Keras replacing input layer

Question

The code that I have (that I can\'t change) uses the Resnet with my_input_tensor as the input_tensor.

model1 = keras.applications.resnet50.ResNe         


        

Answer 1

Layers.pop(0) or anything like that doesn't work.

You have two options that you can try:

1.

You can create a new model with the required layers.

A relatively easy way to do this is to i) extract the model json configuration, ii) change it appropriately, iii) create a new model from it, and then iv) copy over the weights. I'll just show the basic idea.

i) extract the configuration

model_config = model.get_config()

ii) change the configuration

input_layer_name = model_config['layers'][0]['name']
model_config['layers'][0] = {
                      'name': 'new_input',
                      'class_name': 'InputLayer',
                      'config': {
                          'batch_input_shape': (None, 300, 300),
                          'dtype': 'float32',
                          'sparse': False,
                          'name': 'new_input'
                      },
                      'inbound_nodes': []
                  }
model_config['layers'][1]['inbound_nodes'] = [[['new_input', 0, 0, {}]]]
model_config['input_layers'] = [['new_input', 0, 0]]

ii) create a new model

new_model = model.__class__.from_config(model_config, custom_objects={})  # change custom objects if necessary

ii) copy weights

# iterate over all the layers that we want to get weights from
weights = [layer.get_weights() for layer in model.layers[1:]]
for layer, weight in zip(new_model.layers[1:], weights):
    layer.set_weights(weight)

2.

You can try a library like kerassurgeon (I am linking to a fork that works with the tensorflow keras version). Note that insertion and deletion operations only work under certain conditions such as compatible dimensions.

from kerassurgeon.operations import delete_layer, insert_layer

model = delete_layer(model, layer_1)
# insert new_layer_1 before layer_2 in a model
model = insert_layer(model, layer_2, new_layer_3)