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.})