I am working on a project to replace layers by new layers to see if the changes affected positively or negatively. I want to then get the output feature map and input feature map after replacement. The issue I am having is that after a couple of changes, I get that I have multiple connections and a new column called ‘connnected to’ appears. Here are the summaries and the code I am using for replacing layers. I sometimes get this warning after replacing a convolutional layer with the code provided.
I have tried to create an Input layer and then use the same functional approach. My first layer being the input layer and the second the conv2d_0 layer. However, I get ValueError Disconnected from Graph for the input layer after two layer changes.
inputs = self.model.layers.input x = self.model.layers(inputs) for layer in self.model.layers[1:]: if layer.name == layer_name: new_layer = #creation of custom layer that generates output of same shape as replaced layer. x = new_layer(x) else: layer.trainable = False x = layer(x) self.model = tf.keras.Model(inputs, x)