = DenseLayer(dis5, 1, W=Normal(0.02), nonlinearity=sigmoid)
    print ("Dis output:", dis6.output_shape)
    return dis6 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import batch_norm [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 batch_norm [as 别名]
def setup_discriminator(self):
        c = args.discriminator_size
        self.make_layer('disc1.1', batch_norm(self.network['conv1_2']), 1*c, filter_size=(5,5), stride=(2,2), pad=(2,2))
        self.make_layer('disc1.2', self.last_layer(), 1*c, filter_size=(5,5), stride=(2,2), pad=(2,2))
        self.make_layer('disc2', batch_norm(self.network['conv2_2']), 2*c, filter_size=(5,5), stride=(2,2), pad=(2,2))
        self.make_layer('disc3', batch_norm(self.network['conv3_2']), 3*c, filter_size=(3,3), stride=(1,1), pad=(1,1))
        hypercolumn = ConcatLayer([self.network['disc1.2>'], self.network['disc2>'], self.network['disc3>']])
        self.make_layer('disc4', hypercolumn, 4*c, filter_size=(1,1), stride=(1,1), pad=(0,0))
        self.make_layer('disc5', self.last_layer(), 3*c, filter_size=(3,3), stride=(2,2))
        self.make_layer('disc6', self.last_layer(), 2*c, filter_size=(1,1), stride=(1,1), pad=(0,0))
        self.network['disc'] = batch_norm(ConvLayer(self.last_layer(), 1, filter_size=(1,1),
                                                    nonlinearity=lasagne.nonlinearities.linear))


    #------------------------------------------------------------------------------------------------------------------
    # Input / Output
    #------------------------------------------------------------------------------------------------------------------ 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import batch_norm [as 别名]
def build_critic(input_var=None):
    from lasagne.layers import (InputLayer, Conv2DLayer, ReshapeLayer,
                                DenseLayer)
    try:
        from lasagne.layers.dnn import batch_norm_dnn as batch_norm
    except ImportError:
        from lasagne.layers import batch_norm
    from lasagne.nonlinearities import LeakyRectify
    lrelu = LeakyRectify(0.2)
    # input: (None, 1, 28, 28)
    layer = InputLayer(shape=(None, 1, 28, 28), input_var=input_var)
    # two convolutions
    layer = batch_norm(Conv2DLayer(layer, 64, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    layer = batch_norm(Conv2DLayer(layer, 128, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    # fully-connected layer
    layer = batch_norm(DenseLayer(layer, 1024, nonlinearity=lrelu))
    # output layer (linear)
    layer = DenseLayer(layer, 1, nonlinearity=None)
    print ("critic output:", layer.output_shape)
    return layer 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import batch_norm [as 别名]
def build_critic(input_var=None):
    from lasagne.layers import (InputLayer, Conv2DLayer, ReshapeLayer,
                                DenseLayer)
    try:
        from lasagne.layers.dnn import batch_norm_dnn as batch_norm
    except ImportError:
        from lasagne.layers import batch_norm
    from lasagne.nonlinearities import LeakyRectify
    lrelu = LeakyRectify(0.2)
    # input: (None, 1, 28, 28)
    layer = InputLayer(shape=(None, 1, 28, 28), input_var=input_var)
    # two convolutions
    layer = batch_norm(Conv2DLayer(layer, 64, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    layer = batch_norm(Conv2DLayer(layer, 128, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    # fully-connected layer
    layer = batch_norm(DenseLayer(layer, 1024, nonlinearity=lrelu))
    # output layer (linear and without bias)
    layer = DenseLayer(layer, 1, nonlinearity=None, b=None)
    print ("critic output:", layer.output_shape)
    return layer 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import batch_norm [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 batch_norm [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 batch_norm [as 别名]
def createCNN(self):
        net = {}
        net['input'] = lasagne.layers.InputLayer(shape=(None, self.nChannels, self.imageHeight, self.imageWidth), input_var=self.data)       
        print("Input shape: {0}".format(net['input'].output_shape))

        #STAGE 1
        net['s1_conv1_1'] = batch_norm(Conv2DLayer(net['input'], 64, 3, pad='same', W=GlorotUniform('relu')))
        net['s1_conv1_2'] = batch_norm(Conv2DLayer(net['s1_conv1_1'], 64, 3, pad='same', W=GlorotUniform('relu')))
        net['s1_pool1'] = lasagne.layers.Pool2DLayer(net['s1_conv1_2'], 2)

        net['s1_conv2_1'] = batch_norm(Conv2DLayer(net['s1_pool1'], 128, 3, pad=1, W=GlorotUniform('relu')))
        net['s1_conv2_2'] = batch_norm(Conv2DLayer(net['s1_conv2_1'], 128, 3, pad=1, W=GlorotUniform('relu')))
        net['s1_pool2'] = lasagne.layers.Pool2DLayer(net['s1_conv2_2'], 2)

        net['s1_conv3_1'] = batch_norm (Conv2DLayer(net['s1_pool2'], 256, 3, pad=1, W=GlorotUniform('relu')))
        net['s1_conv3_2'] = batch_norm (Conv2DLayer(net['s1_conv3_1'], 256, 3, pad=1, W=GlorotUniform('relu')))  
        net['s1_pool3'] = lasagne.layers.Pool2DLayer(net['s1_conv3_2'], 2)
        
        net['s1_conv4_1'] = batch_norm(Conv2DLayer(net['s1_pool3'], 512, 3, pad=1, W=GlorotUniform('relu')))
        net['s1_conv4_2'] = batch_norm (Conv2DLayer(net['s1_conv4_1'], 512, 3, pad=1, W=GlorotUniform('relu')))  
        net['s1_pool4'] = lasagne.layers.Pool2DLayer(net['s1_conv4_2'], 2)
                      
        net['s1_fc1_dropout'] = lasagne.layers.DropoutLayer(net['s1_pool4'], p=0.5)
        net['s1_fc1'] = batch_norm(lasagne.layers.DenseLayer(net['s1_fc1_dropout'], num_units=256, W=GlorotUniform('relu')))

        net['s1_output'] = lasagne.layers.DenseLayer(net['s1_fc1'], num_units=136, nonlinearity=None)
        net['s1_landmarks'] = LandmarkInitLayer(net['s1_output'], self.initLandmarks)

        for i in range(1, self.nStages):
            self.addDANStage(i + 1, net)

        net['output'] = net['s' + str(self.nStages) + '_landmarks']

        return net 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import batch_norm [as 别名]
def createCNN(self):
        net = {}
        net['input'] = lasagne.layers.InputLayer(shape=(None, self.nChannels, self.imageHeight, self.imageWidth), input_var=self.data)       
        print("Input shape: {0}".format(net['input'].output_shape))

        #STAGE 1
        net['s1_conv1_1'] = batch_norm(Conv2DLayer(net['input'], 64, 3, pad='same', W=GlorotUniform('relu')))
        net['s1_conv1_2'] = batch_norm(Conv2DLayer(net['s1_conv1_1'], 64, 3, pad='same', W=GlorotUniform('relu')))
        net['s1_pool1'] = lasagne.layers.Pool2DLayer(net['s1_conv1_2'], 2)

        net['s1_conv2_1'] = batch_norm(Conv2DLayer(net['s1_pool1'], 128, 3, pad=1, W=GlorotUniform('relu')))
        net['s1_conv2_2'] = batch_norm(Conv2DLayer(net['s1_conv2_1'], 128, 3, pad=1, W=GlorotUniform('relu')))
        net['s1_pool2'] = lasagne.layers.Pool2DLayer(net['s1_conv2_2'], 2)

        net['s1_conv3_1'] = batch_norm (Conv2DLayer(net['s1_pool2'], 256, 3, pad=1, W=GlorotUniform('relu')))
        net['s1_conv3_2'] = batch_norm (Conv2DLayer(net['s1_conv3_1'], 256, 3, pad=1, W=GlorotUniform('relu')))  
        net['s1_pool3'] = lasagne.layers.Pool2DLayer(net['s1_conv3_2'], 2)
        
        net['s1_conv4_1'] = batch_norm(Conv2DLayer(net['s1_pool3'], 512, 3, pad=1, W=GlorotUniform('relu')))
        net['s1_conv4_2'] = batch_norm (Conv2DLayer(net['s1_conv4_1'], 512, 3, pad=1, W=GlorotUniform('relu')))  
        net['s1_pool4'] = lasagne.layers.Pool2DLayer(net['s1_conv4_2'], 2)
                      
        net['s1_fc1_dropout'] = lasagne.layers.DropoutLayer(net['s1_pool4'], p=0.5)
        net['s1_fc1'] = batch_norm(lasagne.layers.DenseLayer(net['s1_fc1_dropout'], num_units=256, W=GlorotUniform('relu')))

        net['s1_output'] = lasagne.layers.DenseLayer(net['s1_fc1'], num_units=136, nonlinearity=None)
        net['s1_landmarks'] = LandmarkInitLayer(net['s1_output'], self.initLandmarks)

        if self.confidenceLayer:
            net['s1_confidence'] = lasagne.layers.DenseLayer(net['s1_fc1'], num_units=2, W=GlorotUniform('relu'), nonlinearity=lasagne.nonlinearities.softmax)

        for i in range(1, self.nStages):
            self.addDANStage(i + 1, net)

        net['output'] = net['s' + str(self.nStages) + '_landmarks']
        if self.confidenceLayer:
            net['output'] = lasagne.layers.ConcatLayer([net['output'], net['s1_confidence']])

        return net 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import batch_norm [as 别名]
def batch_norm(layer):
    if cfg.BATCH_NORM:
        return l_batch_norm(layer)
    else:
        return layer 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import batch_norm [as 别名]
def classificationBranch(net, kernel_size):

    # Post Convolution
    branch = l.batch_norm(l.Conv2DLayer(net,
                        num_filters=int(FILTERS[-1] * RESNET_K),
                        filter_size=kernel_size,
                        nonlinearity=nl.rectify))

    #log.p(("\t\tPOST  CONV SHAPE:", l.get_output_shape(branch), "LAYER:", len(l.get_all_layers(branch)) - 1))

    # Dropout Layer
    branch = l.DropoutLayer(branch)
    
    # Dense Convolution
    branch = l.batch_norm(l.Conv2DLayer(branch,
                        num_filters=int(FILTERS[-1] * RESNET_K * 2),
                        filter_size=1,
                        nonlinearity=nl.rectify))

    #log.p(("\t\tDENSE CONV SHAPE:", l.get_output_shape(branch), "LAYER:", len(l.get_all_layers(branch)) - 1))
    
    # Dropout Layer
    branch = l.DropoutLayer(branch)
    
    # Class Convolution
    branch = l.Conv2DLayer(branch,
                        num_filters=len(cfg.CLASSES),
                        filter_size=1,
                        nonlinearity=None)
    return branch 

# 需要导入模块: from lasagne import layers [as 别名]
# 或者: from lasagne.layers import batch_norm [as 别名]
def create_model(self, X, Z, n_dim, n_out, n_chan=1):
    # params
    n_lat = 100 # latent variables
    n_g_hid1 = 1024 # size of hidden layer in generator layer 1
    n_g_hid2 = 128 # size of hidden layer in generator layer 2
    n_out = n_dim * n_dim * n_chan # total dimensionality of output

    if self.model == 'gaussian': 
      raise Exception('Gaussian variables currently nor supported in GAN')

    # create the generator network
    l_g_in = lasagne.layers.InputLayer(shape=(None, n_lat), input_var=Z)
    l_g_hid1 = batch_norm(lasagne.layers.DenseLayer(l_g_in, n_g_hid1))
    l_g_hid2 = batch_norm(lasagne.layers.DenseLayer(l_g_hid1, n_g_hid2*7*7))
    l_g_hid2 = lasagne.layers.ReshapeLayer(l_g_hid2, ([0], n_g_hid2, 7, 7))
    l_g_dc1 = batch_norm(Deconv2DLayer(l_g_hid2, 64, 5, stride=2, pad=2))
    l_g = Deconv2DLayer(l_g_dc1, n_chan, 5, stride=2, pad=2, 
            nonlinearity=lasagne.nonlinearities.sigmoid)
    print ("Generator output:", l_g.output_shape)

    # create the discriminator network
    lrelu = lasagne.nonlinearities.LeakyRectify(0.2)
    l_d_in = lasagne.layers.InputLayer(shape=(None, n_chan, n_dim, n_dim), 
                                       input_var=X)
    l_d_hid1 = batch_norm(lasagne.layers.Conv2DLayer(
        l_d_in, num_filters=64, filter_size=5, stride=2, pad=2,
        nonlinearity=lrelu, name='l_d_hid1'))
    l_d_hid2 = batch_norm(lasagne.layers.Conv2DLayer(
        l_d_hid1, num_filters=128, filter_size=5, stride=2, pad=2,
        nonlinearity=lrelu, name='l_d_hid2'))
    l_d_hid3 = batch_norm(lasagne.layers.DenseLayer(l_d_hid2, 1024, nonlinearity=lrelu))
    l_d = lasagne.layers.DenseLayer(l_d_hid3, 1, nonlinearity=lasagne.nonlinearities.sigmoid)
    print ("Discriminator output:", l_d.output_shape)

    return l_g, l_d