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提交 95200d31 authored 作者: James Bergstra's avatar James Bergstra
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revisions to ProfileMode docs

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benchmark/regression/regression.py 0 → 100644
浏览文件 @ 95200d31
import theano
import numpy as N
from theano import tensor as T
from theano.tensor import nnet as NN
from theano.compile import module as M
class RegressionLayer(M.Module):
def __init__(self, input = None, target = None, regularize = True):
super(RegressionLayer, self).__init__() #boilerplate
# MODEL CONFIGURATION
self.regularize = regularize
# ACQUIRE/MAKE INPUT AND TARGET
if not input:
input = T.matrix('input')
if not target:
target = T.matrix('target')
# HYPER-PARAMETERS
self.stepsize = T.scalar() # a stepsize for gradient descent
# PARAMETERS
self.w = T.matrix() #the linear transform to apply to our input points
self.b = T.vector() #a vector of biases, which make our transform affine instead of linear
# REGRESSION MODEL
self.activation = T.dot(input, self.w) + self.b
self.prediction = self.build_prediction()
# CLASSIFICATION COST
self.classification_cost = self.build_classification_cost(target)
# REGULARIZATION COST
self.regularization = self.build_regularization()
# TOTAL COST
self.cost = self.classification_cost
if self.regularize:
self.cost = self.cost + self.regularization
# GET THE GRADIENTS NECESSARY TO FIT OUR PARAMETERS
self.grad_w, self.grad_b, grad_act = T.grad(self.cost, [self.w, self.b, self.prediction])
print 'grads', self.grad_w, self.grad_b
# INTERFACE METHODS
self.update = M.Method([input, target],
[self.cost, self.grad_w, self.grad_b, grad_act],
updates={self.w: self.w - self.stepsize * self.grad_w,
self.b: self.b - self.stepsize * self.grad_b})
self.apply = M.Method(input, self.prediction)
def params(self):
return self.w, self.b
def _instance_initialize(self, obj, input_size = None, target_size = None,
seed = 1827, **init):
# obj is an "instance" of this module holding values for each member and
# functions for each method
if input_size and target_size:
# initialize w and b in a special way using input_size and target_size
sz = (input_size, target_size)
rng = N.random.RandomState(seed)
obj.w = rng.uniform(size = sz, low = -0.5, high = 0.5)
obj.b = N.zeros(target_size)
obj.stepsize = 0.01
# here we call the default_initialize method, which takes all the name: value
# pairs in init and sets the property with that name to the provided value
# this covers setting stepsize, l2_coef; w and b can be set that way too
# we call it after as we want the parameter to superseed the default value.
M.default_initialize(obj,**init)
def build_regularization(self):
return T.zero() # no regularization!
class SpecifiedRegressionLayer(RegressionLayer):
""" XE mean cross entropy"""
def build_prediction(self):
# return NN.softmax(self.activation) #use this line to expose a slow subtensor
# implementation
return NN.sigmoid(self.activation)
def build_classification_cost(self, target):
self.classification_cost_matrix = (target - self.prediction)**2
#print self.classification_cost_matrix.type
self.classification_costs = T.sum(self.classification_cost_matrix, axis=1)
return T.sum(self.classification_costs)
def build_regularization(self):
self.l2_coef = T.scalar() # we can add a hyper parameter if we need to
return self.l2_coef * T.sum(self.w * self.w)
def test_module_advanced_example():
profmode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
data_x = N.random.randn(4, 10)
data_y = [ [int(x)] for x in (N.random.randn(4) > 0)]
model = SpecifiedRegressionLayer(regularize = False).make(input_size = 10,
target_size = 1,
stepsize = 0.1,
mode=profmode)
for i in xrange(1000):
xe, gw, gb, ga = model.update(data_x, data_y)
if i % 100 == 0:
print i, xe
pass
#for inputs, targets in my_training_set():
#print "cost:", model.update(inputs, targets)
print "final weights:", model.w
print "final biases:", model.b
profmode.print_summary()
doc/advanced/profilemode.txt
浏览文件 @ 95200d31
...@@ -52,58 +52,61 @@ profile, then call ``profmode.print_summary()``. This will provide you with ...@@ -52,58 +52,61 @@ profile, then call ``profmode.print_summary()``. This will provide you with
the desired timing information, indicating where your graph is spending most the desired timing information, indicating where your graph is spending most
of its time. of its time.
This is best shown through an example. Lets use the example of logistic This is best shown through an example.
regression, covered previously in the `Module`_ section. Lets use the example of logistic
regression. (Code for this example is in the file
.. _Module : module.html?highlight=nnet#advanced-example ``benchmark/regression/regression.py``.)
Compiling the module with ProfileMode and calling ``profmode.print_summary()`` Compiling the module with ProfileMode and calling ``profmode.print_summary()``
generates the following output: generates the following output:
.. code-block:: python .. code-block:: python
"""
ProfileMode.print_summary()
---------------------------
local_time 0.0508708953857 (Time spent running thunks) local_time 0.0749197006226 (Time spent running thunks)
Apply-wise summary: <fraction of local_time spent at this position> (<Apply position>, <Apply Op name>) Apply-wise summary: <fraction of local_time spent at this position> (<Apply position>, <Apply Op name>)
0.397 6 Subtensor{0, ::} 0.069 15 _dot22
0.110 18 <theano.tensor.blas.Gemm object at 0x15eb3d0> 0.064 1 _dot22
0.047 1 _dot22 0.053 0 InplaceDimShuffle{x,0}
0.033 0 InplaceDimShuffle{x,0} 0.049 2 InplaceDimShuffle{1,0}
0.032 2 InplaceDimShuffle{1,0} 0.049 10 mul
0.030 7 second 0.049 6 Elemwise{ScalarSigmoid{output_types_preference=<theano.scalar.basic.transfer_type object at 0x171e650>}}[(0, 0)]
0.029 8 <theano.tensor.nnet.SoftmaxWithBias object at 0x1619150> 0.049 3 InplaceDimShuffle{x}
0.028 16 Sum 0.049 4 InplaceDimShuffle{x,x}
0.027 3 InplaceDimShuffle{x} 0.048 14 Sum{0}
0.024 9 sub 0.047 7 sub
0.024 17 Sum{0} 0.046 17 mul
0.024 15 <theano.tensor.nnet.SoftmaxWithBiasDx object at 0x177fcd0> 0.045 9 sqr
0.023 10 sqr 0.045 8 Elemwise{sub}
0.023 12 Sum{1} 0.045 16 Sum
0.023 4 neg 0.044 18 mul
... (remaining 6 Apply instances account for 0.13 of the runtime) ... (remaining 6 Apply instances account for 0.25 of the runtime)
Op-wise summary: <fraction of local_time spent on this kind of Op> <Op name> Op-wise summary: <fraction of local_time spent on this kind of Op> <Op name>
0.397 Subtensor{0, ::} 0.139 * mul
0.110 * <theano.tensor.blas.Gemm object at 0x15eb3d0> 0.134 * _dot22
0.047 * _dot22 0.092 * sub
0.043 * Elemwise{Mul{output_types_preference=<theano.scalar.basic.transfer_type object at 0x176dbd0>}}[(0, 1)] 0.085 * Elemwise{Sub{output_types_preference=<theano.scalar.basic.transfer_type object at 0x1779f10>}}[(0, 0)]
0.033 * InplaceDimShuffle{x,0} 0.053 * InplaceDimShuffle{x,0}
0.032 * InplaceDimShuffle{1,0} 0.049 * InplaceDimShuffle{1,0}
0.030 * second 0.049 * Elemwise{ScalarSigmoid{output_types_preference=<theano.scalar.basic.transfer_type object at 0x171e650>}}[(0, 0)]
0.029 * <theano.tensor.nnet.SoftmaxWithBias object at 0x1619150> 0.049 * InplaceDimShuffle{x}
0.028 * Sum 0.049 * InplaceDimShuffle{x,x}
0.027 * InplaceDimShuffle{x} 0.048 * Sum{0}
0.024 * sub 0.045 * sqr
0.024 * Sum{0} 0.045 * Sum
0.024 * <theano.tensor.nnet.SoftmaxWithBiasDx object at 0x177fcd0> 0.043 * Sum{1}
0.023 * sqr 0.042 * Elemwise{Mul{output_types_preference=<theano.scalar.basic.transfer_type object at 0x17a0f50>}}[(0, 1)]
0.023 * Sum{1} 0.041 * Elemwise{Add{output_types_preference=<theano.scalar.basic.transfer_type object at 0x1736a50>}}[(0, 0)]
0.023 * neg 0.039 * Elemwise{Second{output_types_preference=<theano.scalar.basic.transfer_type object at 0x1736d90>}}[(0, 1)]
0.022 * Elemwise{Sub{output_types_preference=<theano.scalar.basic.transfer_type object at 0x1900850>}}[(0, 0)]
0.021 * Elemwise{Add{output_types_preference=<theano.scalar.basic.transfer_type object at 0x18ab350>}}[(0, 0)]
0.021 * Elemwise{Second{output_types_preference=<theano.scalar.basic.transfer_type object at 0x177f090>}}[(0, 1)]
0.020 * Elemwise{Neg{output_types_preference=<theano.scalar.basic.transfer_type object at 0x17b4690>}}[(0, 0)]
... (remaining 0 Ops account for 0.00 of the runtime) ... (remaining 0 Ops account for 0.00 of the runtime)
(*) Op is running a c implementation (*) Op is running a c implementation
"""
The summary has two components to it. In the first section called the Apply-wise The summary has two components to it. In the first section called the Apply-wise
summary, timing information is provided for the worst offending Apply nodes. This summary, timing information is provided for the worst offending Apply nodes. This
corresponds to individual nodes within your graph which take the longest to corresponds to individual nodes within your graph which take the longest to
......
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