java
and/or sig_jvm
so they get categorized and filtered properly
tf.stack
to combine a = [1, 2, 3]
and b = [4, 5]
into tensor c which is [1, 2, 3, 4, 5]
. Can you elaborate? As far as I can see, tf.stack
will just stack the two tensors on top of each other instead of concatenating the vectors into a [1, 5]
shape.
a = [1, 2, 3]
and b = [[4, 5], [6, 7]]
and I want to end up with c = [1, 2, 3, 4, 5, 6, 7]
. I am currently using c = tf.concat([a], tf.reshape(b, shape=[-1]))
but I was wondering if there an easier/more-readable way to collapse and concatenate everything in a single step.
I've got inputs with different dtypes. One is an int64, another is a float64. I saw a tutorial on feeding Tensorflow multiple inputs where they used tf.concatenate()
to combine the various inputs but when I tried to do the same I got:
Tensor conversion requested dtype int64 for Tensor with dtype float32: <tf.Tensor 'concatenate/Cast:0' shape=(None, 4180, 5) dtype=float32>
Any ideas?
Input
node?
tf.cond
(i.e. if statements).
cos
and sin
of the timestamp. Is there a point to passing both to the model as input? Or do they only use one of them?
math.nan
in place of sensor data but this broke training (the loss function returned nan
). What should I do in this case? Set the values to zero? Set them to random values? Ideally I want the model to skip them and not use them for training.