I am adding a dense layer before InceptionResNetV2 model(pre-trained) This is InceptionResNetV2 output
model_base = InceptionResNetV2(include_top=True, weights='imagenet')
x = model_base.get_layer('avg_pool').output
x = Dense(3, activation='softmax')(x)
This is the layer will be added
input1 = Input(shape=input_shape1)
pre1 = Conv2D(filters=3, kernel_size=(5, 5), padding='SAME',
input_shape=input_shape1, name='first_dense')(input1)
pre = Model(inputs=input1, outputs=pre1)
This is combining two models
after = Model(inputs=pre.output, outputs=x)
model = Model(inputs=input1, outputs=after.output)
model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])
use
pre.output
as
after.input
But it doesn't works . How can I resolve it?
First let's create a new model from model_base, because you want to get an earlier output.
Your code:
model_base = InceptionResNetV2(include_top=True, weights='imagenet')
x = model_base.get_layer('avg_pool').output
x = Dense(3, activation='softmax')(x)
New model_base
:
model_base = Model(model_base.input, x)
Now, it's important to pass the output pre1
to this model:
base_out = model_base(pre1)
That's it:
model = Model(input1, base_out)