Using Keras masking layer with 2D convolutions (Conv2D)

Question

I'm trying to design a neural network including time dependent input with different lengths and I'm currently using a Masking layer. This network worked well with TensorFlow version 1.9.0 but after updating to version 1.11.0, I get the following error:

Layer conv2d_1 does not support masking, but was passed an input_mask: Tensor("cnn1/Reshape_2:0", shape=(?, 81, 81), dtype=bool)

Any idea on how to solve this problem?

I'm using the following code:

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, \
    TimeDistributed, Dense, Masking, Activation, BatchNormalization

model= Sequential()
# first layer
model.add(TimeDistributed(Masking(0., input_shape=(81,81,3)),
                                  input_shape=(None,81,81,3), name='mask'))
# CNN layers
model.add(TimeDistributed(Conv2D(filters=10,
                                 kernel_size=5,
                                 strides=1,
                                 padding='same'),
                         name='cnn1'))

model.add(Activation('relu', name='relu1'))
model.add(BatchNormalization())
model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))

# output layer
model.add(TimeDistributed(Dense(3, name='output')))
model.add(Activation('softmax'))

# compilation
model.compile(loss='categorical_crossentropy')

Answer 1

Replace your keras imports with tensorflow:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, \
    TimeDistributed, Dense, Masking, Activation, BatchNormalization