def test_2in_3_1out():
m = Machine((2, 3, 1))
nose.tools.eq_(m.shape, (2, 3, 1))
nose.tools.eq_(m.input_divide.shape, (2, ))
nose.tools.eq_(m.input_subtract.shape, (2, ))
nose.tools.eq_(len(m.weights), 2)
nose.tools.eq_(m.weights[0].shape, (2, 3))
assert numpy.allclose(m.weights[0], 0., rtol=1e-10, atol=1e-15)
nose.tools.eq_(m.weights[1].shape, (3, 1))
assert numpy.allclose(m.weights[1], 0., rtol=1e-10, atol=1e-15)
nose.tools.eq_(len(m.biases), 2)
nose.tools.eq_(m.biases[0].shape, (3, ))
assert numpy.allclose(m.biases[0], 0., rtol=1e-10, atol=1e-15)
nose.tools.eq_(m.biases[1].shape, (1, ))
assert numpy.allclose(m.biases[1], 0., rtol=1e-10, atol=1e-15)
nose.tools.eq_(m.hidden_activation, HyperbolicTangent())
nose.tools.eq_(m.output_activation, HyperbolicTangent())
# calculate and match
weights = [numpy.random.rand(2, 3), numpy.random.rand(3, 1)]
biases = [numpy.random.rand(3), numpy.random.rand(1)]
m.weights = weights
m.biases = biases
pymac = PythonMachine(biases, weights, m.hidden_activation,
m.output_activation)
X = numpy.random.rand(10, 2)
assert numpy.allclose(m(X), pymac.forward(X), rtol=1e-10, atol=1e-15)
def test_20in_10_5_3out_with_momentum():
machine = Machine((20, 10, 5, 3))
machine.randomize()
machine.hidden_activation = HyperbolicTangent()
machine.output_activation = HyperbolicTangent()
BATCH_SIZE = 10
cost = SquareError(machine.output_activation)
for k in range(10):
check_training(machine, cost, True, BATCH_SIZE, 0.1, 0.1)
def test_2in_3_1out():
machine = Machine((2, 3, 1))
machine.randomize()
machine.hidden_activation = HyperbolicTangent()
machine.output_activation = HyperbolicTangent()
BATCH_SIZE = 10
cost = SquareError(machine.output_activation)
for k in range(10):
check_training(machine, cost, True, BATCH_SIZE, 0.1, 0.0)
def test_100in_10_10_5out_nobias():
machine = Machine((100, 10, 10, 5))
machine.randomize()
machine.hidden_activation = HyperbolicTangent()
machine.output_activation = HyperbolicTangent()
machine.biases = 0
BATCH_SIZE = 10
cost = SquareError(machine.output_activation)
for k in range(10):
check_training(machine, cost, False, BATCH_SIZE, 0.1, 0.0)
def test_initialization():
# Two inputs and 1 output
m = Machine(2, 1)
assert (m.weights == 0.0).all()
nose.tools.eq_(m.weights.shape, (2, 1))
assert (m.biases == 0.0).all()
nose.tools.eq_(m.biases.shape, (1, ))
# Start by providing the data
w = numpy.array([[0.4, 0.1], [0.4, 0.2], [0.2, 0.7]], 'float64')
m = Machine(w)
b = numpy.array([0.3, -3.0], 'float64')
isub = numpy.array([0., 0.5, 0.5], 'float64')
idiv = numpy.array([0.5, 1.0, 1.0], 'float64')
m.input_subtract = isub
m.input_divide = idiv
m.biases = b
m.activation = HyperbolicTangent()
assert (m.input_subtract == isub).all()
assert (m.input_divide == idiv).all()
assert (m.weights == w).all()
assert (m.biases == b).all()
nose.tools.eq_(m.activation, HyperbolicTangent())
# Save to file
# c = HDF5File("bla.hdf5", 'w')
# m.save(c)
# Start by reading data from a file
c = HDF5File(MACHINE)
m = Machine(c)
assert (m.weights == w).all()
assert (m.biases == b).all()
# Makes sure we cannot stuff incompatible data
w = numpy.array([[0.4, 0.4, 0.2], [0.1, 0.2, 0.7]], 'float64')
m = Machine(w)
b = numpy.array([0.3, -3.0, 2.7, -18, 52], 'float64') #wrong
nose.tools.assert_raises(RuntimeError, setattr, m, 'biases', b)
nose.tools.assert_raises(RuntimeError, setattr, m, 'input_subtract', b)
nose.tools.assert_raises(RuntimeError, setattr, m, 'input_divide', b)
def test_comparisons():
# Start by creating the data
w1 = numpy.array([[0.4, 0.1], [0.4, 0.2], [0.2, 0.7]], 'float64')
w2 = numpy.array([[0.4, 1.1], [0.4, 0.2], [0.2, 0.7]], 'float64')
b1 = numpy.array([0.3, -3.0], 'float64')
b2 = numpy.array([0.3, 3.0], 'float64')
isub1 = numpy.array([0., 0.5, 0.5], 'float64')
isub2 = numpy.array([0.5, 0.5, 0.5], 'float64')
idiv1 = numpy.array([0.5, 1.0, 1.0], 'float64')
idiv2 = numpy.array([1.5, 1.0, 1.0], 'float64')
# Creates Machine's
m1 = Machine(w1)
m1.input_subtract = isub1
m1.input_divide = idiv1
m1.biases = b1
m1.activation = HyperbolicTangent()
m1b = Machine(m1)
m1c = Machine(w1)
m1c.input_subtract = isub1
m1c.input_divide = idiv1
m1c.biases = b1
m1c.activation = HyperbolicTangent()
m2 = Machine(w2)
m2.input_subtract = isub1
m2.input_divide = idiv1
m2.biases = b1
m2.activation = HyperbolicTangent()
m3 = Machine(w1)
m3.input_subtract = isub2
m3.input_divide = idiv1
m3.biases = b1
m3.activation = HyperbolicTangent()
m4 = Machine(w1)
m4.input_subtract = isub1
m4.input_divide = idiv2
m4.biases = b1
m4.activation = HyperbolicTangent()
m5 = Machine(w1)
m5.input_subtract = isub1
m5.input_divide = idiv1
m5.biases = b2
m5.activation = HyperbolicTangent()
m6 = Machine(w1)
m6.input_subtract = isub1
m6.input_divide = idiv1
m6.biases = b1
m6.activation = Identity()
# Compares them using the overloaded operators == and !=
assert m1 == m1b
assert not m1 != m1b
assert m1.is_similar_to(m1b)
assert m1 == m1c
assert not m1 != m1c
assert m1.is_similar_to(m1c)
assert not m1 == m2
assert m1 != m2
assert not m1.is_similar_to(m2)
assert not m1 == m3
assert m1 != m3
assert not m1.is_similar_to(m3)
assert not m1 == m4
assert m1 != m4
assert not m1.is_similar_to(m4)
assert not m1 == m5
assert m1 != m5
assert not m1.is_similar_to(m5)
assert not m1 == m6
assert m1 != m6
assert not m1.is_similar_to(m6)