ans = DAGNetwork with properties: Layers: [34×1 nnet.cnn.layer.Layer] Connections: [33×2 table] InputNames: {'input'} OutputNames: {'pitch'}

net = DAGNetwork with properties: Layers: [34×1 nnet.cnn.layer.Layer] Connections: [33×2 table] InputNames: {'input'} OutputNames: {'pitch'}

ans = 34×1带有图层的图层数组:1' input' Image input 1024×1×1 images 2' conv1' Convolution 1024 512×1×1 convolutions with stride [4 1] and padding 'same' 3 'conv1_relu' ReLU ReLU 4 'conv1- bn ' Batch Normalization Batch Normalization with 1024 channels 5 'conv1-maxpool' Max Pooling 2×1 Max Pooling with stride [2 1] and padding [0 0 0 0 0] 6 'conv1- Dropout ' Dropout 25% Dropout 7 'conv2' Convolution 128 64×1×1024 convolutions with stride [1 1] and padding 'same' 8 'conv2_relu' ReLU ReLU 9 'conv2- bn ' Batch Normalization Batch正常化128个频道10“conv2-maxpool”马克斯池2×1马克斯池步(2 - 1)和填充[0 0 0 0]11的conv2-dropout辍学25%辍学12 conv3卷积128×64×128旋转步[1]和填充“相同”13”conv3_relu ReLU ReLU 14“conv3-BN”批量标准化批量标准化与128个频道15“conv3-maxpool”马克斯池2×1马克斯池步(2 - 1)和填充[0 0 0 0]16“conv3-dropout”辍学25%辍学17 conv4卷积12864×1×128 convolutions with stride [1 1] and padding 'same' 18 'conv4_relu' ReLU ReLU 19 'conv4-BN' Batch Normalization Batch Normalization with 128 channels 20 'conv4-maxpool' Max Pooling 2×1 Max Pooling with stride [2 1] and padding [0 0 0 0 0] 21 'conv4-dropout' Dropout 25% Dropout 22 'conv5' Convolution 256 64×1×128 convolutions with stride [1 1] and padding 'same' 23 'conv5_relu' ReLU ReLU 24 'conv5- bn ' Batch Normalization Batch Normalization with 256 channels 25 'conv5-maxpool' Max Pooling2×1马克斯池步(2 - 1)和填充[0 0 0 0]26“conv5-dropout”辍学25%辍学27 conv6卷积512 64×1×256旋转步[1]和填充“相同”28日的conv6_relu ReLU ReLU 29“conv6-BN”批量标准化批量正常化30 512个频道“conv6-maxpool”马克斯池2×1马克斯池步(2 - 1)和填充[0 0 0 0]31“conv6-dropout”辍学25%辍学32“分类”完全连接360完全连接层33 classifier_sigmoid乙状结肠sigmoid 34 'pitch'回归输出均方误差

lastUtteranceEstimation =单217.2709