It's a tale of two nets, one that works fine and one that suddenly fails
mysteriously.
Here's the bit that's still working:
#---------------------------------------------
X,Y = sets.mnist32(); idx = np.arange(X.shape[0]);np.random.shuffle(idx)
def plot(x):
plt.imshow(x.reshape((32,32)),cmap='Greys_r');plt.show()
class Mask(Layer):
''' A layer which applies a nearly-bianarized multiplicative mask, w,
with a nonzero bias, b, to its input.
The absolute values of weights are raised to the power p before
bounding by the hyperbolic tangent function,
and before the addition of bais.'''
def __init__(self, incoming, b=.01, p=4, initial=.15, **kwargs):
super(Mask, self).__init__(incoming, **kwargs)
num_inputs = self.input_shape[1]
self.b = b; self.p = p
W = np.ones((num_inputs,)).astype(floatX)*initial
W = shared(W)
self.W = self.add_param(W,(num_inputs,), name = 'W_Mask')
def get_output_for(self,input,**kwargs):
return((tanh((abs(self.W)**self.p) )+self.b)*input)
# Large Net.
x = T.matrix('x')
In = InputLayer((None,X.shape[1]),input_var=x)
Noise = GaussianNoiseLayer(In)
Layer_1 = DenseLayer(Noise,num_units=(X.shape[1]),nonlinearity=tanh)
LM = Mask(Layer_1)
Layer_2 = DenseLayer(LM,num_units=X.shape[1],nonlinearity=sigmoid)
Out = get_output(Layer_2)
params = get_all_params(Layer_2)
cost = ((x-Out)**2).sum()+ \
.001* (regularize_layer_params(LM,l1))+ \
.01* (regularize_layer_params(Layer_2,l2))+
(regularize_layer_params(Layer_1,l2))
Updates = sgd(cost,params,learning_rate=.001)
train = function([x],[cost, Out], updates=Updates)
import tqdm
bar = tqdm.tqdm
batchsize = 500
batches = len(idx) // batchsize
epochs = 10
E = []
while epochs:
np.random.shuffle(idx)
for b in bar(range(batches)):
XX = X[idx[b*batchsize:(b+1)*batchsize]]
E.append(train(XX)[0])
#And here's where things go off the rails:
#---------------------------------------------------------------------------
#Extract small net
before = [Layer_1.W.get_value(),Layer_1.b.get_value()]
after = Layer_2.W.get_value()
m = LM.W.get_value()
active = []
for i,n in enumerate(range(len(m))):
if m[n] > .5:
active.append(i)
before = [before[0][:,active],before[1][active]]
after = after[active,:]
x = T.matrix('x')
In_2 = InputLayer((None,X.shape[1]), inpput_var=x)
Noise2 = GaussianNoiseLayer(In_2)
Layer_1_2 = DenseLayer(Noise2,num_units=171,nonlinearity=tanh)
Layer_1_2.W.set_value(before[0])
Layer_1_2.b.set_value(before[1])
Layer_2_2 = DenseLayer(Layer_1_2,num_units=1024,nonlinearity=sigmoid)
Layer_2_2.W.set_value(after)
#Layer_22.b.set_value(after[1])
Out_2 = get_output(Layer_2_2)
params_2 = get_all_params(Layer_2_2)
cost_2 = ((x-Out_2)**2).sum()+.01* (regularize_layer_params(Layer_2_2,l2))+
(regularize_layer_params(Layer_1_2,l2))
Updates_2 = sgd(cost_2,params_2,learning_rate=.001)
train_2 = function([x],[cost_2, Out_2], updates=Updates_2)
plot(X[0]);plt.show()
#plot(Out2.eval({x2:XX[:1]}));plt.show()
#And the error:
#------------------------------------------------------------------------------------------
---------------------------------------------------------------------------MissingInputError
Traceback (most recent call
last)<ipython-input-7-2d5d42706ca7> in <module>() 32 Updates_2 =
sgd(cost_2,params_2,learning_rate=.001) 33 ---> 34 train_2 =
function([x],[cost_2, Out_2], updates=Updates_2) 35 36
plot(X[0]);plt.show()
/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)
478 accept_inplace=accept_inplace, name=name, 479
profile=profile, on_unused_input=on_unused_input,--> 480
output_keys=output_keys) 481 482
/usr/local/lib/python3.4/dist-packages/theano/compile/function_module.py in
orig_function(inputs, outputs, mode, accept_inplace, name, profile,
on_unused_input, output_keys) 1781 profile=profile, 1782
on_unused_input=on_unused_input,-> 1783
output_keys=output_keys).create( 1784 defaults) 1785
/usr/local/lib/python3.4/dist-packages/theano/compile/function_module.py in
__init__(self, inputs, outputs, mode, accept_inplace, function_builder,
profile, on_unused_input, fgraph, output_keys) 1433 # OUTPUT
VARIABLES) 1434 fgraph, additional_outputs = std_fgraph(inputs,
outputs,-> 1435
accept_inplace) 1436 fgraph.profile = profile 1437 else:
/usr/local/lib/python3.4/dist-packages/theano/compile/function_module.py in
std_fgraph(input_specs, output_specs, accept_inplace) 174 175 fgraph
= gof.fg.FunctionGraph(orig_inputs, orig_outputs,--> 176
update_mapping=update_mapping) 177 178 for node in
fgraph.apply_nodes:
/usr/local/lib/python3.4/dist-packages/theano/gof/fg.py in __init__(self,
inputs, outputs, features, clone, update_mapping) 179 180 for
output in outputs:--> 181 self.__import_r__(output, reason="init")
182 for i, output in enumerate(outputs): 183
output.clients.append(('output', i))
/usr/local/lib/python3.4/dist-packages/theano/gof/fg.py in __import_r__(self,
variable, reason) 374 # Imports the owners of the variables 375
if variable.owner and variable.owner not in self.apply_nodes:--> 376
self.__import__(variable.owner, reason=reason) 377 if
(variable.owner is None and 378 not isinstance(variable,
graph.Constant) and
/usr/local/lib/python3.4/dist-packages/theano/gof/fg.py in __import__(self,
apply_node, check, reason) 416 "for more
information on this error." 417 % str(node)),-->
418 variable=r) 419 420 for node in
new_nodes:
MissingInputError: An input of the graph, used to compute Shape(input), was not
provided and not given a value.Use the Theano flag
exception_verbosity='high',for more information on this error.
Backtrace when the variable is created:
File "/usr/local/lib/python3.4/dist-packages/ipykernel/kernelbase.py", line
228, in dispatch_shell
handler(stream, idents, msg)
File "/usr/local/lib/python3.4/dist-packages/ipykernel/kernelbase.py", line
391, in execute_request
user_expressions, allow_stdin)
File "/usr/local/lib/python3.4/dist-packages/ipykernel/ipkernel.py", line
199, in do_execute
shell.run_cell(code, store_history=store_history, silent=silent)
File
"/usr/local/lib/python3.4/dist-packages/IPython/core/interactiveshell.py", line
2705, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File
"/usr/local/lib/python3.4/dist-packages/IPython/core/interactiveshell.py", line
2809, in run_ast_nodes
if self.run_code(code, result):
File
"/usr/local/lib/python3.4/dist-packages/IPython/core/interactiveshell.py", line
2869, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-7-2d5d42706ca7>", line 18, in <module>
In_2 = InputLayer((None,X.shape[1]), inpput_var=x)
File "/usr/local/lib/python3.4/dist-packages/lasagne/layers/input.py", line
63, in __init__
input_var = input_var_type(var_name)
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