import cntk as C C.parameter(init=[[1,2]]) # add .value to show the content # no empty but placeholders: C.input_variable((5,3)) # placeholder C.Parameter((5,3)) # learnable tensor C.parameter(shape=(5,3), init=0).value C.parameter(shape=(5,3), init=1).value C.parameter(shape=(5,3), init=C.initializer.uniform() ) C.parameter(shape=(5,3), init=C.initializer.normal() ) C.parameter(shape=(5,3), init=C.initializer.normal() ).shape C.squeeze(x[:,1],1) # be careful x[:,1] is not following Numpy convention C.times( C.Parameter((5,3)), C.Parameter((3,4)) ) # .eval to get the value from the ops C.reshape(C.parameter(shape=(5,3)), (3,5)) C.transpose(C.parameter(shape=(5,3)), perm=(1,0)) C.splice( C.parameter((5,3)), C.parameter((5,4)), 1 ) # no stack ops : use expand_dims + splice C.expand_dims(C.parameter((5,3)), 0 ) # careful: axis count 0 is equivalent to 1 in TF/Pytorch C.squeeze(C.parameter((5,1, 3)), 1) C.parameter(shape=(5,3), init=np.arange(10)) C.reduce_max( C.parameter((5,3)) ) C.squeeze(C.reduce_max( C.parameter((5,3)) , 1 ), 1) # keepdims not working C.element_max( C.parameter((5,3)), C.parameter((5,3)) )