Saving The Specific Layer From Within A Sequential Keras Model

I am building an auto-encoder and training the model so the targeted output is the same as the input. I am using a sequential Keras model. When I use model.predict I would like it

Solution 1:

After training, create a new model (model2) from your trained model (model) ending in your desired layer.

You can do so either with layer name:

(In model.summary(), your dense's layer 'name' with 256 neurons is dense_5)

from keras.models import Model
model2= Model(model.input,model.get_layer('dense_5').output)

Or with layer order:

(your dense layer with 256 neurons is fifth in model.summary())

from keras.models import Model
model2= Model(model.input,model.layers[4].output)

Then you can use predict

preds=model2.predict(x)

Solution 2:

layer.get_weights() returns the weights of a layer as a numpy array which can then be saved, for example with np.save.

To set the weights from a numpy array, layer.set_weights(weights) can be used.

You can access your layer either by name (model.get_layer(LAYER_NAME) or by its number (model.layers[LAYER_INDEX]).