我正在努力使
Lasagne tutorial适应LSTM模型.这就是我的代码是atm的方式:
def build_lstm(input_var=None):
num_inputs, num_units, num_classes = 4978, 300, 127
l_inp = lasagne.layers.InputLayer((None, None, num_inputs))
batchsize, seqlen, _ = l_inp.input_var.shape
l_lstm = lasagne.layers.LSTMLayer(l_inp, num_units=num_units)
l_shp = lasagne.layers.ReshapeLayer(l_lstm, (-1, num_units))
l_dense = lasagne.layers.DenseLayer(l_shp, num_units=num_classes)
l_out = lasagne.layers.ReshapeLayer(l_dense, (batchsize, seqlen, num_classes))
return l_out
train_in, test_in, train_out, test_out = build_dataset()
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
myLSTMNetwork = build_lstm()
#Loss evaluation
prediction = lasagne.layers.get_output(myLSTMNetwork)
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = loss.mean()
我可以毫无错误地构建我的模型.但在插入与Loss评估相关的代码后,我收到此错误:
File "code.py", line 110, in build_lstm
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/lasagne/objectives.py", line 146, in categorical_crossentropy
return theano.tensor.nnet.categorical_crossentropy(predictions, targets)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/theano/tensor/nnet/nnet.py", line 1906, in categorical_crossentropy
raise TypeError('rank mismatch between coding and true distributions')
TypeError: rank mismatch between coding and true distributions
最佳答案 我的输出层应该有batchsize * seqlen作为参数.
不批量化,seqlen.