我正在尝试对Conv1D模型中的多个输入执行。所以我有 15 个大小为 1x1500 的输入,其中每个输入都是一系列层的输入。所以我有 15 个卷积模型,我想在全连接层之前合并它们。我已经在一个函数中定义了卷积模型,但是我无法理解如何调用该函数然后将它们合并。
def defineModel(nkernels, nstrides, dropout, input_shape):
model = Sequential()
model.add(Conv1D(nkernels, nstrides, activation='relu', input_shape=input_shape))
model.add(Conv1D(nkernels*2, nstrides, activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling1D(nstrides))
model.add(Dropout(dropout))
return model
models = {}
for i in range(15):
models[i] = defineModel(64,2,0.75,(64,1))
我已经成功地连接了 4 个模型,如下所示:
merged = Concatenate()([ model1.output, model2.output, model3.output, model4.output])
merged = Dense(512, activation='relu')(merged)
merged = Dropout(0.75)(merged)
merged = Dense(1024, activation='relu')(merged)
merged = Dropout(0.75)(merged)
merged = Dense(40, activation='softmax')(merged)
model = Model(inputs=[model1.input, model2.input, model3.input, model4.input], outputs=merged)
由于单独编写 15 层效率不高,我如何在 for 循环中为 15 层执行此操作?
呼啦一阵风
幕布斯6054654
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