Python init.HeNormal方法代码示例

# 需要导入模块: from lasagne import init [as 别名]
# 或者: from lasagne.init import HeNormal [as 别名]
def initialization(name):

    initializations = {'sigmoid':init.HeNormal(gain=1.0),
            'softmax':init.HeNormal(gain=1.0),
            'elu':init.HeNormal(gain=1.0),
            'relu':init.HeNormal(gain=math.sqrt(2)),
            'lrelu':init.HeNormal(gain=math.sqrt(2/(1+0.01**2))),
            'vlrelu':init.HeNormal(gain=math.sqrt(2/(1+0.33**2))),
            'rectify':init.HeNormal(gain=math.sqrt(2)),
            'identity':init.HeNormal(gain=math.sqrt(2))
            }

    return initializations[name]


#################### BASELINE MODEL ##################### 

# 需要导入模块: from lasagne import init [as 别名]
# 或者: from lasagne.init import HeNormal [as 别名]
def define_network(inputs):

    network = lasagne.layers.InputLayer(shape=(None, params.CHANNELS, params.INPUT_SIZE, params.INPUT_SIZE, params.INPUT_SIZE),
                                input_var=inputs)

    network = Conv3DDNNLayer(
            network, num_filters=64, filter_size=(5, 5, 5),
            nonlinearity=lasagne.nonlinearities.leaky_rectify,
            W=HeNormal(gain='relu'))

    network = MaxPool3DDNNLayer(network, pool_size=(2, 2, 2))

    if params.BATCH_NORMALIZATION:
        network = lasagne.layers.batch_norm(network)

    network = Conv3DDNNLayer(
            network, num_filters=64, filter_size=(5, 5, 5),
            nonlinearity=lasagne.nonlinearities.leaky_rectify,
            W=HeNormal(gain='relu'))

    network = Conv3DDNNLayer(
            network, num_filters=96, filter_size=(5, 5, 5),
            nonlinearity=lasagne.nonlinearities.leaky_rectify,
            W=HeNormal(gain='relu'))

    if params.BATCH_NORMALIZATION:
        network = lasagne.layers.batch_norm(network)

    network = lasagne.layers.DenseLayer(
            network,
            num_units=420,
            nonlinearity=lasagne.nonlinearities.leaky_rectify,
            W=HeNormal(gain='relu')
    )

    network = lasagne.layers.DenseLayer(
            network, num_units=params.N_CLASSES,
            nonlinearity=lasagne.nonlinearities.softmax)

    return network 

# 需要导入模块: from lasagne import init [as 别名]
# 或者: from lasagne.init import HeNormal [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 init [as 别名]
# 或者: from lasagne.init import HeNormal [as 别名]
def build_UNet(n_input_channels=1, BATCH_SIZE=None, num_output_classes=2, pad='same', nonlinearity=lasagne.nonlinearities.elu, input_dim=(128, 128), base_n_filters=64, do_dropout=False):
    net = OrderedDict()
    net['input'] = InputLayer((BATCH_SIZE, n_input_channels, input_dim[0], input_dim[1]))

    net['contr_1_1'] = batch_norm(ConvLayer(net['input'], base_n_filters, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['contr_1_2'] = batch_norm(ConvLayer(net['contr_1_1'], base_n_filters, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['pool1'] = Pool2DLayer(net['contr_1_2'], 2)

    net['contr_2_1'] = batch_norm(ConvLayer(net['pool1'], base_n_filters*2, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['contr_2_2'] = batch_norm(ConvLayer(net['contr_2_1'], base_n_filters*2, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['pool2'] = Pool2DLayer(net['contr_2_2'], 2)

    net['contr_3_1'] = batch_norm(ConvLayer(net['pool2'], base_n_filters*4, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['contr_3_2'] = batch_norm(ConvLayer(net['contr_3_1'], base_n_filters*4, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['pool3'] = Pool2DLayer(net['contr_3_2'], 2)

    net['contr_4_1'] = batch_norm(ConvLayer(net['pool3'], base_n_filters*8, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['contr_4_2'] = batch_norm(ConvLayer(net['contr_4_1'], base_n_filters*8, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    l = net['pool4'] = Pool2DLayer(net['contr_4_2'], 2)
    # the paper does not really describe where and how dropout is added. Feel free to try more options
    if do_dropout:
        l = DropoutLayer(l, p=0.4)

    net['encode_1'] = batch_norm(ConvLayer(l, base_n_filters*16, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['encode_2'] = batch_norm(ConvLayer(net['encode_1'], base_n_filters*16, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['upscale1'] = batch_norm(Deconv2DLayer(net['encode_2'], base_n_filters*16, 2, 2, crop="valid", nonlinearity=nonlinearity, W=HeNormal(gain="relu")))

    net['concat1'] = ConcatLayer([net['upscale1'], net['contr_4_2']], cropping=(None, None, "center", "center"))
    net['expand_1_1'] = batch_norm(ConvLayer(net['concat1'], base_n_filters*8, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['expand_1_2'] = batch_norm(ConvLayer(net['expand_1_1'], base_n_filters*8, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['upscale2'] = batch_norm(Deconv2DLayer(net['expand_1_2'], base_n_filters*8, 2, 2, crop="valid", nonlinearity=nonlinearity, W=HeNormal(gain="relu")))

    net['concat2'] = ConcatLayer([net['upscale2'], net['contr_3_2']], cropping=(None, None, "center", "center"))
    net['expand_2_1'] = batch_norm(ConvLayer(net['concat2'], base_n_filters*4, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['expand_2_2'] = batch_norm(ConvLayer(net['expand_2_1'], base_n_filters*4, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['upscale3'] = batch_norm(Deconv2DLayer(net['expand_2_2'], base_n_filters*4, 2, 2, crop="valid", nonlinearity=nonlinearity, W=HeNormal(gain="relu")))

    net['concat3'] = ConcatLayer([net['upscale3'], net['contr_2_2']], cropping=(None, None, "center", "center"))
    net['expand_3_1'] = batch_norm(ConvLayer(net['concat3'], base_n_filters*2, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['expand_3_2'] = batch_norm(ConvLayer(net['expand_3_1'], base_n_filters*2, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['upscale4'] = batch_norm(Deconv2DLayer(net['expand_3_2'], base_n_filters*2, 2, 2, crop="valid", nonlinearity=nonlinearity, W=HeNormal(gain="relu")))

    net['concat4'] = ConcatLayer([net['upscale4'], net['contr_1_2']], cropping=(None, None, "center", "center"))
    net['expand_4_1'] = batch_norm(ConvLayer(net['concat4'], base_n_filters, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))
    net['expand_4_2'] = batch_norm(ConvLayer(net['expand_4_1'], base_n_filters, 3, nonlinearity=nonlinearity, pad=pad, W=HeNormal(gain="relu")))

    net['output_segmentation'] = ConvLayer(net['expand_4_2'], num_output_classes, 1, nonlinearity=None)
    net['dimshuffle'] = DimshuffleLayer(net['output_segmentation'], (1, 0, 2, 3))
    net['reshapeSeg'] = ReshapeLayer(net['dimshuffle'], (num_output_classes, -1))
    net['dimshuffle2'] = DimshuffleLayer(net['reshapeSeg'], (1, 0))
    net['output_flattened'] = NonlinearityLayer(net['dimshuffle2'], nonlinearity=lasagne.nonlinearities.softmax)

    return net 

# 需要导入模块: from lasagne import init [as 别名]
# 或者: from lasagne.init import HeNormal [as 别名]
def buildModel():

    print "BUILDING MODEL TYPE..."

    #default settings
    filters = 64
    first_stride = 2
    last_filter_multiplier = 16

    #input layer
    net = l.InputLayer((None, IM_DIM, IM_SIZE[1], IM_SIZE[0]))

    #conv layers
    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters, filter_size=7, pad='same', stride=first_stride, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 2, filter_size=5, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 4, filter_size=3, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 8, filter_size=3, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * last_filter_multiplier, filter_size=3, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    print "\tFINAL POOL OUT SHAPE:", l.get_output_shape(net) 

    #dense layers
    net = l.batch_norm(l.DenseLayer(net, 512, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.DropoutLayer(net, DROPOUT)  
    net = l.batch_norm(l.DenseLayer(net, 512, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.DropoutLayer(net, DROPOUT)  

    #Classification Layer
    if MULTI_LABEL:
        net = l.DenseLayer(net, NUM_CLASSES, nonlinearity=nonlinearities.sigmoid, W=init.HeNormal(gain=1))
    else:
        net = l.DenseLayer(net, NUM_CLASSES, nonlinearity=nonlinearities.softmax, W=init.HeNormal(gain=1))

    print "...DONE!"

    #model stats
    print "MODEL HAS", (sum(hasattr(layer, 'W') for layer in l.get_all_layers(net))), "WEIGHTED LAYERS"
    print "MODEL HAS", l.count_params(net), "PARAMS"

    return net 

# 需要导入模块: from lasagne import init [as 别名]
# 或者: from lasagne.init import HeNormal [as 别名]
def ResNet_BottleNeck_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)

    Judging from https://github.com/KaimingHe/resnet-1k-layers/blob/master/resnet-pre-act.lua.
    Number of filters go 16 -> 64 -> 128 -> 256

    Forumala to figure out depth: 9n + 2
    '''

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

    # first layer, output is 16x16x16
    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 64x16x16
    l = residual_bottleneck_block(l, first=True)
    for _ in range(1, n):
        l = residual_bottleneck_block(l)

    # second stack of residual blocks, output is 128x8x8
    l = residual_bottleneck_block(l, increase_dim=True)
    for _ in range(1, n):
        l = residual_bottleneck_block(l)

    # third stack of residual blocks, output is 256x4x4
    l = residual_bottleneck_block(l, increase_dim=True)
    for _ in range(1, n):
        l = residual_bottleneck_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 init [as 别名]
# 或者: from lasagne.init import HeNormal [as 别名]
def ResNet_FullPre_Wide(input_shape=(None, 3, PIXELS, PIXELS), input_var=None, n_classes=10, n=6, k=4):
    """
    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)

    And 'Wide Residual Networks', Sergey Zagoruyko, Nikos Komodakis 2016 (http://arxiv.org/pdf/1605.07146v1.pdf)

    Depth = 6n + 2
    """
    n_filters = {0: 16, 1: 16*k, 2: 32*k, 3: 64*k}

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

    # first layer, output is 16 x 64 x 64
    l = batch_norm(ConvLayer(l_in, num_filters=n_filters[0], filter_size=(3, 3), stride=(1, 1), nonlinearity=rectify, pad='same', W=he_norm))

    # first stack of residual blocks, output is 32 x 64 x 64
    l = residual_wide_block(l, first=True, filters=n_filters[1])
    for _ in range(1, n):
        l = residual_wide_block(l, filters=n_filters[1])

    # second stack of residual blocks, output is 64 x 32 x 32
    l = residual_wide_block(l, increase_dim=True, filters=n_filters[2])
    for _ in range(1, (n+2)):
        l = residual_wide_block(l, filters=n_filters[2])

    # third stack of residual blocks, output is 128 x 16 x 16
    l = residual_wide_block(l, increase_dim=True, filters=n_filters[3])
    for _ in range(1, (n+2)):
        l = residual_wide_block(l, filters=n_filters[3])

    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 init [as 别名]
# 或者: from lasagne.init import HeNormal [as 别名]
def buildModel(mtype=1):

    print "BUILDING MODEL TYPE", mtype, "..."

    #default settings (Model 1)
    filters = 64
    first_stride = 2
    last_filter_multiplier = 16

    #specific model type settings (see working notes for details)
    if mtype == 2:
        first_stride = 1
    elif mtype == 3:
        filters = 32
        last_filter_multiplier = 8

    #input layer
    net = l.InputLayer((None, IM_DIM, IM_SIZE[1], IM_SIZE[0]))

    #conv layers
    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters, filter_size=7, pad='same', stride=first_stride, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    if mtype == 2:
        net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters, filter_size=5, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
        net = l.MaxPool2DLayer(net, pool_size=2)

    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 2, filter_size=5, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 4, filter_size=3, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 8, filter_size=3, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * last_filter_multiplier, filter_size=3, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    print "\tFINAL POOL OUT SHAPE:", l.get_output_shape(net) 

    #dense layers
    net = l.batch_norm(l.DenseLayer(net, 512, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.batch_norm(l.DenseLayer(net, 512, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))

    #Classification Layer
    if MULTI_LABEL:
        net = l.DenseLayer(net, NUM_CLASSES, nonlinearity=nonlinearities.sigmoid, W=init.HeNormal(gain=1))
    else:
        net = l.DenseLayer(net, NUM_CLASSES, nonlinearity=nonlinearities.softmax, W=init.HeNormal(gain=1))

    print "...DONE!"

    #model stats
    print "MODEL HAS", (sum(hasattr(layer, 'W') for layer in l.get_all_layers(net))), "WEIGHTED LAYERS"
    print "MODEL HAS", l.count_params(net), "PARAMS"

    return net 

# 需要导入模块: from lasagne import init [as 别名]
# 或者: from lasagne.init import HeNormal [as 别名]
def buildModel(mtype=1):

    print "BUILDING MODEL TYPE", mtype, "..."

    #default settings (Model 1)
    filters = 64
    first_stride = 2
    last_filter_multiplier = 16

    #specific model type settings (see working notes for details)
    if mtype == 2:
        first_stride = 1
    elif mtype == 3:
        filters = 32
        last_filter_multiplier = 8

    #input layer
    net = l.InputLayer((None, IM_DIM, IM_SIZE[1], IM_SIZE[0]))

    #conv layers
    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters, filter_size=7, pad='same', stride=first_stride, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    if mtype == 2:
        net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters, filter_size=5, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
        net = l.MaxPool2DLayer(net, pool_size=2)

    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 2, filter_size=5, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 4, filter_size=3, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * 8, filter_size=3, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    net = l.batch_norm(l.Conv2DLayer(net, num_filters=filters * last_filter_multiplier, filter_size=3, pad='same', stride=1, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.MaxPool2DLayer(net, pool_size=2)

    print "\tFINAL POOL OUT SHAPE:", l.get_output_shape(net) 

    #dense layers
    net = l.batch_norm(l.DenseLayer(net, 512, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.DropoutLayer(net, DROPOUT)  
    net = l.batch_norm(l.DenseLayer(net, 512, W=init.HeNormal(gain=INIT_GAIN), nonlinearity=NONLINEARITY))
    net = l.DropoutLayer(net, DROPOUT)  

    #Classification Layer
    if MULTI_LABEL:
        net = l.DenseLayer(net, NUM_CLASSES, nonlinearity=nonlinearities.sigmoid, W=init.HeNormal(gain=1))
    else:
        net = l.DenseLayer(net, NUM_CLASSES, nonlinearity=nonlinearities.softmax, W=init.HeNormal(gain=1))

    print "...DONE!"

    #model stats
    print "MODEL HAS", (sum(hasattr(layer, 'W') for layer in l.get_all_layers(net))), "WEIGHTED LAYERS"
    print "MODEL HAS", l.count_params(net), "PARAMS"

    return net