Can't set trainable to false of custom layer.

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[x] Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short). Here is the model I'm trying to train

def ATSc(pretrained_weights=None, input_size=(1081,), embedding_layer=None):
    inputs = Input(input_size)
    embedding = embedding_layer(inputs)
    embmax = EmbeddingMax()(embedding)
    embmax._keras_history[0].kernel._trainable=False
    convs = []
    ngram_filters = [9, 18, 27, 36]
    n_filters = 64
    counter = 0
    for n_gram in ngram_filters:
        l_conv1 = Conv1D(filters=n_filters, kernel_size=1)(embmax)
        #l_conv1 = BatchNormalization()(l_conv1)
        l_relu1 = Activation("relu")(l_conv1)
        l_conv2 = Conv1D(
            filters=n_filters,
            kernel_size=n_gram,
            padding='same')(l_relu1)

        #l_conv2 = BatchNormalization()(l_conv2)
        l_relu2 = Activation("relu")(l_conv2)
        convs.append(l_relu2)

    l_concat = Concatenate()(convs)
    print(l_concat._keras_shape)
    l_blstm = Bidirectional(
        LSTM(
            units=10,
            activation="tanh",
            return_sequences=True))(l_concat)
    l_flatten = Flatten()(l_blstm)
    l_dropout = Dropout(0.5)(l_flatten)
    l_softmax = Dense(1, activation='sigmoid')(l_dropout)
    model = Model(input=inputs, output=l_softmax)
    metric = 'mean_absolute_error'
    loss_f = percent_error
    opt = Adam(lr=1e-3)
    # plot_model(model, to_file='model.png')
    model.summary()
    model.compile(optimizer=opt, loss=loss_f, metrics=[metric])
    if(pretrained_weights):
        model.load_weights(pretrained_weights, by_name=True)
    return model

Class Embmax:

from keras import backend as K
from keras.layers import Layer
import keras.initializers

class EmbeddingMax(Layer):

    def __init__(self ,**kwargs):
        super(EmbeddingMax, self).__init__(**kwargs)

    def build(self, input_shape):
        # Create a trainable weight variable for this layer.
        self.kernel = self.add_weight(name='kernel', 
                                      shape=(input_shape[1],input_shape[2]),
                                      initializer=keras.initializers.Zeros(),
                                      trainable=True)
        super(EmbeddingMax, self).build(input_shape)  # Be sure to call this at the end

    def call(self, x):
        return x+self.kernel

    def change_trainable(self, value):
        self.kernel.trainable=value

    def compute_output_shape(self, input_shape):
        return input_shape

I need embmax to be False on the first training, but when the model finishes training I need to set it to true. The thing is it won't change the number of trainable params. And if I try to set embmax.kernel.trainable=False I get AttributeError: can't set attribute. How do I change the property of that layer.

keras-team / keras

Can't set trainable to false of custom layer. #12296