def test_exhaustive_dropout_average():
# This is only a smoke test: verifies that it compiles and runs,
# not any particular value.
inp = theano.tensor.matrix()
mlp = MLP(nvis=2,
layers=[
Linear(2, 'h0', irange=0.8),
Linear(2, 'h1', irange=0.8),
Softmax(3, 'out', irange=0.8)
])
out = exhaustive_dropout_average(mlp, inp)
f = theano.function([inp], out, allow_input_downcast=True)
f([[2.3, 4.9]])
out = exhaustive_dropout_average(mlp, inp, input_scales={'h0': 3})
f = theano.function([inp], out, allow_input_downcast=True)
f([[2.3, 4.9]])
out = exhaustive_dropout_average(mlp, inp, masked_input_layers=['h1'])
f = theano.function([inp], out, allow_input_downcast=True)
f([[2.3, 4.9]])
np.testing.assert_raises(ValueError, exhaustive_dropout_average, mlp, inp,
['h5'])
np.testing.assert_raises(ValueError, exhaustive_dropout_average, mlp, inp,
['h0'], 2., {'h5': 3.})
def test_exhaustive_dropout_average():
# This is only a smoke test: verifies that it compiles and runs,
# not any particular value.
inp = theano.tensor.matrix()
mlp = MLP(nvis=2, layers=[Linear(2, 'h0', irange=0.8),
Linear(2, 'h1', irange=0.8),
Softmax(3, 'out', irange=0.8)])
out = exhaustive_dropout_average(mlp, inp)
f = theano.function([inp], out)
f([[2.3, 4.9]])
def test_exhaustive_dropout_average():
# This is only a smoke test: verifies that it compiles and runs,
# not any particular value.
inp = theano.tensor.matrix()
mlp = MLP(nvis=2,
layers=[
Linear(2, 'h0', irange=0.8),
Linear(2, 'h1', irange=0.8),
Softmax(3, 'out', irange=0.8)
])
out = exhaustive_dropout_average(mlp, inp)
f = theano.function([inp], out)
f([[2.3, 4.9]])
def test_exhaustive_dropout_average():
# This is only a smoke test: verifies that it compiles and runs,
# not any particular value.
inp = theano.tensor.matrix()
mlp = MLP(nvis=2, layers=[Linear(2, 'h0', irange=0.8),
Linear(2, 'h1', irange=0.8),
Softmax(3, 'out', irange=0.8)])
out = exhaustive_dropout_average(mlp, inp)
f = theano.function([inp], out, allow_input_downcast=True)
f([[2.3, 4.9]])
out = exhaustive_dropout_average(mlp, inp, input_scales={'h0': 3})
f = theano.function([inp], out, allow_input_downcast=True)
f([[2.3, 4.9]])
out = exhaustive_dropout_average(mlp, inp, masked_input_layers=['h1'])
f = theano.function([inp], out, allow_input_downcast=True)
f([[2.3, 4.9]])
np.testing.assert_raises(ValueError, exhaustive_dropout_average, mlp,
inp, ['h5'])
np.testing.assert_raises(ValueError, exhaustive_dropout_average, mlp,
inp, ['h0'], 2., {'h5': 3.})
