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Python functions.MaxPooling2D方法代碼示例

本文整理匯總了Python中chainer.functions.MaxPooling2D方法的典型用法代碼示例。如果您正苦於以下問題：Python functions.MaxPooling2D方法的具體用法？Python functions.MaxPooling2D怎麽用？Python functions.MaxPooling2D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chainer.functions的用法示例。


在下文中一共展示了functions.MaxPooling2D方法的5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: __call__

# 需要導入模塊: from chainer import functions [as 別名]
# 或者: from chainer.functions import MaxPooling2D [as 別名]
def __call__(self, x):
        h = F.relu(self.conv1_1(x))
        h = F.relu(self.conv1_2(h))
        h = F.max_pooling_2d(h, 2, stride=2)
        h = F.relu(self.conv2_1(h))
        h = F.relu(self.conv2_2(h))
        h = F.max_pooling_2d(h, 2, stride=2)
        h = F.relu(self.conv3_1(h))
        h = F.relu(self.conv3_2(h))
        h = F.max_pooling_2d(h, 2, stride=2)
        h = F.relu(self.conv4_1(h))
        h = F.relu(self.conv4_2(h))
        h = F.spatial_pyramid_pooling_2d(h, 3, F.MaxPooling2D)
        h = F.tanh(self.fc4(h))
        h = F.dropout(h, ratio=.5, train=self.train)
        h = F.tanh(self.fc5(h))
        h = F.dropout(h, ratio=.5, train=self.train)
        h = self.fc6(h)
        return h
開發者ID:oyam，項目名稱:Semantic-Segmentation-using-Adversarial-Networks，代碼行數:21，代碼來源:spp_discriminator.py

示例2: __init__

# 需要導入模塊: from chainer import functions [as 別名]
# 或者: from chainer.functions import MaxPooling2D [as 別名]
def __init__(self):
        super(VGG, self).__init__()
        with self.init_scope():
            self.conv1_1 = L.Convolution2D(3, 64, 3, stride=1, pad=1)
            self.conv1_2 = L.Convolution2D(64, 64, 3, stride=1, pad=1)
            self.conv2_1 = L.Convolution2D(64, 128, 3, stride=1, pad=1)
            self.conv2_2 = L.Convolution2D(128, 128, 3, stride=1, pad=1)
            self.conv3_1 = L.Convolution2D(128, 256, 3, stride=1, pad=1)
            self.conv3_2 = L.Convolution2D(256, 256, 3, stride=1, pad=1)
            self.conv3_3 = L.Convolution2D(256, 256, 3, stride=1, pad=1)
            self.conv4_1 = L.Convolution2D(256, 512, 3, stride=1, pad=1)
            self.conv4_2 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.conv4_3 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.conv5_1 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.conv5_2 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.conv5_3 = L.Convolution2D(512, 512, 3, stride=1, pad=1)
            self.fc6 = L.Linear(25088, 4096)
            self.fc7 = L.Linear(4096, 4096)
            self.fc8 = L.Linear(4096, 1000)

        # Keep track of the pooling indices inside each function instance
        self.conv_blocks = [
            [self.conv1_1, self.conv1_2],
            [self.conv2_1, self.conv2_2],
            [self.conv3_1, self.conv3_2, self.conv3_3],
            [self.conv4_1, self.conv4_2, self.conv4_3],
            [self.conv5_1, self.conv5_2, self.conv5_3]
        ]
        self.deconv_blocks = []
        self.mps = [F.MaxPooling2D(2, 2) for _ in self.conv_blocks]
開發者ID:hvy，項目名稱:chainer-visualization，代碼行數:32，代碼來源:vgg.py

示例3: __init__

# 需要導入模塊: from chainer import functions [as 別名]
# 或者: from chainer.functions import MaxPooling2D [as 別名]
def __init__(self, in_channel, n_mid=64):
        w = math.sqrt(2)
        super(EncDec, self).__init__(
            enc=L.Convolution2D(in_channel, n_mid, 7, 1, 3, w),
            bn_m=L.BatchNormalization(n_mid),
            dec=L.Convolution2D(n_mid, n_mid, 7, 1, 3, w),
            bn_o=L.BatchNormalization(n_mid),
        )
        self.p = F.MaxPooling2D(2, 2, use_cudnn=False)
        self.inside = None
開發者ID:pfnet-research，項目名稱:chainer-segnet，代碼行數:12，代碼來源:segnet.py

示例4: setUp

# 需要導入模塊: from chainer import functions [as 別名]
# 或者: from chainer.functions import MaxPooling2D [as 別名]
def setUp(self):
        self.x = numpy.random.uniform(-1, 1, self.in_shape).astype('f')
        self.p = F.MaxPooling2D(2, 2, use_cudnn=False)
        self.pooled_y = self.p(self.x)
        self.gy = numpy.random.uniform(
            -1, 1, self.in_shape).astype(numpy.float32)
開發者ID:pfnet-research，項目名稱:chainer-segnet，代碼行數:8，代碼來源:test_upsampling_2d.py

示例5: __init__

# 需要導入模塊: from chainer import functions [as 別名]
# 或者: from chainer.functions import MaxPooling2D [as 別名]
def __init__(self, train=False):
        super(VGG16, self).__init__()
        self.trunk = [
            ('conv1_1', L.Convolution2D(3, 64, 3, 1, 1)),
            ('relu1_1', F.ReLU()),
            ('conv1_2', L.Convolution2D(64, 64, 3, 1, 1)),
            ('relu1_2', F.ReLU()),
            ('pool1', F.MaxPooling2D(2, 2)),
            ('conv2_1', L.Convolution2D(64, 128, 3, 1, 1)),
            ('relu2_1', F.ReLU()),
            ('conv2_2', L.Convolution2D(128, 128, 3, 1, 1)),
            ('relu2_2', F.ReLU()),
            ('pool2', F.MaxPooling2D(2, 2)),
            ('conv3_1', L.Convolution2D(128, 256, 3, 1, 1)),
            ('relu3_1', F.ReLU()),
            ('conv3_2', L.Convolution2D(256, 256, 3, 1, 1)),
            ('relu3_2', F.ReLU()),
            ('conv3_3', L.Convolution2D(256, 256, 3, 1, 1)),
            ('relu3_3', F.ReLU()),
            ('pool3', F.MaxPooling2D(2, 2)),
            ('conv4_1', L.Convolution2D(256, 512, 3, 1, 1)),
            ('relu4_1', F.ReLU()),
            ('conv4_2', L.Convolution2D(512, 512, 3, 1, 1)),
            ('relu4_2', F.ReLU()),
            ('conv4_3', L.Convolution2D(512, 512, 3, 1, 1)),
            ('relu4_3', F.ReLU()),
            ('pool4', F.MaxPooling2D(2, 2)),
            ('conv5_1', L.Convolution2D(512, 512, 3, 1, 1)),
            ('relu5_1', F.ReLU()),
            ('conv5_2', L.Convolution2D(512, 512, 3, 1, 1)),
            ('relu5_2', F.ReLU()),
            ('conv5_3', L.Convolution2D(512, 512, 3, 1, 1)),
            ('relu5_3', F.ReLU()),
            ('rpn_conv_3x3', L.Convolution2D(512, 512, 3, 1, 1)),
            ('rpn_relu_3x3', F.ReLU()),
        ]
        for name, link in self.trunk:
            if 'conv' in name:
                self.add_link(name, link)
開發者ID:uei，項目名稱:deel，代碼行數:41，代碼來源:VGG16.py


注：本文中的chainer.functions.MaxPooling2D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。