Quick reply. The problem is that you are having 0 iteration in your recurence and we do not support that. So check why in your code you could end up with just 0 iteration.
Frédéric On Thu, Apr 26, 2018 at 3:12 AM jau94 <ijaur...@cmcrc.com> wrote: > Hi, > > I am using Theano == 1.0.1 for the Sequential Matching Network > <https://github.com/MarkWuNLP/MultiTurnResponseSelection>. I have tested > their code and it works well > > But now, I want to modify their *predict method* in *SMN_last.py. *I want > to be able to provide the test data at each time to the theano.function, > instead of only giving the index (line 195) of a fixed test data: > > val_model = theano.function([*index*], [y,predict,cost,error], givens=val_dic, >> on_unused_input='ignore') >> >> > What if the test data can vary each time I want to call to the > theano.function method. I don't want to be restricted to only select from > the batches of a fixed test data. > > Thefore, I have done the following change to the *predict method *in > *SMN_last.py*. I called this method *load_graph*. > > > > def load_graph(U,batch_size=20,max_l = >>> 100,hidden_size=100,word_embedding_size=100,session_hidden_size=50,session_input_size >>> =50, model_name = 'SMN_last.bin',save_file_pred=None, max_turn=10): >>> # for optimization >> >> hiddensize = hidden_size >> >> U = U.astype(dtype=theano.config.floatX) #Cast the embedding matrix >>> to a floatX tensor (THIS IS STILL A NUMPY ARRAY) >> >> rng = np.random.RandomState(3435) #A single random number is >>> generated and returned >> >> lsize, rsize = max_l,max_l >> >> >>> #DECLARE THE INPUT TENSORS!!! >> >> test_set = T.matrix(dtype='int32') #Creates a tensor matrix >> >> sessionmask = T.matrix() #Creates a tensor matrix >> >> lx = [] >> >> lxmask = [] >> >> for i in range(max_turn): #For max_turn (default=10), generate as >>> many tensor matrices >> >> lx.append(T.matrix()) >> >> lxmask.append(T.matrix()) >> >> index = T.lscalar() #Declare a tensor scalar >> >> rx = T.matrix('rx') #Declare a tensor matrix with a name. I think >>> this will be the response! >> >> rxmask = T.matrix() #Mask for the response as a tensor matrix >> >> y = T.ivector('y') #Declare a tensor scalar >> >> Words = theano.shared(value = U, name = "Words") #Declare a shared >>> variable with the embeddings >> >> >>> >>> llayer0_input = [] >> >> for i in range(max_turn): >> >> llayer0_input.append(Words[T.cast(lx[i].flatten(),dtype="int32")] >> >> .reshape((lx[i].shape[0],lx[i].shape[1],Words.shape[1]))) >> >> >>> rlayer0_input = >>> Words[T.cast(rx.flatten(),dtype="int32")].reshape((rx.shape[0],rx.shape[1],Words.shape[1])) >>> # input: word embeddings of the mini batch >> >> >>> # # #Why is divided in train, dev, test when we are predicting? >> >> # # #test_set = datasets >> >> >>> >>> q_embedding = [] >> >> offset = 2 * lsize >> >> >>> >>> test_set_lx = [] >> >> test_set_lx_mask = [] >> >> for i in range(max_turn): >> >> test_set_lx.append(T.cast(test_set[:,offset*i:offset*i + >>> lsize],dtype=theano.config.floatX)) >> >> test_set_lx_mask.append(T.cast(test_set[:,offset*i + >>> lsize:offset*i + 2*lsize],dtype=theano.config.floatX)) >> >> >>> test_set_rx = T.cast(test_set[:,offset*max_turn:offset*max_turn + >>> lsize],dtype=theano.config.floatX) >> >> test_set_rx_mask = T.cast(test_set[:,offset*max_turn >>> +lsize:offset*max_turn +2 *lsize],dtype=theano.config.floatX) >> >> test_set_session_mask = >>> T.cast(test_set[:,-max_turn-1:-1],dtype=theano.config.floatX) >> >> test_set_y =T.cast(test_set[:,-1],dtype='int32') #somehow put int32 >>> here >> >> >>> >>> test_dic = {} >> >> for i in range(max_turn): >> >> test_dic[lx[i]] = >>> test_set_lx[i][index*batch_size:(index+1)*batch_size] >> >> test_dic[lxmask[i]] = >>> test_set_lx_mask[i][index*batch_size:(index+1)*batch_size] >> >> test_dic[rx] = test_set_rx[index*batch_size:(index+1)*batch_size] >> >> test_dic[sessionmask] = >>> test_set_session_mask[index*batch_size:(index+1)*batch_size] >> >> test_dic[rxmask] = >>> test_set_rx_mask[index*batch_size:(index+1)*batch_size] >> >> test_dic[y] = test_set_y[index*batch_size:(index+1)*batch_size] >> >> >>> >>> sentence2vec = >>> GRU(n_in=word_embedding_size,n_hidden=hiddensize,n_out=hiddensize) >> >> >>> for i in range(max_turn): >> >> q_embedding.append(sentence2vec(llayer0_input[i],lxmask[i],True)) >> >> r_embedding = sentence2vec(rlayer0_input,rxmask,True) >> >> >>> pooling_layer = >>> ConvSim(rng,max_l,session_input_size,hidden_size=hiddensize) >> >> >>> poolingoutput = [] >> >> #test = >>> theano.function([index],pooling_layer(llayer0_input[-4],rlayer0_input,q_embedding[i],r_embedding),givens=test_dic,on_unused_input='ignore') >> >> >>> >>> for i in range(max_turn): >> >> poolingoutput.append(pooling_layer(llayer0_input[i],rlayer0_input, >> >> q_embedding[i],r_embedding)) >> >> >>> session2vec = >>> GRU(n_in=session_input_size,n_hidden=session_hidden_size,n_out=session_hidden_size) >> >> res = session2vec(T.stack(poolingoutput,1),sessionmask) >> >> classifier = LogisticRegression(res, session_hidden_size ,2,rng) >> >> >>> #cost = classifier.negative_log_likelihood(y) >> >> #error = classifier.errors(y) >> >> #opt = Adam() >> >> params = classifier.params >> >> params += sentence2vec.params >> >> params += session2vec.params >> >> params += pooling_layer.params >> >> params += [Words] >> >> >>> >>> load_params(params,model_name) >> >> >>> predict = classifier.predict_prob >> >> >>> #test_model = theano.function([test_set,index], >>> [y,predict,cost,error], givens=test_dic,on_unused_input='ignore') >> >> test_model = theano.function([test_set, index], [predict], >>> givens=test_dic, on_unused_input='ignore', allow_input_downcast=True) >> >> >>> return test_model >> >> > Note that the main changes are: > > 1. Addition of tensor *test_set=T.matrix(dtype='int32') *: The tensor > that will store the new input test > 2. Delete of the shared tensors, as test is already a tensor and not a > numpy array. > 3. Don't do any prediction. The method returns the created computational > graph *test_model* > 4. The theano.function now has two inputs: test_set and index > > Then, I want to run the model with. I have the same samples of a single > batch 200. The input looks like: > > test_set-> (200,1111) > > > > Test set is a numpy array, with dtype='int32'. I call to the forward pass > only for the first batch (index=0): > > predictions=test_model(test_set, 0) > > > In this part, I get the following error: > > Traceback (most recent call last): >> >> File "main_predict_single_batch.py", line 295, in <module> >> >> predict=model(test,0) >> >> File >>> "/data/ijauregi/Desktop/MyPythons/py27_theano_tensorflow/lib/python2.7/site-packages/theano/compile/function_module.py", >>> line 917, in __call__ >> >> storage_map=getattr(self.fn, 'storage_map', None)) >> >> File >>> "/data/ijauregi/Desktop/MyPythons/py27_theano_tensorflow/lib/python2.7/site-packages/theano/gof/link.py", >>> line 325, in raise_with_op >> >> reraise(exc_type, exc_value, exc_trace) >> >> File >>> "/data/ijauregi/Desktop/MyPythons/py27_theano_tensorflow/lib/python2.7/site-packages/theano/compile/function_module.py", >>> line 903, in __call__ >> >> self.fn() if output_subset is None else\ >> >> File >>> "/data/ijauregi/Desktop/MyPythons/py27_theano_tensorflow/lib/python2.7/site-packages/theano/scan_module/scan_op.py", >>> line 963, in rval >> >> r = p(n, [x[0] for x in i], o) >> >> File >>> "/data/ijauregi/Desktop/MyPythons/py27_theano_tensorflow/lib/python2.7/site-packages/theano/scan_module/scan_op.py", >>> line 952, in p >> >> self, node) >> >> File "theano/scan_module/scan_perform.pyx", line 215, in >>> theano.scan_module.scan_perform.perform >>> (/home/ijauregi/.theano/compiledir_Linux-3.10-el7.x86_64-x86_64-with-redhat-7.3-Maipo-x86_64-2.7.13-64/scan_perform/mod.cpp:2628) >> >> NotImplementedError: We didn't implemented yet the case where scan do 0 >>> iteration >> >> Apply node that caused the error: >>> forall_inplace,cpu,scan_fn}(Elemwise{minimum,no_inplace}.0, >>> Elemwise{sub,no_inplace}.0, Subtensor{int64:int64:int8}.0, >>> Elemwise{Cast{float32}}.0, IncSubtensor{InplaceSet;:int64:}.0, >>> <TensorType(float32, matrix)>, <TensorType(float32, matrix)>, >>> <TensorType(float32, matrix)>, <TensorType(float32, matrix)>, >>> <TensorType(float32, matrix)>, <TensorType(float32, matrix)>, >>> InplaceDimShuffle{x,0}.0, InplaceDimShuffle{x,0}.0, >>> InplaceDimShuffle{x,0}.0) >> >> Toposort index: 1122 >> >> Inputs types: [TensorType(int64, scalar), TensorType(float32, (False, >>> False, True)), TensorType(float32, 3D), TensorType(float32, (False, False, >>> True)), TensorType(float32, 3D), TensorType(float32, matrix), >>> TensorType(float32, matrix), TensorType(float32, matrix), >>> TensorType(float32, matrix), TensorType(float32, matrix), >>> TensorType(float32, matrix), TensorType(float32, row), TensorType(float32, >>> row), TensorType(float32, row)] >> >> Inputs shapes: [(), (0, 200, 1), (0, 200, 300), (0, 200, 1), (2, 200, >>> 100), (300, 100), (100, 100), (300, 100), (300, 100), (100, 100), (100, >>> 100), (1, 100), (1, 100), (1, 100)] >> >> Inputs strides: [(), (800, 4, 4), (1200, 1200, 4), (800, 4, 4), (80000, >>> 400, 4), (400, 4), (400, 4), (400, 4), (400, 4), (400, 4), (400, 4), (400, >>> 4), (400, 4), (400, 4)] >> >> Inputs values: [array(0), array([], shape=(0, 200, 1), dtype=float32), >>> array([], shape=(0, 200, 300), dtype=float32), array([], shape=(0, 200, 1), >>> dtype=float32), 'not shown', 'not shown', 'not shown', 'not shown', 'not >>> shown', 'not shown', 'not shown', 'not shown', 'not shown', 'not shown'] >> >> Outputs clients: >>> [[Subtensor{int64:int64:int8}(forall_inplace,cpu,scan_fn}.0, >>> ScalarFromTensor.0, ScalarFromTensor.0, Constant{1})]] >> >> >>> HINT: Re-running with most Theano optimization disabled could give you a >>> back-trace of when this node was created. This can be done with by setting >>> the Theano flag 'optimizer=fast_compile'. If that does not work, Theano >>> optimizations can be disabled with 'optimizer=None'. >> >> HINT: Use the Theano flag 'exception_verbosity=high' for a debugprint and >>> storage map footprint of this apply node. >> >> >> > > Could someone help me discover what is wrong? What is this error message > indicating to me? I couldn't find any good explanation. > The same input data works well in *SMN_last.py*. > > -- > > --- > You received this message because you are subscribed to the Google Groups > "theano-users" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to theano-users+unsubscr...@googlegroups.com. > For more options, visit https://groups.google.com/d/optout. > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. 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