CODEWITHSUNDEEP
pythonnumpytensorflow
Louie
Louie

Reputation: 67

Tensorflow runtime determine the shape of Tensor

Does Tensorflow support runtime determine the shape of Tensor?

The problem is to build a Constant tensor in runtime based on the input vector length_q. The number of columns of the target tensor is the sum of length_q. The code snippet is shown as follows, the length of length_q is fixed to 64.

T = tf.reduce_sum(length_q, 0)[0]
N = np.shape(length_q)[0]
wm = np.zeros((N, T), dtype=np.float32)

# Something inreletive.
count = 0
for i in xrange(N):
    ones = np.ones(length_q[i])
    wm[i][count:count+length_q[i]] = ones
    count += length_q[i]
return tf.Constant(wm)

Update

I want to create a dynamic Tensor according to the input length_q. length_q is some input vector (64*1). The new tensor's shape I want to create depends on the sum of length_q because in each batch the data in length_q changes. The current code snippet is as follows:

def some_matrix(length_q):
    T = tf.reduce_sum(length_q, 0)[0]
    N = np.shape(length_q)[0]
    wm = np.zeros((N, T), dtype=np.float32)
    count = 0
return wm

def network_inference(length_q):
    wm = tf.constant(some_matrix(length_q));
    ...

And the problem occurs probably because length_q is the placeholder and doesn't have summation operation. Are there some ways to solve this problem?

Upvotes: 1

Views: 1539

Answers (2)

mrry
mrry

Reputation: 126154

It sounds like the tf.fill() op is what you need. This op allows you to specify a shape as a tf.Tensor (i.e. a runtime value) along with a value:

def some_matrix(length_q):
    T = tf.reduce_sum(length_q, 0)[0]
    N = tf.shape(length_q)[0]
    wm = tf.fill([T, N], 0.0)
    return wm

Upvotes: 1

Raady
Raady

Reputation: 1726

Not clear about what you are calculating. If you need to calculate N shape, you can generate ones like this

T = tf.constant(20.0,tf.float32) # tf variable which is reduced sum , 20.0 is example float value 
T = tf.cast(T,tf.int32)      # columns will be integer only
N = 10                       # if numpy integer-  assuming np.shape giving 10
# N = length_q.getshape()[0] # if its a tensor, 'lenght_q' replace by your tensor name
wm = tf.ones([N,T],dtype=tf.float32) # N rows and T columns
sess = tf.Session()
sess.run(tf.initialize_all_variables())
sess.run(wm)

Upvotes: 0

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