比如1, channels, height, width，这样就可以在需要时通过reshape操作来加入batch_size维度并进行批处理。
例如,如果有一个形状为(batch_size, height, width, channels)的张量,可以使用以下代码将其拆分成batch_size个形状为(height, width, channels)的切片: 代码语言:txt 复制 slices = tf.split(tensor, batch_size, axis=0) 拆分后的切片可以用于并行处理,例如在分布式训练中,可以将不同的切片分配给不同的计...
1024是指的数据的尺寸,None指的batch size的大小,所以可以是任何数。 tf.placeholder(tf.float32, shape=[None, img_height, img_width, channels]) 类似地,后面几个是图片尺寸的参数,第一个参数为None,表示batch size的大小。 tf.transpose #‘x‘ is [[1 2 3] [4 5 6]] tf.transpose(x) ==> [...
1、在GoogleNet的3a模块中,假设输入特征图的大小是2828192,1x1卷积通道为64,3x3卷积通道为128,5x5卷积通道为32,如下图所示: 左边的卷积核参数计算如下: 192 × (1×1×64) +192 × (3×3×128) + 192 × (5×5×32) = 387072 而右图的3x3卷积层前加入通道数为96的...
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,代码行数:21,代码来源:datalayer.py...
view(batch_size, height, width, num_channels) # pad input to be disible by width and height, if needed input_feature = self.maybe_pad(input_feature, height, width) # [batch_size, height/2, width/2, num_channels] input_feature_0 = input_feature[:, 0::2, 0::2, :] # [batch...
img_size=(cfg.width, cfg.height), nb_channels=cfg.nb_channels, timesteps=cfg.timesteps, label_len=cfg.label_len, characters=cfg.characters)returntrain_generator, val_generator 开发者ID:kurapan,项目名称:CRNN,代码行数:20,代码来源:train.py ...
convenience init(width: Int, height: Int, featureChannelCount: Int, batchSize: Int) Creates a tensor without data, with the sizes and number of feature channels you specify.Deprecated convenience init(width: Int, height: Int, featureChannelCount: Int, batchSize: Int, data: ...
补充：numpy模拟此过程不存在维度对不上的问题
多类分类的准确性，（至少在本包中定义）只是每个类的类调用，即TP/（TP+FN）。真阴性在评分中不...