out_channels))returnnn.Sequential(*blk)# This model uses a smaller convolution kernel, stride, and padding and# removes the maximum pooling layernet=nn.Sequential(nn.Conv2d(in_channels,64,kernel_size=3,stride=1,padding=1),nn.BatchNorm2d(64),nn.ReLU())net.add_module(...
1) test_y = test_data.test_labels[:2000] class CNN(nn.Module): def __init__(self): super(CNN,self).__init__() self.conv1 = nn.Sequential( # 搭建卷积网络 nn.Conv2d( # shape(1,28,28) in_channels=1, # input height out_channels=16, # n_filters kernel_size=5, # filter的...
X, Y = tf.train.shuffle_batch(data_queues, num_threads=cfg.num_threads,batch_size=cfg.batch_size, capacity=cfg.batch_size*64, min_after_dequeue=cfg.batch_size*32, allow_smaller_final_batch=False)return(X, Y) 开发者ID:llSourcell,项目名称:capsule_networks,代码行数:13,代码来源:utils.py...
self.data_container = sampledata() self._index =0# data blob: holds a batch of N images, each with 3 channels# The height and width (100 x 100) are dummy valuestop[0].reshape(self._batch_size,3,224,224) top[1].reshape(self._batch_size) 开发者ID:luhaofang,项目名称:tripletloss,...
_index = 0 # data blob: holds a batch of N images, each with 3 channels # The height and width (100 x 100) are dummy values top[0].reshape(self._batch_size, 3, 224, 224) top[1].reshape(self._batch_size) Example #2Source File: data_layer.py From triplet with MIT License 6...
The flow cell is custom made by gluing a transducer (Ultrasonics World MPI-C7r8y5st0alDs 2-02107,_74, 019_560H) to a glass block (80 mm × 80 mm × 16 mm). Figure 2 shows a pictu4 roef 2o0f this flow cell which contains three channels of 5 mm diameter bored into a glass ...