Keras replacing input layer

The solution from @MilindDeore did not work for me, unfortunately. While I can print the summary of the new model, I receive a “Matrix size incompatible” error upon prediction. I guess this makes sense, since the new input shape of the dense layer does not match the shape of the old dense layer weights.

Thus, here is another solution. The key for me was to use “_layers” instead of “layers”. The latter only seems to return a copy.

import keras
import numpy as np

def get_model():
    old_input_shape = (20, 20, 3)
    model = keras.models.Sequential()
    model.add(keras.layers.Conv2D(9, (3, 3), padding="same", input_shape=old_input_shape))
    model.add(keras.layers.MaxPooling2D((2, 2)))
    model.add(keras.layers.Flatten())
    model.add(keras.layers.Dense(1, activation="sigmoid"))
    model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(lr=0.0001), metrics=['acc'], )
    model.summary()
    return model

def change_model(model, new_input_shape=(None, 40, 40, 3)):
    # replace input shape of first layer
    model._layers[1].batch_input_shape = new_input_shape

    # feel free to modify additional parameters of other layers, for example...
    model._layers[2].pool_size = (8, 8)
    model._layers[2].strides = (8, 8)

    # rebuild model architecture by exporting and importing via json
    new_model = keras.models.model_from_json(model.to_json())
    new_model.summary()

    # copy weights from old model to new one
    for layer in new_model.layers:
        try:
            layer.set_weights(model.get_layer(name=layer.name).get_weights())
        except:
            print("Could not transfer weights for layer {}".format(layer.name))

    # test new model on a random input image
    X = np.random.rand(10, 40, 40, 3)
    y_pred = new_model.predict(X)
    print(y_pred)

    return new_model

if __name__ == '__main__':
    model = get_model()
    new_model = change_model(model)

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)

This should be pretty easy with kerassurgeon. First you need to install the library; depending on if you are using Keras through TensorFlow (with tf 2.0 and up) or Keras as a separate library, it needs to be installed in different ways.

For Keras in TF: pip install tfkerassurgeon (https://github.com/Raukk/tf-keras-surgeon).
For standalone Keras: pip install kerassurgeon (https://github.com/BenWhetton/keras-surgeon)

To replace the input (example with TF 2.0; currently untested code):

from tensorflow import keras  # or import keras for standalone version
from tensorflow.keras.layers import Input

model = keras.models.load_model('my_model.h5')
my_input_tensor = Input(input_shape=(260, 260, 3))

# or kerassurgeon for standalone Keras
from tfkerassurgeon import delete_layer, insert_layer

model = delete_layer(model.layers[0])
# inserts before layer 0
model = insert_layer(model.layers[0], my_input_tensor)

Unfortunately kerassurgeon did not support my model as I had frozen layers. I had to make a small change to @MilindDeore’s solution – replace model.layers.pop(0) to model._layers.pop(0) and it worked for me. Note that I am using tf.keras in TF 2.0.

For tf.keras in Tensorflow 2, using tfsurgeon did not work as I had custom layers.

What did work was changing layer._batch_input_size (note the underscore)

Hacky, but got the job done.