python - TimeDistributed Layers in Keras produce segmentation fault

Question

I'm searching now for some days a bug in my code. I want to write a TimeDitributed NN in python with keras to classify a sequence of images. Therefore I wrote this code:

    def create_rgb_model(self):

        x = Input(shape=((4,) + VIDEO_SHAPE + (3,)))

        base_model = mobilenet.MobileNet(weights='imagenet', include_top=False, input_shape=VIDEO_SHAPE+(3,))

        trainable = 9
        for layer in base_model.layers[:-trainable]:
            layer.trainable = False
        for layer in base_model.layers[-trainable:]:
            layer.trainable = True

        som = TimeDistributed(base_model)(x)
        som = TimeDistributed(GlobalMaxPooling2D())(som)
        som = GRU(64)(som)
        som = Dense(1024, activation='relu')(som)
        som = Dropout(0.3)(som)
        som = Dense(512, activation='relu')(som)
        som = Dropout(0.3)(som)
        som = Dense(128, activation='relu')(som)
        som = Dropout(0.3)(som)
        som = Dense(64, activation='relu')(som)
        som = Dense(self.num_classes, activation='softmax')(som)

        model = Model(inputs=x, outputs=som)

        model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'], run_eagerly=True)

        return model

And I use keras.utils.Sequence to feed fit() with data. I tried different batch sizes, but even with batch size 1, Python returns the segmentation fault. Furthermore I tried to run my code on a GPU and a CPU.

question from:https://stackoverflow.com/questions/65924408/timedistributed-layers-in-keras-produce-segmentation-fault

Answer 1

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