本文整理汇总了Python中chainer.functions.MaxPooling2D方法的典型用法代码示例。如果您正苦于以下问题：Python functions.MaxPooling2D方法的具体用法？Python functions.MaxPooling2D怎么用？Python functions.MaxPooling2D使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类chainer.functions的用法示例。
在下文中一共展示了functions.MaxPooling2D方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __call__
# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import MaxPooling2D [as 别名]
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 示例2: __init__
# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import MaxPooling2D [as 别名]
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] 示例3: __init__
# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import MaxPooling2D [as 别名]
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 示例4: setUp
# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import MaxPooling2D [as 别名]
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) 示例5: __init__
# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import MaxPooling2D [as 别名]
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)