How is it that you would create a tensorflow vector from a tensorflow constant/variable etc?
For example I have a constant x and I want to create a vector which is [x].
I have tried the code below and it doesn't work. Any help would be appreciated.
x = tf.placeholder_with_default(1.0,[], name="x")
nextdd = tf.constant([x], shape=[1], dtype=tf.float32)
First I'd like to define a tensor for you:
Tensors are n-dimensional matrices. A rank 0 tensor is a scalar, e.g. 42. a rank 1 tensor is a Vector, e.g. [1,2,3], a rank 2 tensor is a matrix, a rank 3 tensor might be an image of shape [640, 480, 3] (640x480 resolution, 3 color channels). a rank 4 tensor might be a batch of such images of shape [10, 640, 480, 3] (10 640x480 images), etc.
Second, you have basically 4 types of tensors in Tensorflow.
1) Placeholders - these are tensors that you pass into tensorflow when you call sess.run. For example: sess.run([nextdd], {x:[1,2,3]}) creates a rank 1 tensor out of x.
2) Constants - these are fixed values as the name suggests. E.g. tf.constant(42) and should be specified at compile time, not runtime (eluding to your primary mistake here).
3) Computed tensors - x = tf.add(a,b) is a computed tensor, it's computed from a,b. Its value is not stored after the computation is finished.
4) Variables - These are mutable tensors that are kept around after the computation is complete. For example the weights of a neural network.
Now to address your question explicitly. x is already a tensor. If you were passing in a vector then it's a rank 1 tensor (aka a vector). You can use it just like you'd use a constant, computed tensor, or variable. They all work the same in operations. There is no reason for the nextdd line at all.
Now, nextdd fails becuase you tried to create a constant from a variable term, which isn't a defined operation. tf.constant(42) is well defined, that's what a constant is.
You could just use x directly, as in:
x = tf.placeholder_with_default(1.0,[], name="x")
y = tf.add(x, x)
sess = tf.InteractiveSession()
y.eval()
Result:
2.0
From you description, it looks like you want to use tf.expand_dims:
# 't' is a tensor of shape [2]
tf.shape(tf.expand_dims(t, 0)) # [1, 2]
tf.shape(tf.expand_dims(t, 1)) # [2, 1]
tf.shape(tf.expand_dims(t, -1)) # [2, 1]
nextddbe a const or a variable?