model.add(layers.Max Pooling1D(pool_size=4, strides=2)) # 池化层 model.add(layers.GRU(256, return_sequences=True)) # GRU层要足够大 model.add(layers.Flatten()) # 展平层 model.add(layers.Dropout(0.5)) # Dropout层 model.add(layers.Batch Normalization()) # 批标准化 model.add(layers.Dense(1, activation='sigmoid')) # 分类输出层 opt = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay=0.01) # 设置优化器 model.compile(optimizer=opt, # 优化器
