It looks like one of the updates is being upcast to float64. Can you
explicitly cast them to float32?
On Thursday, July 28, 2016 at 1:50:32 PM UTC-7, Steve Leach wrote:
>
> I posted this in lasagne-users as well: Running into a strange error when
> trying to calculate updates. The end
> goal is a learned pruning/dropping layer, who's total number of
> activations along with how binary those
> activations are are part of the cost function - these activations from its
> own weights are then multiplied by the
> incoming layer in order to selectively turn off the least useful inputs.
> Once the mask is learned, the layers
> before and after can be resized. Seems sound in principle, but I'm having
> trouble convincing theano of the idea.
>
>
> The code:
>
> ------------------------------------------------------------------------------------------------------------------------------------------
> from lasagne.layers import InputLayer, DenseLayer, Layer, get_output
> from lasagne.nonlinearities import sigmoid, rectify, tanh
> from numpy.random import random
> from numpy.random import randint
> from numpy import zeros
> from theano import shared
> import theano as th
> import theano.tensor as T
> import numpy as np
> import lasagne as nn
> floatX = th.config.floatX
> batchsize = 4
> Width = 10
> Target = 5
> np.set_printoptions(precision=2, suppress=True)
>
> class Mask(Layer):
> def __init__(self, incoming, A=10**4, **kwargs):
> super(Mask, self).__init__(incoming, **kwargs)
> num_inputs = self.input_shape[1]
> self.A = A
> self.num_units = num_inputs
> W = ((random( (batchsize,num_inputs))-(1/2))*.001).astype(floatX)
> for n in range(1,W.shape[0]):
> W[n]=W[0]
> W = shared(W )
> self.W = self.add_param(W,(batchsize,num_inputs), name = 'W_Mask')
>
> def get_output_for(self, input, **kwargs):
> #return ((sigmoid(rectify(self.W*self.A) )-(1/2))*2)
> return ((tanh(self.A*self.W)/2)+.5 +(input*0) ).astype(floatX)
>
> def average(self):
> Acc = zeros((1,self.num_units))
> W = self.W.get_value()
> for w in W:
> Acc += w
> Acc /= batchsize
> W = zeros((batchsize,self.num_units))
> for n in range(batchsize):
> W[n] = Acc[:]
> self.W.set_value(W)
>
> class MaskLayer(Layer):
> def __init__(self, incoming, W, A=10**6, **kwargs):
> super(MaskLayer, self).__init__(incoming, **kwargs)
> num_inputs = self.input_shape[1]
> self.A = A
> self.num_units = num_inputs
> self.W = self.add_param(W,(1,num_inputs), name = 'W')
> def get_output_for(self, input, **kwargs):
> return (((tanh(self.A*self.W)/2)+.5)*input).astype(floatX)
>
>
> In = InputLayer((3,Width),input_var=x)
> M = Mask(In)
> LM = MaskLayer(In,M.W)
> LN = DenseLayer(LM,1,nonlinearity=sigmoid)
> X = (random((batchsize,Width))-.5).astype(floatX)
> x = T.matrix('x')
> Out = get_output(LN,x)
> M1 = get_output(M,x)
> params = nn.layers.get_all_params(LN)
>
> mask = th.function([x],M1)
>
> cost = ((Target - (M1.sum().mean() / batchsize))**2 +
> 1000*T.sin(M1*np.pi).mean() - Out.mean()).astype(floatX)
> Updates = nn.updates.sgd(cost,params,learning_rate=.001)
> #train = th.function([x],[cost],updates=Updates)
> run = th.function([x],[cost, Out, M1],on_unused_input='ignore',
> updates=Updates)
> print('In:\n',X)
> print('Mask:\n',M.get_output_for(X).eval())
> print('Masked:\n',LM.get_output_for(X).eval())
> print('Dense:\n',LN.get_output_for(X).eval())
>
>
> The Error:
>
> --------------------------------------------------------------------------------------------------------
>
> ---------------------------------------------------------------------------TypeError
> Traceback (most recent call
> last)/usr/local/lib/python3.4/dist-packages/theano/compile/pfunc.py in
> rebuild_collect_shared(outputs, inputs, replace, updates, rebuild_strict,
> copy_inputs_over, no_default_updates) 192 update_val =
> store_into.type.filter_variable(update_val,--> 193
> allow_convert=False) 194 except
> TypeError:
>
> /usr/local/lib/python3.4/dist-packages/theano/tensor/type.py in
> filter_variable(self, other, allow_convert) 233
> other=other,--> 234 self=self)) 235
> TypeError: Cannot convert Type TensorType(float64, matrix) (of Variable
> Elemwise{sub,no_inplace}.0) into Type TensorType(float32, matrix). You can
> try to manually convert Elemwise{sub,no_inplace}.0 into a TensorType(float32,
> matrix).
>
> During handling of the above exception, another exception occurred:
> TypeError Traceback (most recent call last)
> <ipython-input-70-60c11801fab9> in <module>() 68 Updates =
> nn.updates.sgd(cost,params,learning_rate=.001) 69 #train =
> th.function([x],[cost],updates=Updates)---> 70 run = th.function([x],[cost,
> Out, M1],on_unused_input='ignore', updates=Updates)
> 71 print('In:\n',X)
> 72 print('Mask:\n',M.get_output_for(X).eval())
>
> /usr/local/lib/python3.4/dist-packages/theano/compile/function.py in
> function(inputs, outputs, mode, updates, givens, no_default_updates,
> accept_inplace, name, rebuild_strict, allow_input_downcast, profile,
> on_unused_input) 320 on_unused_input=on_unused_input,
> 321 profile=profile,--> 322
> output_keys=output_keys) 323 # We need to add the flag check_aliased
> inputs if we have any mutable or
> 324 # borrowed used defined inputs
>
> /usr/local/lib/python3.4/dist-packages/theano/compile/pfunc.py in
> pfunc(params, outputs, mode, updates, givens, no_default_updates,
> accept_inplace, name, rebuild_strict, allow_input_downcast, profile,
> on_unused_input, output_keys) 441
> rebuild_strict=rebuild_strict, 442
> copy_inputs_over=True,--> 443
> no_default_updates=no_default_updates) 444 # extracting the arguments
> 445 input_variables, cloned_extended_outputs, other_stuff =
> output_vars
>
> /usr/local/lib/python3.4/dist-packages/theano/compile/pfunc.py in
> rebuild_collect_shared(outputs, inputs, replace, updates, rebuild_strict,
> copy_inputs_over, no_default_updates) 206 '
> function to remove broadcastable dimensions.') 207 --> 208
> raise TypeError(err_msg, err_sug) 209 assert update_val.type ==
> store_into.type 210
>
> TypeError: ('An update must have the same type as the original shared
> variable (shared_var=W_Mask, shared_var.type=TensorType(float32, matrix),
> update_val=Elemwise{sub,no_inplace}.0, update_val.type=TensorType(float64,
> matrix)).', 'If the difference is related to the broadcast pattern, you can
> call the tensor.unbroadcast(var, axis_to_unbroadcast[, ...]) function to
> remove broadcastable dimensions.')
>
>
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