Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
macosx-x86_64-mkl
is not a good thing to use.
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).