Deep Learning Toolbox / Deep Learning Layers / Pooling Layers Description The Max Pooling 2D Layer block performs downsampling by dividing the input into rectangular pooling regions, then computing the maximum of each region. This block accepts 2-D image data in the SSC format (three dimensions...
「@Author:Runsen」@基础知识 1.Padding 2. FIlter/kernels 3.Pooling 4.Flattening 5.Fully Connected (Dense) 基础知识图像格式数据的输入通常是张量流中的四维数组 「(数值、宽度、高度、深度)」 「num_instance:「...
layer = MaxPooling2DLayer with properties: Name: '' HasUnpoolingOutputs: 0 NumOutputs: 1 OutputNames: {'out'} Hyperparameters PoolSize: [2 2] Stride: [2 2] PaddingMode: 'manual' PaddingSize: [0 0 0 0] The height and the width of the rectangular regions (pool size) are both 2....
ValueError: Negative dimension size caused by subtracting 2 from 1 for '{{node max_pooling2d_2/MaxPool}} = MaxPoolT=DT_FLOAT, data_format="NHWC", ksize=[1, 2, 2, 1], padding="VALID", strides=[1, 2, 2, 1]' with input shapes: [?,1,1,64]. ...
ValueError: Negative dimension size caused by subtracting 2 from 1 for 'max_pooling2d_28/MaxPool' (op: 'MaxPool') with input shapes: [?,1,32,64]. 问题原因 keras提供了两种后端,一种是Theano,另一种是Tensorflow 在卷积核使用不同的内核时,由于input_shape的格式不同会导致报错 Theano: input_...