Theano/doc/hpcs2011_tutorial/logreg_example.py at b2a34ac1521c1d3bdcd20c2d75a93aa724a6bd7d · Sentient07/Theano · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
from __future__ import absolute_import, print_function, division
import numpy
import theano
import theano.tensor as T
rng = numpy.random

N = 400
feats = 784
D = (rng.randn(N, feats).astype(theano.config.floatX), rng.randint(size=N,low=0, high=2).astype(theano.config.floatX))
training_steps = 10000

# Declare Theano symbolic variables
x = T.matrix("x")
y = T.vector("y")
w = theano.shared(rng.randn(feats).astype(theano.config.floatX), name="w")
b = theano.shared(numpy.asarray(0., dtype=theano.config.floatX), name="b")
x.tag.test_value = D[0]
y.tag.test_value = D[1]
#print "Initial model:"
#print w.get_value(), b.get_value()


# Construct Theano expression graph
p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probability of having a one
prediction = p_1 > 0.5 # The prediction that is done: 0 or 1
xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1) # Cross-entropy
cost = xent.mean() + 0.01*(w**2).sum() # The cost to optimize
gw,gb = T.grad(cost, [w,b])

# Compile expressions to functions
train = theano.function(
            inputs=[x,y],
            outputs=[prediction, xent],
            updates={w:w-0.01*gw, b:b-0.01*gb},
            name = "train")
predict = theano.function(inputs=[x], outputs=prediction,
            name = "predict")

if any( x.op.__class__.__name__=='Gemv' for x in train.maker.fgraph.toposort()):
    print('Used the cpu')
elif any( x.op.__class__.__name__=='GpuGemm' for x in train.maker.fgraph.toposort()):
    print('Used the gpu')
else:
    print('ERROR, not able to tell if theano used the cpu or the gpu')
    print(train.maker.fgraph.toposort())


for i in range(training_steps):
    pred, err = train(D[0], D[1])
#print "Final model:"
#print w.get_value(), b.get_value()

print("target values for D")
print(D[1])

print("prediction on D")
print(predict(D[0]))

# Print the graph used in the slides
theano.printing.pydotprint(predict,
                           outfile="pics/logreg_pydotprint_predic.png",
                           var_with_name_simple=True)
theano.printing.pydotprint(prediction,
                          outfile="pics/logreg_pydotprint_prediction.png",
                          var_with_name_simple=True)
theano.printing.pydotprint(train,
                           outfile="pics/logreg_pydotprint_train.png",
                           var_with_name_simple=True)