Typeerror: Das sendefähiges Muster des Eingangs ist für diese op falsch.

Question

Ich bin ein neuer Theanos-er .. mein Code erfolgreich mit Optimierung = None ausgeführt wurde. Aber wenn sie mit mode = FAST_RUN, ich habe mehrere Optimierungsversagen, die mir mehrere Tage gestört ... obwohl es immer noch nach diesem Ausfall führen könnte ... aber es ist tooooooooo langsam ..... und ich kann diese Fehler verengt op scannen ... die Fehlerinfo ist unten..könnte jemand Ideen haben?

ERROR (theano.gof.opt): SeqOptimizer apply <theano.gpuarray.opt.GraphToGPU object at 0xb788f2d0> 
ERROR (theano.gof.opt): Traceback: 
ERROR (theano.gof.opt): Traceback (most recent call last): 
    File "/usr/lib/python2.7/site-packages/theano/gof/opt.py", line 241, in apply 
    sub_prof = optimizer.optimize(fgraph) 
    File "/usr/lib/python2.7/site-packages/theano/gof/opt.py", line 87, in optimize 
    ret = self.apply(fgraph, *args, **kwargs) 
    File "/usr/lib/python2.7/site-packages/theano/gpuarray/opt.py", line 392, in apply 
    outputs = new_ops(*[mapping[i] for i in node.inputs], return_list=True) 
    File "/usr/lib/python2.7/site-packages/theano/gof/op.py", line 615, in __call__ 
    node = self.make_node(*inputs, **kwargs) 
    File "/usr/lib/python2.7/site-packages/theano/gpuarray/elemwise.py", line 419, in make_node 
    res = DimShuffle.make_node(self, input) 
    File "/usr/lib/python2.7/site-packages/theano/tensor/elemwise.py", line 202, in make_node 
    % (self.input_broadcastable, ib))) 
TypeError: The broadcastable pattern of the input is incorrect for this op. Expected (True, False, False, False, True, False), got (False, False, False, False, False, False). 

ERROR (theano.gof.opt): Optimization failure due to: local_gpua_reshape 
ERROR (theano.gof.opt): node: Reshape{6}(Elemwise{add,no_inplace}.0, HostFromGpu(gpuarray).0) 
ERROR (theano.gof.opt): TRACEBACK: 
ERROR (theano.gof.opt): Traceback (most recent call last): 
    File "/usr/lib/python2.7/site-packages/theano/gof/opt.py", line 2036, in process_node 
    remove=remove) 
    File "/usr/lib/python2.7/site-packages/theano/gof/toolbox.py", line 569, in replace_all_validate_remove 
    chk = fgraph.replace_all_validate(replacements, reason) 
    File "/usr/lib/python2.7/site-packages/theano/gof/toolbox.py", line 518, in replace_all_validate 
    fgraph.replace(r, new_r, reason=reason, verbose=False) 
    File "/usr/lib/python2.7/site-packages/theano/gof/fg.py", line 486, in replace 
    ". The type of the replacement must be the same.", old, new) 
BadOptimization: BadOptimization Error 
    Variable: id 4166841488 HostFromGpu(gpuarray).0 
    Op HostFromGpu(gpuarray)(GpuReshape{6}.0) 
    Value Type: <type 'NoneType'> 
    Old Value: None 
    New Value: None 
    Reason: local_gpua_reshape. The type of the replacement must be the same. 
    Old Graph: 
    Reshape{6} [id A] <TensorType(float32, (True, False, False, False, True, False))> '' 
    |Elemwise{add,no_inplace} [id B] <TensorType(float32, 3D)> '' 
    | |Elemwise{mul} [id C] <TensorType(float32, 3D)> '' 
    | | |Elemwise{add,no_inplace} [id D] <TensorType(float32, (True, False, False))> '' 
    | | | |Elemwise{add,no_inplace} [id E] <TensorType(float32, (True, False, False))> '' 
    | | | | |Elemwise{mul} [id F] <TensorType(float32, (True, False, False))> '' 
    | | | | |Elemwise{mul} [id G] <TensorType(float32, (True, False, False))> '' 
    | | | |InplaceDimShuffle{x,0,1} [id H] <TensorType(float32, (True, False, False))> '' 
    | | | |Dot22 [id I] <TensorType(float32, matrix)> '' 
    | | |Elemwise{true_div,no_inplace} [id J] <TensorType(float32, (False, False, True))> '' 
    | | |InplaceDimShuffle{0,1,x} [id K] <TensorType(float32, (False, False, True))> '' 
    | | | |Elemwise{exp,no_inplace} [id L] <TensorType(float32, matrix)> '' 
    | | |InplaceDimShuffle{x,0,x} [id M] <TensorType(float32, (True, False, True))> '' 
    | |  |Sum{axis=[0], acc_dtype=float64} [id N] <TensorType(float32, vector)> '' 
    | |Elemwise{mul} [id O] <TensorType(float32, 3D)> '' 
    | |Reshape{3} [id P] <TensorType(float32, 3D)> '' 
    | | |Dot22 [id Q] <TensorType(float32, matrix)> '' 
    | | | |Reshape{2} [id R] <TensorType(float32, matrix)> '' 
    | | | |HostFromGpu(gpuarray) [id S] <TensorType(float32, matrix)> '' 
    | | |HostFromGpu(gpuarray) [id T] <TensorType(int64, vector)> '' 
    | | |<GpuArrayType<None>(int64, vector)> [id U] <GpuArrayType<None>(int64, vector)> 
    | |HostFromGpu(gpuarray) [id V] <TensorType(float32, (True, True, True))> '' 
    |  |<GpuArrayType<None>(float32, (True, True, True))> [id W] <GpuArrayType<None>(float32, (True, True, True))> 
    |HostFromGpu(gpuarray) [id X] <TensorType(int64, vector)> '' 
    |<GpuArrayType<None>(int64, vector)> [id Y] <GpuArrayType<None>(int64, vector)> 

    New Graph: 
    HostFromGpu(gpuarray) [id Z] <TensorType(float32, 6D)> '' 
    |GpuReshape{6} [id BA] <GpuArrayType<None>(float32, 6D)> '' 
    |GpuFromHost<None> [id BB] <GpuArrayType<None>(float32, 3D)> '' 
    | |Elemwise{add,no_inplace} [id B] <TensorType(float32, 3D)> '' 
    |HostFromGpu(gpuarray) [id X] <TensorType(int64, vector)> '' 


Hint: relax the tolerance by setting tensor.cmp_sloppy=1 
    or even tensor.cmp_sloppy=2 for less-strict comparison 

` und dieser Hinweis hat keine Hilfe ...

Answer 1

der ursprüngliche Code funktioniert OK. und mein inkrementeller Code ist wie folgt:

def sample_subspace(ctx, next_state_0, next_state_1, tparams, options, maxlen, k): 
    # init sample with -1 
    sample = -1 * tensor.ones((k, maxlen)).astype('int64') 
    sample_scores = tensor.zeros((k)).astype('float32') 
    alignment = tensor.zeros((k, maxlen, maxlen)).astype('float32') 
    # sample the first k words 
    next_w = -1 * tensor.ones((1, 1)).astype('int64') 

    next_state_1 = tensor.alloc(0., 1, next_state_0.shape[-1]) 
    proj, proj_1, next_probs = decode_one_word(next_w, ctx, next_state_0, next_state_1, tparams, options) 
    new_next_state_0 = tensor.tile(proj[0], (k, 1)) 
    new_next_state_1 = tensor.tile(proj_1[0], (k, 1)) 
    hyp_scores = tensor.zeros(1).astype('float32') 
    cand_scores = hyp_scores[:, None] - tensor.log(next_probs) 
    cand_flat = cand_scores.flatten() 
    ranks_flat = cand_flat.argsort()[:k] 
    scores_flat = cand_flat.sort()[:k] 
    dec_alphas = proj[2] 

    sample = tensor.set_subtensor(sample[:,0], ranks_flat) 
    sample_scores = tensor.inc_subtensor(sample_scores[:], scores_flat) 
    padx0 = tensor.alloc(0., maxlen) 
    padx0 = tensor.set_subtensor(padx0[:dec_alphas[0].shape[0]], dec_alphas[0][:]) 
    alignment = tensor.set_subtensor(alignment[:,0], tensor.tile(padx0[None,:], (k, 1))) 

    new_next_w = ranks_flat[None, :] 

    def _one_step(next_w, next_state_0, next_state_1, score=None, alignment=None): 
     ctx_r = tensor.tile(ctx, (1, k, 1)) 
     proj, proj_1, next_probs = decode_one_word(next_w, ctx_r, next_state_0, next_state_1, tparams, options) 
     new_next_state_0 = proj[0] 
     new_next_state_1 = proj_1[0] 
     hyp_scores = tensor.zeros(1).astype('float32') 
     cand_scores = hyp_scores[:, None] - tensor.log(next_probs) 
     new_next_w = tensor.argmin(cand_scores, axis=1, keepdims=True).dimshuffle(1,0) 
     dec_alphas = proj[2] 
     score = tensor.min(cand_scores, axis=0, keepdims=True) 
     alignment = dec_alphas 
     return new_next_w, new_next_state_0, new_next_state_1, score, alignment 

    results, updates = theano.scan(fn=_one_step, 
            outputs_info=[new_next_w, new_next_state_0, new_next_state_1, None, None], 
            n_steps=maxlen - 1) 

    sample = tensor.set_subtensor(sample[:, 1:], results[0].dimshuffle(2,0,1)[:,:,0]) 
    #sample = theano.printing.Print('samples')(sample) 
    sample_scores = tensor.inc_subtensor(sample_scores[:], results[3].sum(0)[:,0]) 
    padx = tensor.alloc(0., k, maxlen-1, maxlen) 
    padx = tensor.set_subtensor(padx[:, :, :results[4].shape[2]], results[4].dimshuffle(1,0,2)) 
    alignment = tensor.set_subtensor(alignment[:,1:], padx) 

    # decode k sequences 
    return sample, sample_scores, alignment