how to get the exact value of the tensor variable and its type.

[rahashwini]

how to get the exact value of the tensor variable and its type.

[rahashwini]

Traceback (most recent call last):
File "<pyshell#33>", line 1, in
T.shape(prediction).eval()
File "C:\Users\Ashwini\AppData\Local\Programs\Python\Python36\lib\site-packages\theano\gof\graph.py", line 522, in eval
self._fn_cache[inputs] = theano.function(inputs, self)
File "C:\Users\Ashwini\AppData\Local\Programs\Python\Python36\lib\site-packages\theano\compile\function.py", line 317, in function
output_keys=output_keys)
File "C:\Users\Ashwini\AppData\Local\Programs\Python\Python36\lib\site-packages\theano\compile\pfunc.py", line 486, in pfunc
output_keys=output_keys)
File "C:\Users\Ashwini\AppData\Local\Programs\Python\Python36\lib\site-packages\theano\compile\function_module.py", line 1839, in orig_function
name=name)
File "C:\Users\Ashwini\AppData\Local\Programs\Python\Python36\lib\site-packages\theano\compile\function_module.py", line 1487, in init
accept_inplace)
File "C:\Users\Ashwini\AppData\Local\Programs\Python\Python36\lib\site-packages\theano\compile\function_module.py", line 181, in std_fgraph
update_mapping=update_mapping)
File "C:\Users\Ashwini\AppData\Local\Programs\Python\Python36\lib\site-packages\theano\gof\fg.py", line 175, in init
self.import_r(output, reason="init")
File "C:\Users\Ashwini\AppData\Local\Programs\Python\Python36\lib\site-packages\theano\gof\fg.py", line 346, in import_r
self.import(variable.owner, reason=reason)
File "C:\Users\Ashwini\AppData\Local\Programs\Python\Python36\lib\site-packages\theano\gof\fg.py", line 391, in import
raise MissingInputError(error_msg, variable=r)
theano.gof.fg.MissingInputError: Input 0 of the graph (indices start from 0), used to compute dot(X_train, W), was not provided and not given a value. Use the Theano flag exception_verbosity='high', for more information on this error.

Backtrace when that variable is created:

File "", line 1, in
File "C:\Users\Ashwini\AppData\Local\Programs\Python\Python36\lib\idlelib\run.py", line 144, in main
ret = method(*args, **kwargs)
File "C:\Users\Ashwini\AppData\Local\Programs\Python\Python36\lib\idlelib\run.py", line 474, in runcode
exec(code, self.locals)
File "G:\Implementation of the Project\Deep Neural Network for Learning to Rank\ispamm-group-lasso-deep-networks-179b38d3edb5\ispamm-group-lasso-deep-networks-179b38d3edb5\test7_list.py", line 130, in
input_var = T.matrix(name='X_train')

[f0k]

T.shape(prediction).eval()

eval() won't work unless you also give it some concrete input, such as T.shape(prediction).eval({input_var: your_input_data_as_a_numpy_array}).

If you're just interested in the shape of the prediction, however, you'll probably want to query the output layer of your network: output_layer.output_shape or network.output_shape, however it's called in your code. This will provide the shape tracked by Lasagne while creating the network, as far as it can be inferred from the shape given to the InputLayer. If the InputLayer shape is incomplete, you can use lasagne.layers.get_output_shape(network, (1, 2, 3, 4)) to compute the output shape for an input of shape (1, 2, 3, 4).