python 2.7 - Converting theano tensor types -

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I have a calculation graph created with the Thano, it happens in this way: 

 < code> import from T importer as imported TNO, such as NPW1 = Nano. Shared (N.P.R.M.Rend (45,32). Staceype ('float32'), 'w1') b1 = thino. Shared (N.P.Randm.Rand (32). Stesip ('float32'), 'B1') W2 = Nano. Shared (N.P. Randem.Rand (32.3). Steppe 'Float32'), 'W2') B2 = Thino. Shared (Np.Rom.Rend (3) .stip ('float32'), 'b2') input = teamatics ('input') hidden = ttn (teadot (input, w1) + b1) Output = T.net Softmax (TDT (hidden, W2) + B2)   
 Now, mapping from a vector is a vector, however, the input is set as a matrix type So, I can pass several vectors together with mapping. I'm learning some machines and it makes the learning phase more efficient.  
 The problem is that after the learning phase, I want to see the mapping vector in the form of a vector so that I can calculate:  
  jac = theano.gradient. jacobian (output, wrt = input)   
  jacobian  complains that the input  is not TensorType (float32, vector) . Is there any way that I can change the input tensor type without rebuilding the entire computing graph?   
 
 
 Technically, this is a possible solution:  
  TNO import Import as TINO import as NAP w1 = theano.shared (np.random.rand (45,32). Sweetpe ('float32'), 'w1') b1 = theano.shared (np.random. ('Float32'), 'w2') b2 = theano. shared (np.random.rand (3). sweetpe ('float32'), 'b2') input = t. vector ('input') # will be made again! hidden = tt tn (tdt (input. Repp ((1, 45)), W1) + B1) Output = T.Nनेट.SoftMax (TD (Hidden, W2) + B2) # This driver is jac = theano.gradient.jacobian (output.rep ((- 1,)), wrt = input) .Upgrade ((1, 45, 3))   
  jac.eval ({input: np.random.rand (10 * 45)}). Size  result  (100, 45, 3 ) The problem is that it calculates the derivative generated across the batch index, so in theory the first  1x45  number all  10x3  Output (in a length of 10 length) can affect  
 For this, there are many solutions. You can take a diagonal on the first two axes, but unfortunately,!  
 I think this can be done with  scans , but this is another matter.

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