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Python layers.Upscale2DLayer方法代碼示例

本文整理匯總了Python中lasagne.layers.Upscale2DLayer方法的典型用法代碼示例。如果您正苦於以下問題：Python layers.Upscale2DLayer方法的具體用法？Python layers.Upscale2DLayer怎麽用？Python layers.Upscale2DLayer使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類lasagne.layers的用法示例。


在下文中一共展示了layers.Upscale2DLayer方法的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: build_fcn_segmenter

# 需要導入模塊: from lasagne import layers [as 別名]
# 或者: from lasagne.layers import Upscale2DLayer [as 別名]
def build_fcn_segmenter(input_var, shape, version=2):
    ret = {}

    if version == 2:
        ret['input'] = la = InputLayer(shape, input_var)
        ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=8, filter_size=7))
        ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=16, filter_size=3))
        ret['pool%d'%len(ret)] = la = MaxPool2DLayer(la, pool_size=2)
        ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=32, filter_size=3))
        ret['pool%d'%len(ret)] = la = MaxPool2DLayer(la, pool_size=2)
        ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=64, filter_size=3))
        ret['pool%d'%len(ret)] = la = MaxPool2DLayer(la, pool_size=2)
        ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=64, filter_size=3))
        ret['dec%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=64, filter_size=3,
            pad='full'))
        ret['ups%d'%len(ret)] = la = Upscale2DLayer(la, scale_factor=2)
        ret['dec%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=64, filter_size=3,
            pad='full'))
        ret['ups%d'%len(ret)] = la = Upscale2DLayer(la, scale_factor=2)
        ret['dec%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=32, filter_size=7,
            pad='full'))
        ret['ups%d'%len(ret)] = la = Upscale2DLayer(la, scale_factor=2)
        ret['dec%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=16, filter_size=3,
            pad='full'))
        ret['conv%d'%len(ret)] = la = bn(Conv2DLayer(la, num_filters=8, filter_size=7))
        ret['output'] = la = Conv2DLayer(la, num_filters=1, filter_size=7,
                pad='full', nonlinearity=nn.nonlinearities.sigmoid)

    return ret, nn.layers.get_output(ret['output']), \
            nn.layers.get_output(ret['output'], deterministic=True)
開發者ID:woshialex，項目名稱:diagnose-heart，代碼行數:32，代碼來源:models.py

示例2: build_autoencoder_network

# 需要導入模塊: from lasagne import layers [as 別名]
# 或者: from lasagne.layers import Upscale2DLayer [as 別名]
def build_autoencoder_network():
    input_var = T.tensor4('input_var');

    layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    prely = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));

    featm    = batch_norm(layers.Conv2DLayer(prely, 180, filter_size=(1,1), nonlinearity=leaky_rectify));
    feat_map = batch_norm(layers.Conv2DLayer(featm, 120, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
    maskm    = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
    mask_rep = batch_norm(layers.Conv2DLayer(maskm,   1, filter_size=(1,1), nonlinearity=None),   beta=None, gamma=None);
    mask_map = SoftThresPerc(mask_rep, perc=90.0, alpha=0.5, beta=init.Constant(0.1), tight=100.0, name="mask_map");
    layer    = ChInnerProdMerge(feat_map, mask_map, name="encoder");

    layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer =            layers.Deconv2DLayer(layer,   3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);

    glblf = batch_norm(layers.Conv2DLayer(prely,  100, filter_size=(1,1), nonlinearity=leaky_rectify));
    glblf = layers.Pool2DLayer(glblf, pool_size=(20,20), stride=20, mode='average_inc_pad');
    glblf = batch_norm(layers.Conv2DLayer(glblf,   64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Conv2DLayer(glblf,    3, filter_size=(1,1), nonlinearity=rectify), name="global_feature");

    glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = layers.Upscale2DLayer(glblf, scale_factor=20);
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf =            layers.Deconv2DLayer(glblf,  3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);

    layer = layers.ElemwiseSumLayer([layer, glblf]);

    network = ReshapeLayer(layer, ([0], -1));
    mask_var = lasagne.layers.get_output(mask_map);
    output_var = lasagne.layers.get_output(network);

    return network, input_var, mask_var, output_var;
開發者ID:SBU-BMI，項目名稱:u24_lymphocyte，代碼行數:55，代碼來源:deep_conv_ae_spsparse_alt36.py

示例3: build_autoencoder_network

# 需要導入模塊: from lasagne import layers [as 別名]
# 或者: from lasagne.layers import Upscale2DLayer [as 別名]
def build_autoencoder_network():
    input_var = T.tensor4('input_var');

    layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 140, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 160, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 180, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    prely = batch_norm(layers.Conv2DLayer(layer, 200, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));

    featm    = batch_norm(layers.Conv2DLayer(prely, 160, filter_size=(1,1), nonlinearity=leaky_rectify));
    feat_map = batch_norm(layers.Conv2DLayer(featm, 120, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
    maskm    = batch_norm(layers.Conv2DLayer(prely, 120, filter_size=(1,1), nonlinearity=leaky_rectify));
    mask_rep = batch_norm(layers.Conv2DLayer(maskm,   1, filter_size=(1,1), nonlinearity=None),   beta=None, gamma=None);
    mask_map = SoftThresPerc(mask_rep, perc=99.9, alpha=0.5, beta=init.Constant(0.1), tight=100.0, name="mask_map");
    layer    = ChInnerProdMerge(feat_map, mask_map, name="encoder");

    layer = batch_norm(layers.Deconv2DLayer(layer, 200, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 180, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 160, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 140, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer =            layers.Deconv2DLayer(layer,   3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);

    glblf = batch_norm(layers.Conv2DLayer(prely,  100, filter_size=(1,1), nonlinearity=leaky_rectify));
    glblf = layers.Pool2DLayer(glblf, pool_size=(20,20), stride=20, mode='average_inc_pad');
    glblf = batch_norm(layers.Conv2DLayer(glblf,   64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Conv2DLayer(glblf,    3, filter_size=(1,1), nonlinearity=rectify), name="global_feature");

    glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = layers.Upscale2DLayer(glblf, scale_factor=20);
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf =            layers.Deconv2DLayer(glblf,  3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);

    layer = layers.ElemwiseSumLayer([layer, glblf]);

    network = ReshapeLayer(layer, ([0], -1));
    mask_var = lasagne.layers.get_output(mask_map);
    output_var = lasagne.layers.get_output(network);

    return network, input_var, mask_var, output_var;
開發者ID:SBU-BMI，項目名稱:u24_lymphocyte，代碼行數:55，代碼來源:deep_conv_ae_spsparse_alt35.py

示例4: build_autoencoder_network

# 需要導入模塊: from lasagne import layers [as 別名]
# 或者: from lasagne.layers import Upscale2DLayer [as 別名]
def build_autoencoder_network():
    input_var = T.tensor4('input_var');

    layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    prely = batch_norm(layers.Conv2DLayer(layer, 140, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));

    featm    = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
    feat_map = batch_norm(layers.Conv2DLayer(featm, 100, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
    maskm    = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
    mask_rep = batch_norm(layers.Conv2DLayer(maskm,   1, filter_size=(1,1), nonlinearity=None),   beta=None, gamma=None);
    mask_map = SoftThresPerc(mask_rep, perc=99.9, alpha=0.5, beta=init.Constant(0.1), tight=100.0, name="mask_map");
    layer    = ChInnerProdMerge(feat_map, mask_map, name="encoder");

    layer = batch_norm(layers.Deconv2DLayer(layer, 140, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer =            layers.Deconv2DLayer(layer,   3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);

    glblf = batch_norm(layers.Conv2DLayer(prely,  100, filter_size=(1,1), nonlinearity=leaky_rectify));
    glblf = layers.Pool2DLayer(glblf, pool_size=(20,20), stride=20, mode='average_inc_pad');
    glblf = batch_norm(layers.Conv2DLayer(glblf,   64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Conv2DLayer(glblf,    3, filter_size=(1,1), nonlinearity=rectify), name="global_feature");

    glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = layers.Upscale2DLayer(glblf, scale_factor=20);
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf =            layers.Deconv2DLayer(glblf,  3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);

    layer = layers.ElemwiseSumLayer([layer, glblf]);

    network = ReshapeLayer(layer, ([0], -1));
    layers.set_all_param_values(network, pickle.load(open(filename_model_ae, 'rb')));
    feat_var = lasagne.layers.get_output(feat_map, deterministic=True);
    mask_var = lasagne.layers.get_output(mask_map, deterministic=True);
    outp_var = lasagne.layers.get_output(network,  deterministic=True);

    return network, input_var, feat_var, mask_var, outp_var;
開發者ID:SBU-BMI，項目名稱:u24_lymphocyte，代碼行數:57，代碼來源:deep_conv_ae_spsparse_alt34_recon.py

示例5: build_autoencoder_network

# 需要導入模塊: from lasagne import layers [as 別名]
# 或者: from lasagne.layers import Upscale2DLayer [as 別名]
def build_autoencoder_network():
    input_var = T.tensor4('input_var');

    layer = layers.InputLayer(shape=(None, 3, PS, PS), input_var=input_var);
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer,  80, filter_size=(5,5), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 100, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Conv2DLayer(layer, 120, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    prely = batch_norm(layers.Conv2DLayer(layer, 140, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));

    featm    = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
    feat_map = batch_norm(layers.Conv2DLayer(featm, 100, filter_size=(1,1), nonlinearity=rectify, name="feat_map"));
    maskm    = batch_norm(layers.Conv2DLayer(prely, 100, filter_size=(1,1), nonlinearity=leaky_rectify));
    mask_rep = batch_norm(layers.Conv2DLayer(maskm,   1, filter_size=(1,1), nonlinearity=None),   beta=None, gamma=None);
    mask_map = SoftThresPerc(mask_rep, perc=99.9, alpha=0.5, beta=init.Constant(0.1), tight=100.0, name="mask_map");
    layer    = ChInnerProdMerge(feat_map, mask_map, name="encoder");

    layer = batch_norm(layers.Deconv2DLayer(layer, 140, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 120, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer, 100, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer = batch_norm(layers.Deconv2DLayer(layer,  80, filter_size=(5,5), stride=1, crop='same', nonlinearity=leaky_rectify));
    layer =            layers.Deconv2DLayer(layer,   3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);

    glblf = batch_norm(layers.Conv2DLayer(prely,  100, filter_size=(1,1), nonlinearity=leaky_rectify));
    glblf = layers.Pool2DLayer(glblf, pool_size=(20,20), stride=20, mode='average_inc_pad');
    glblf = batch_norm(layers.Conv2DLayer(glblf,   64, filter_size=(3,3), stride=1, pad='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Conv2DLayer(glblf,    3, filter_size=(1,1), nonlinearity=rectify), name="global_feature");

    glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 64, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = layers.Upscale2DLayer(glblf, scale_factor=20);
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 48, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf = batch_norm(layers.Deconv2DLayer(glblf, 32, filter_size=(3,3), stride=1, crop='same', nonlinearity=leaky_rectify));
    glblf =            layers.Deconv2DLayer(glblf,  3, filter_size=(1,1), stride=1, crop='same', nonlinearity=identity);

    layer = layers.ElemwiseSumLayer([layer, glblf]);

    network = ReshapeLayer(layer, ([0], -1));
    mask_var = lasagne.layers.get_output(mask_map);
    output_var = lasagne.layers.get_output(network);

    return network, input_var, mask_var, output_var;
開發者ID:SBU-BMI，項目名稱:u24_lymphocyte，代碼行數:55，代碼來源:deep_conv_ae_spsparse_alt34.py

示例6: build_decoder

# 需要導入模塊: from lasagne import layers [as 別名]
# 或者: from lasagne.layers import Upscale2DLayer [as 別名]
def build_decoder(net):
    net['uconv5_3']= ConvLayer(net['conv5_3'], 512, 3, pad=1)
    print "uconv5_3: {}".format(net['uconv5_3'].output_shape[1:])

    net['uconv5_2'] = ConvLayer(net['uconv5_3'], 512, 3, pad=1)
    print "uconv5_2: {}".format(net['uconv5_2'].output_shape[1:])

    net['uconv5_1'] = ConvLayer(net['uconv5_2'], 512, 3, pad=1)
    print "uconv5_1: {}".format(net['uconv5_1'].output_shape[1:])

    net['upool4'] = Upscale2DLayer(net['uconv5_1'], scale_factor=2)
    print "upool4: {}".format(net['upool4'].output_shape[1:])

    net['uconv4_3'] = ConvLayer(net['upool4'], 512, 3, pad=1)
    print "uconv4_3: {}".format(net['uconv4_3'].output_shape[1:])

    net['uconv4_2'] = ConvLayer(net['uconv4_3'], 512, 3, pad=1)
    print "uconv4_2: {}".format(net['uconv4_2'].output_shape[1:])

    net['uconv4_1'] = ConvLayer(net['uconv4_2'], 512, 3, pad=1)
    print "uconv4_1: {}".format(net['uconv4_1'].output_shape[1:])

    net['upool3'] = Upscale2DLayer(net['uconv4_1'], scale_factor=2)
    print "upool3: {}".format(net['upool3'].output_shape[1:])

    net['uconv3_3'] = ConvLayer(net['upool3'], 256, 3, pad=1)
    print "uconv3_3: {}".format(net['uconv3_3'].output_shape[1:])

    net['uconv3_2'] = ConvLayer(net['uconv3_3'], 256, 3, pad=1)
    print "uconv3_2: {}".format(net['uconv3_2'].output_shape[1:])

    net['uconv3_1'] = ConvLayer(net['uconv3_2'], 256, 3, pad=1)
    print "uconv3_1: {}".format(net['uconv3_1'].output_shape[1:])

    net['upool2'] = Upscale2DLayer(net['uconv3_1'], scale_factor=2)
    print "upool2: {}".format(net['upool2'].output_shape[1:])

    net['uconv2_2'] = ConvLayer(net['upool2'], 128, 3, pad=1)
    print "uconv2_2: {}".format(net['uconv2_2'].output_shape[1:])

    net['uconv2_1'] = ConvLayer(net['uconv2_2'], 128, 3, pad=1)
    print "uconv2_1: {}".format(net['uconv2_1'].output_shape[1:])

    net['upool1'] = Upscale2DLayer(net['uconv2_1'], scale_factor=2)
    print "upool1: {}".format(net['upool1'].output_shape[1:])

    net['uconv1_2'] = ConvLayer(net['upool1'], 64, 3, pad=1,)
    print "uconv1_2: {}".format(net['uconv1_2'].output_shape[1:])

    net['uconv1_1'] = ConvLayer(net['uconv1_2'], 64, 3, pad=1)
    print "uconv1_1: {}".format(net['uconv1_1'].output_shape[1:])

    net['output'] = ConvLayer(net['uconv1_1'], 1, 1, pad=0,nonlinearity=sigmoid)
    print "output: {}".format(net['output'].output_shape[1:])

    return net
開發者ID:imatge-upc，項目名稱:salgan，代碼行數:58，代碼來源:generator.py


注：本文中的lasagne.layers.Upscale2DLayer方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。