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提交 9f000926 authored 作者: Harm de Vries's avatar Harm de Vries
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Replaced softmax with either softmax_op or softmax_graph

上级 c6ccaeeb
隐藏空白字符变更
内嵌 并排
正在显示 包含 90 行增加 和 91 行删除
theano/tensor/nnet/nnet.py
浏览文件 @ 9f000926
......@@ -413,7 +413,7 @@ class Softmax(gof.Op):
def grad(self, inp, grads):
x, = inp
g_sm, = grads
sm = softmax(x)
sm = softmax_op(x)
return [softmax_grad(g_sm, sm)]
def R_op(self, inputs, eval_points):
......@@ -578,7 +578,7 @@ def softmax_graph(c):
def local_softmax_with_bias(node):
"""Try to turn softmax(sum_of_stuff) -> softmax_w_bias(matrix, bias)
"""
if node.op == softmax:
if node.op == softmax_op:
x, = node.inputs
if x.owner and x.owner.op == tensor.add:
vectors = []
......@@ -1406,7 +1406,7 @@ def crossentropy_to_crossentropy_with_softmax(fgraph):
if node.op == crossentropy_categorical_1hot:
nll, = node.outputs
sm, one_of_n = node.inputs
if sm.owner and sm.owner.op == softmax:
if sm.owner and sm.owner.op == softmax_op:
x, = sm.owner.inputs
new_nll, new_sm, new_am = crossentropy_softmax_argmax_1hot_with_bias(x,
tensor.zeros_like(x[0]), one_of_n)
......@@ -1556,7 +1556,7 @@ def local_advanced_indexing_crossentropy_onehot(node):
except Exception:
pass
if sm is not None and sm.owner and sm.owner.op in (softmax,
if sm is not None and sm.owner and sm.owner.op in (softmax_op,
softmax_with_bias):
sm_w_bias = local_softmax_with_bias.transform(sm.owner)
if sm_w_bias:
......@@ -1586,7 +1586,7 @@ def local_advanced_indexing_crossentropy_onehot_grad(node):
except Exception:
return
if (sm is not None) and sm.owner and (sm.owner.op in (softmax,
if (sm is not None) and sm.owner and (sm.owner.op in (softmax_op,
softmax_with_bias)):
sm_w_bias = local_softmax_with_bias.transform(sm.owner)
if sm_w_bias:
......@@ -2056,7 +2056,7 @@ def make_out_pattern(X):
return out_var
local_log_softmax = gof.PatternSub(in_pattern=(tensor.log, (softmax, 'x')),
local_log_softmax = gof.PatternSub(in_pattern=(tensor.log, (softmax_op, 'x')),
out_pattern=(make_out_pattern, 'x'),
allow_multiple_clients=True)
......
theano/tensor/nnet/tests/test_nnet.py
浏览文件 @ 9f000926
......@@ -22,7 +22,7 @@ from theano.tensor.nnet import (categorical_crossentropy,
CrossentropyCategorical1Hot,
CrossentropyCategorical1HotGrad,
sigmoid, softplus,
Softmax, softmax_op, SoftmaxWithBias,
Softmax, softmax_op, softmax_graph, SoftmaxWithBias,
softmax_grad,
softmax_with_bias, SoftmaxGrad,
Prepend_scalar_constant_to_each_row,
......@@ -52,22 +52,22 @@ class T_Softmax(utt.InferShapeTester):
def test0(self):
def f(a):
return softmax(a)[:, 0]
return softmax_op(a)[:, 0]
utt.verify_grad(f, [numpy.random.rand(3, 4)])
def test1(self):
def f(a):
return softmax(a)[:, 1]
return softmax_op(a)[:, 1]
utt.verify_grad(f, [numpy.random.rand(3, 4)])
def test2(self):
def f(a):
return softmax(a)[:, 2]
return softmax_op(a)[:, 2]
utt.verify_grad(f, [numpy.random.rand(3, 4)])
def test3(self):
def f(a):
return softmax(a)[:, 3]
return softmax_op(a)[:, 3]
utt.verify_grad(f, [numpy.random.rand(3, 4)])
def test_infer_shape(self):
......@@ -78,14 +78,14 @@ class T_Softmax(utt.InferShapeTester):
def test_vector(self):
x = T.vector()
f = theano.function([x], softmax(x))
f = theano.function([x], softmax_op(x))
xv = numpy.random.randn(6).astype(config.floatX)
assert numpy.allclose(f(xv), numpy.exp(xv) / numpy.exp(xv).sum())
def test_vector_grad(self):
def f(a):
return softmax(a)
return softmax_op(a)
utt.verify_grad(f, [numpy.random.rand(4)])
......@@ -127,10 +127,10 @@ class T_SoftmaxWithBias(utt.InferShapeTester):
vbias = theano.shared(value=0.1, name='vbias') # 0.01
hid = T.vector('hid')
f = theano.function([hid],
T.nnet.softmax(T.dot(hid, W.T) + vbias))
T.nnet.softmax_op(T.dot(hid, W.T) + vbias))
ops = [node.op for node in f.maker.fgraph.toposort()]
assert softmax_with_bias not in ops
assert softmax in ops
assert softmax_op in ops
f([0, 1, 0])
# print f.maker.fgraph.toposort()
......@@ -398,7 +398,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
fgraph = gof.FunctionGraph(
[x, one_of_n],
[op(softmax(x), one_of_n)])
[op(softmax_op(x), one_of_n)])
assert fgraph.outputs[0].owner.op == op
theano.compile.mode.optdb.query(
......@@ -414,7 +414,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
op = crossentropy_categorical_1hot
fgraph = gof.FunctionGraph(
[x, one_of_n],
[op(softmax(x), one_of_n)])
[op(softmax_op(x), one_of_n)])
assert fgraph.outputs[0].owner.op == op
theano.compile.mode.optdb.query(
......@@ -432,7 +432,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
fgraph = gof.FunctionGraph(
[x, b, one_of_n],
[op(softmax(x + b), one_of_n)])
[op(softmax_op(x + b), one_of_n)])
assert fgraph.outputs[0].owner.op == op
# print 'BEFORE'
......@@ -464,7 +464,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
fgraph = gof.FunctionGraph(
[x, b, c, one_of_n],
[op(softmax(T.add(x, b, c)), one_of_n)])
[op(softmax_op(T.add(x, b, c)), one_of_n)])
assert fgraph.outputs[0].owner.op == op
# print 'BEFORE'
......@@ -492,7 +492,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
op = crossentropy_categorical_1hot
fgraph = gof.FunctionGraph(
[x, b, one_of_n],
[op(softmax(x + b), one_of_n)])
[op(softmax_op(x + b), one_of_n)])
assert fgraph.outputs[0].owner.op == op
# print 'BEFORE'
# for node in fgraph.toposort():
......@@ -515,7 +515,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
x = tensor.matrix('x')
one_of_n = tensor.lvector('one_of_n')
op = crossentropy_categorical_1hot
xe = op(softmax(x), one_of_n)
xe = op(softmax_op(x), one_of_n)
sum_xe = tensor.sum(xe)
g_x = tensor.grad(sum_xe, x)
fgraph = gof.FunctionGraph(
......@@ -544,7 +544,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
has_cx1hot = True
if node.op == crossentropy_softmax_1hot_with_bias_dx:
has_cx1hotdx = True
if node.op == softmax:
if node.op == softmax_op:
has_softmax = True
if node.op == softmax_grad:
has_softmaxdx = True
......@@ -557,7 +557,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
x = tensor.vector('x')
one_of_n = tensor.lvector('one_of_n')
op = crossentropy_categorical_1hot
xe = op(softmax(x), one_of_n)
xe = op(softmax_op(x), one_of_n)
sum_xe = tensor.sum(xe)
g_x = tensor.grad(sum_xe, x)
fgraph = gof.FunctionGraph(
......@@ -586,7 +586,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
has_cx1hot = True
if node.op == crossentropy_softmax_1hot_with_bias_dx:
has_cx1hotdx = True
if node.op == softmax:
if node.op == softmax_op:
has_softmax = True
if node.op == softmax_grad:
has_softmaxdx = True
......@@ -612,10 +612,10 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
# Basic case
expressions = [
T.sum(-T.log(softmax(x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax(x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax(x))[T.arange(y.shape[0]), y]),
T.sum(-T.log(softmax(x))[T.arange(y.shape[0]), y])
T.sum(-T.log(softmax_op(x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax_op(x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax_op(x))[T.arange(y.shape[0]), y]),
T.sum(-T.log(softmax_op(x))[T.arange(y.shape[0]), y])
]
for expr in expressions:
# Verify the optimizer worked on the expressions
......@@ -641,7 +641,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
ops = [node.op for node in g.maker.fgraph.toposort()]
assert len(ops) == 2
assert crossentropy_softmax_1hot_with_bias_dx in ops
assert softmax in ops
assert softmax_op in ops
assert softmax_grad not in ops
g(x_val, y_val)
except Exception:
......@@ -650,10 +650,10 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
# Test that a biased softmax is optimized correctly
bias_expressions = [
T.sum(-T.log(softmax(x + b)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax(b + x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax(x + b))[T.arange(y.shape[0]), y]),
T.sum(-T.log(softmax(b + x))[T.arange(y.shape[0]), y])]
T.sum(-T.log(softmax_op(x + b)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax_op(b + x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax_op(x + b))[T.arange(y.shape[0]), y]),
T.sum(-T.log(softmax_op(b + x))[T.arange(y.shape[0]), y])]
for expr in bias_expressions:
f = theano.function([x, b, y], expr, mode=mode)
......@@ -683,10 +683,10 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
# Test that using "mean" instead of sum works, too
mean_expressions = [
T.mean(-T.log(softmax(x)[T.arange(y.shape[0]), y])),
-T.mean(T.log(softmax(x)[T.arange(y.shape[0]), y])),
-T.mean(T.log(softmax(x))[T.arange(y.shape[0]), y]),
T.mean(-T.log(softmax(x))[T.arange(y.shape[0]), y])]
T.mean(-T.log(softmax_op(x)[T.arange(y.shape[0]), y])),
-T.mean(T.log(softmax_op(x)[T.arange(y.shape[0]), y])),
-T.mean(T.log(softmax_op(x))[T.arange(y.shape[0]), y]),
T.mean(-T.log(softmax_op(x))[T.arange(y.shape[0]), y])]
for expr in mean_expressions:
f = theano.function([x, y], expr, mode=mode)
......@@ -712,7 +712,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
#there's an extra dimshuffle in there
# but I can't think of a good rule to get rid of it
assert crossentropy_softmax_1hot_with_bias_dx in ops
assert softmax in ops
assert softmax_op in ops
assert softmax_grad not in ops
g(x_val, y_val)
except Exception:
......@@ -720,10 +720,10 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
raise
mean_bias_expressions = [
T.mean(-T.log(softmax(x + b)[T.arange(y.shape[0]), y])),
-T.mean(T.log(softmax(b + x)[T.arange(y.shape[0]), y])),
-T.mean(T.log(softmax(x + b))[T.arange(y.shape[0]), y]),
T.mean(-T.log(softmax(b + x))[T.arange(y.shape[0]), y])]
T.mean(-T.log(softmax_op(x + b)[T.arange(y.shape[0]), y])),
-T.mean(T.log(softmax_op(b + x)[T.arange(y.shape[0]), y])),
-T.mean(T.log(softmax_op(x + b))[T.arange(y.shape[0]), y]),
T.mean(-T.log(softmax_op(b + x))[T.arange(y.shape[0]), y])]
for expr in mean_bias_expressions:
f = theano.function([x, b, y], expr, mode=mode)
......@@ -764,10 +764,10 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
y = T.lvector('y')
yi = T.cast(y, 'int32')
expressions = [
T.sum(-T.log(softmax(x)[T.arange(yi.shape[0]), yi])),
-T.sum(T.log(softmax(x)[T.arange(yi.shape[0]), yi])),
-T.sum(T.log(softmax(x))[T.arange(yi.shape[0]), yi]),
T.sum(-T.log(softmax(x))[T.arange(yi.shape[0]), yi])
T.sum(-T.log(softmax_op(x)[T.arange(yi.shape[0]), yi])),
-T.sum(T.log(softmax_op(x)[T.arange(yi.shape[0]), yi])),
-T.sum(T.log(softmax_op(x))[T.arange(yi.shape[0]), yi]),
T.sum(-T.log(softmax_op(x))[T.arange(yi.shape[0]), yi])
]
for expr in expressions:
......@@ -794,7 +794,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
ops = [node.op for node in g.maker.fgraph.toposort()]
assert len(ops) == 3
assert crossentropy_softmax_1hot_with_bias_dx in ops
assert softmax in ops
assert softmax_op in ops
assert softmax_grad not in ops
g(x_val, y_val)
except Exception:
......@@ -815,8 +815,8 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
# Test that a biased softmax is optimized correctly
bias_expressions = [
T.sum(-T.log(softmax(x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax(x)[T.arange(y.shape[0]), y]))]
T.sum(-T.log(softmax_op(x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax_op(x)[T.arange(y.shape[0]), y]))]
for expr in bias_expressions:
f = theano.function([x, y], expr, mode=mode)
......@@ -839,7 +839,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
ops = [node.op for node in g.maker.fgraph.toposort()]
assert len(ops) == 4
assert crossentropy_softmax_1hot_with_bias_dx in ops
assert softmax in ops
assert softmax_op in ops
assert softmax_grad not in ops
g(x_val, y_val)
except Exception:
......@@ -862,10 +862,10 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
# Test that a biased softmax is optimized correctly
bias_expressions = [
T.sum(-T.log(softmax(x + b)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax(b + x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax(x + b))[T.arange(y.shape[0]), y]),
T.sum(-T.log(softmax(b + x))[T.arange(y.shape[0]), y])]
T.sum(-T.log(softmax_op(x + b)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax_op(b + x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax_op(x + b))[T.arange(y.shape[0]), y]),
T.sum(-T.log(softmax_op(b + x))[T.arange(y.shape[0]), y])]
for expr in bias_expressions:
f = theano.function([x, b, y], expr, mode=mode)
......@@ -923,10 +923,10 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
# Test that a biased softmax is optimized correctly
bias_expressions = [
T.sum(-T.log(softmax(x + b)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax(b + x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax(x + b))[T.arange(y.shape[0]), y]),
T.sum(-T.log(softmax(b + x))[T.arange(y.shape[0]), y])]
T.sum(-T.log(softmax_op(x + b)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax_op(b + x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax_op(x + b))[T.arange(y.shape[0]), y]),
T.sum(-T.log(softmax_op(b + x))[T.arange(y.shape[0]), y])]
for expr in bias_expressions:
f = theano.function([x, b, y_], expr, mode=mode)
......@@ -985,10 +985,10 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
# Test that a biased softmax is optimized correctly
bias_expressions = [
T.sum(-T.log(softmax(x + b)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax(b + x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax(x + b))[T.arange(y.shape[0]), y]),
T.sum(-T.log(softmax(b + x))[T.arange(y.shape[0]), y])]
T.sum(-T.log(softmax_op(x + b)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax_op(b + x)[T.arange(y.shape[0]), y])),
-T.sum(T.log(softmax_op(x + b))[T.arange(y.shape[0]), y]),
T.sum(-T.log(softmax_op(b + x))[T.arange(y.shape[0]), y])]
for expr in bias_expressions:
f = theano.function([x, b, y_], expr, mode=mode)
......@@ -1046,7 +1046,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
for node in func.maker.fgraph.toposort():
if node.op == crossentropy_softmax_argmax_1hot_with_bias:
has_cx1hot = True
if node.op == softmax:
if node.op == softmax_op:
has_softmax = True
assert has_cx1hot
......@@ -1060,7 +1060,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
for node in func.maker.fgraph.toposort():
if node.op == crossentropy_softmax_1hot_with_bias_dx:
has_cx1hotdx = True
if node.op == softmax:
if node.op == softmax_op:
has_softmax = True
if node.op == softmax_grad:
has_softmaxdx = True
......@@ -1071,25 +1071,25 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
# Cases to test
expressions = [
a * T.sum(-T.log(softmax(x)[T.arange(y.shape[0]), y])),
-a * T.sum(T.log(softmax(x)[T.arange(y.shape[0]), y])),
a * (-T.sum(T.log(softmax(x)[T.arange(y.shape[0]), y]))),
a * T.sum(T.log(softmax(x)[T.arange(y.shape[0]), y])),
a * T.sum(-T.log(softmax(x))[T.arange(y.shape[0]), y]),
-a * T.sum(T.log(softmax(x))[T.arange(y.shape[0]), y]),
a * (-T.sum(T.log(softmax(x))[T.arange(y.shape[0]), y])),
a * T.sum(T.log(softmax(x))[T.arange(y.shape[0]), y]),
a * T.mean(-T.log(softmax(x)[T.arange(y.shape[0]), y])),
-a * T.mean(T.log(softmax(x)[T.arange(y.shape[0]), y])),
a * (-T.mean(T.log(softmax(x)[T.arange(y.shape[0]), y]))),
a * T.mean(T.log(softmax(x)[T.arange(y.shape[0]), y])),
a * T.mean(-T.log(softmax(x))[T.arange(y.shape[0]), y]),
-a * T.mean(T.log(softmax(x))[T.arange(y.shape[0]), y]),
a * (-T.mean(T.log(softmax(x))[T.arange(y.shape[0]), y])),
a * T.mean(T.log(softmax(x))[T.arange(y.shape[0]), y]),
a * T.sum(-T.log(softmax_op(x)[T.arange(y.shape[0]), y])),
-a * T.sum(T.log(softmax_op(x)[T.arange(y.shape[0]), y])),
a * (-T.sum(T.log(softmax_op(x)[T.arange(y.shape[0]), y]))),
a * T.sum(T.log(softmax_op(x)[T.arange(y.shape[0]), y])),
a * T.sum(-T.log(softmax_op(x))[T.arange(y.shape[0]), y]),
-a * T.sum(T.log(softmax_op(x))[T.arange(y.shape[0]), y]),
a * (-T.sum(T.log(softmax_op(x))[T.arange(y.shape[0]), y])),
a * T.sum(T.log(softmax_op(x))[T.arange(y.shape[0]), y]),
a * T.mean(-T.log(softmax_op(x)[T.arange(y.shape[0]), y])),
-a * T.mean(T.log(softmax_op(x)[T.arange(y.shape[0]), y])),
a * (-T.mean(T.log(softmax_op(x)[T.arange(y.shape[0]), y]))),
a * T.mean(T.log(softmax_op(x)[T.arange(y.shape[0]), y])),
a * T.mean(-T.log(softmax_op(x))[T.arange(y.shape[0]), y]),
-a * T.mean(T.log(softmax_op(x))[T.arange(y.shape[0]), y]),
a * (-T.mean(T.log(softmax_op(x))[T.arange(y.shape[0]), y])),
a * T.mean(T.log(softmax_op(x))[T.arange(y.shape[0]), y]),
]
for expr in expressions:
......@@ -1130,7 +1130,7 @@ def test_argmax_pushdown():
# test that the max_and_argmax is pushed down if the max is not used
out = tensor.max_and_argmax(
softmax(tensor.exp(tensor.tanh(sigmoid(x)))),
softmax_graph(tensor.exp(tensor.tanh(sigmoid(x)))),
axis=-1)[1]
fgraph = gof.FunctionGraph(
[x],
......@@ -1147,7 +1147,7 @@ def test_argmax_pushdown():
x = tensor.matrix()
# test that the max_and_argmax is not pushed down if the max is used
out = tensor.max_and_argmax(
softmax(tensor.exp(tensor.tanh(sigmoid(x)))),
softmax_graph(tensor.exp(tensor.tanh(sigmoid(x)))),
axis=-1)[0]
fgraph = gof.FunctionGraph(
[x],
......@@ -1236,7 +1236,7 @@ def test_asymptotic_32():
x2 = tensor.dvector()
y = tensor.lvector()
c = categorical_crossentropy(softmax(x + x2), y)
c = categorical_crossentropy(softmax_graph(x + x2), y)
f = theano.function([x, y, x2], [c.sum(),
tensor.grad(c.sum(), x)], mode='FAST_RUN')
if 0:
......@@ -1293,7 +1293,7 @@ class Test_softmax_opt:
# printing.debugprint(f)
# print '==='
assert len(f_ops) == 1
assert softmax in f_ops
assert softmax_op in f_ops
f(self.rng.rand(3, 4).astype(config.floatX))
def test_basic_keepdims(self):
......@@ -1307,7 +1307,7 @@ class Test_softmax_opt:
# printing.debugprint(f)
# print '==='
assert len(f_ops) == 1
assert softmax in f_ops
assert softmax_op in f_ops
f(self.rng.rand(3, 4).astype(config.floatX))
def test_grad(self):
......@@ -1329,7 +1329,7 @@ class Test_softmax_opt:
raise SkipTest('Optimization not enabled for the moment')
assert len(g_ops) == 2
assert softmax in g_ops
assert softmax_op in g_ops
assert softmax_grad in g_ops
g(self.rng.rand(3, 4), self.rng.uniform(.5, 1, (3, 4)))
......@@ -1375,10 +1375,9 @@ class Test_softmax_opt:
# etc.
def test_softmax():
from theano.tensor.nnet import softmax
from theano.tensor.nnet import softmax_graph
def test_stabilize_log_softmax():
mode = theano.compile.mode.get_default_mode()
mode = mode.including('local_log_softmax', 'specialize')
......
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