I need to change the value of some elements of a tensor. I know what elements -- they are in a boolean tensor already.

I don't see how to do this in keras code. But if I were using TensorFlow code I would do something like this:

Conditional assignment of tensor values in TensorFlow

In python numpy, the code would look something like this:

x = np.zeros_like(sometensor) 
x[sometensor>0.5] = 1.0

In Keras code (and I'm using TF backend) here's my best attempt (does not work):

encoder_outputs_bin = k.backend.zeros_like(encoder_outputs, name="encoder_outputs_bin")
point_five = k.backend.constant(0.5, shape=k.backend.shape(encoder_outputs), name="point_five")
positives = k.backend.greater_equal(encoder_outputs, point_five)
encoder_outputs_bin[positives].assign(tf.ones(1)) # TF syntax -- might not work in keras

This answer is not really "assign", it's getting another tensor, but I believe it will do...

Also, what you intend to do will totally break backpropagation for these elements.

Knowing this:

positives = k.backend.greater_equal(encoder_outputs, 0.5)
positives = k.backend.cast(positives, k.backend.floatx())encoder_outputs = positives + ((1-positives)*encoder_outputs)