Hi, here is a piece of code I have been working on -
self.x = T.tensor3("x")
self.y = T.ivector("y")
self.sequences = sequences
# Check this step for general dimshuffle
if self.sequences:
self.x_shuffled = self.x.dimshuffle((1,0,2))
# self.output = T.tensor3()
self.output,self.scan_updates = theano.scan(self.step,sequences=self.x_shuffled
# ,non_sequences=weights
,outputs_info=None)
def step(self,x):
init_layer = self.layers[0]
init_layer.set_inpt(x, self.mini_batch_size)
for j in range(1, len(self.layers)):
prev_layer, layer = self.layers[j-1], self.layers[j]
layer.set_inpt(
prev_layer.output, self.mini_batch_size)
self.output = self.layers[-1].output
return self.output
# define the (regularized) cost function, symbolic gradients, and updates
l2_norm_squared = sum([(layer.w**2).sum() for layer in self.layers])
cost = self.layers[-1].cost(self)+\
0.5*lmbda*l2_norm_squared/num_training_batches
grads = T.grad(cost, self.params)
updates = [(param, param-eta*grad)
for param, grad in zip(self.params, grads)]
# define functions to train a mini-batch, and to compute the
# accuracy in validation and test mini-batches.
i = T.lscalar() # mini-batch index
train_mb = theano.function(
[i], cost,
updates=updates + self.scan_updates,
givens={
self.x:
training_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size],
self.y:
training_y[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
},on_unused_input='ignore')
validate_mb_accuracy = theano.function(
[i], self.layers[-1].accuracy(self.y),
updates=self.scan_updates,
givens={
self.x:
validation_x[i*self.mini_batch_size: (i+1)*self.mini_batch_size],
self.y:
validation_y[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
},on_unused_input='ignore')
I have shown only the relevant part of the code here.
I am working with sequences of data points and hence you can see the step
function which i am calling repeatedly using theano.scan. The 'train_mb'
function works perfectly fine when I call it. The error is thrown in the
validate_mb_accuracy function.
The complete error with exception_verbosity=high is :
theano.gof.fg.MissingInputError: A variable that is an input to the graph
>> was neither provided as an input to the function nor given a value. A chain
>> of variables leading from this input to an output is [<TensorType(float64,
>> matrix)>, Reshape{2}.0, dot.0, Elemwise{add,no_inplace}.0, sigmoid.0,
>> Reshape{2}.0, dot.0, Elemwise{add,no_inplace}.0, sigmoid.0, Reshape{2}.0,
>> dot.0, Elemwise{add,no_inplace}.0, Softmax.0, argmax,
>> Elemwise{eq,no_inplace}.0, Sum{acc_dtype=int64}.0, mean]. This chain may
>> not be unique
>
> Backtrace when the variable is created:
>
> File "/home/daksh/PycharmProjects/nn/theano_ann_sequences/tester.py",
>> line 65, in <module>
>
> net = shallow()
>
> File "/home/daksh/PycharmProjects/nn/theano_ann_sequences/tester.py",
>> line 36, in shallow
>
>
>> FullyConnectedLayer(n_in=100,n_out=10,activation_fn=softmax)],mini_batch_size,sequences
>>
>> = True)
>
> File "/home/daksh/PycharmProjects/nn/theano_ann_sequences/network.py",
>> line 35, in __init__
>
> ,outputs_info=None)
>
>
Line 35 in the error points to the validation_mb function.
Any clues on why I am getting this error? I am even recording the updates
of the scan and passing them to the theano.function call as updates(This
was pointed out as the probable cause in some of the blogs.
Any help will be appreciated.
Thanks
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