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本文详细剖析了一个名为Sequential_6的深度学习模型，该模型包含多个卷积层、最大池化层、全连接层及激活函数等组件。模型参数总数为1,259,521，具有训练和非训练参数。模型通过不断学习和优化，用于处理图像识别或分类任务。

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Model:"sequential_6"_________________________________________________________________Layer(type)OutputShapeParam#=================================================================conv2d_16(Conv2D)(None,148,148,64)1792_________________________________________________________________

activation_26(Activation)(None,148,148,64)0_________________________________________________________________

max_pooling2d_16(MaxPooling(None,74,74,64)0_________________________________________________________________

conv2d_17(Conv2D)(None,72,72,64)36928_________________________________________________________________

activation_27(Activation)(None,72,72,64)0_________________________________________________________________

max_pooling2d_17(MaxPooling(None,36,36,64)0_________________________________________________________________

conv2d_18(Conv2D)(None,34,34,64)36928_________________________________________________________________

activation_28(Activation)(None,34,34,64)0_________________________________________________________________

max_pooling2d_18(MaxPooling(None,17,17,64)0_________________________________________________________________

flatten_6(Flatten)(None,18496)0_________________________________________________________________

dense_11(Dense)(None,64)1183808_________________________________________________________________

activation_29(Activation)(None,64)0_________________________________________________________________

dropout_6(Dropout)(None,64)0_________________________________________________________________

dense_12(Dense)(None,1)65_________________________________________________________________

activation_30(Activation)(None,1)0=================================================================Totalparams:1,259,521Trainableparams:1,259,521Non-trainable params:0