def test_UnusedParameterWarning(self):
nn = MLPR(layers=[L("Linear", pieces=2)], n_iter=1)
a_in = numpy.zeros((8, 16))
nn._initialize(a_in, a_in)
assert_in('Parameter `pieces` is unused', self.buf.getvalue())
self.buf = io.StringIO() # clear
def make(self, activation, seed=1234, train=False, **keywords):
nn = MLPR(layers=[L(activation, units=16, **keywords), L("Linear", units=1)], random_state=seed, n_iter=1)
if train:
nn.fit(self.a_in, self.a_out)
else:
nn._initialize(self.a_in, self.a_out)
return nn
def test_UnusedParameterWarning(self):
nn = MLPR(layers=[L("Linear", pieces=2)], n_iter=1)
a_in = numpy.zeros((8,16))
nn._initialize(a_in, a_in)
assert_in('Parameter `pieces` is unused', self.buf.getvalue())
self.buf = io.StringIO() # clear
class TestSerializedNetwork(TestLinearNetwork):
def setUp(self):
self.original = MLPR(layers=[L("Linear")])
a_in, a_out = numpy.zeros((8, 16)), numpy.zeros((8, 4))
self.original._initialize(a_in, a_out)
buf = io.BytesIO()
pickle.dump(self.original, buf)
buf.seek(0)
self.nn = pickle.load(buf)
def test_TypeOfWeightsArray(self):
for w, b in self.nn._mlp_to_array():
assert_equal(type(w), numpy.ndarray)
assert_equal(type(b), numpy.ndarray)
# Override base class test, you currently can't re-train a network that
# was serialized and deserialized.
def test_FitAutoInitialize(self):
pass
def test_ResizeInputFrom4D(self):
pass
def test_ResizeInputFrom3D(self):
pass
def test_PredictNoOutputUnitsAssertion(self):
# Override base class test, this is not initialized but it
# should be able to predict without throwing assert.
assert_true(self.nn.is_initialized)
def test_PredictAlreadyInitialized(self):
a_in = numpy.zeros((8, 16))
self.nn.predict(a_in)
class TestSerializedNetwork(TestLinearNetwork):
def setUp(self):
self.original = MLPR(layers=[L("Linear")])
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,4))
self.original._initialize(a_in, a_out)
buf = io.BytesIO()
pickle.dump(self.original, buf)
buf.seek(0)
self.nn = pickle.load(buf)
def test_TypeOfWeightsArray(self):
for w, b in self.nn._mlp_to_array():
assert_equal(type(w), numpy.ndarray)
assert_equal(type(b), numpy.ndarray)
# Override base class test, you currently can't re-train a network that
# was serialized and deserialized.
def test_FitAutoInitialize(self): pass
def test_ResizeInputFrom4D(self): pass
def test_ResizeInputFrom3D(self): pass
def test_PredictNoOutputUnitsAssertion(self):
# Override base class test, this is not initialized but it
# should be able to predict without throwing assert.
assert_true(self.nn.is_initialized)
def test_PredictAlreadyInitialized(self):
a_in = numpy.zeros((8,16))
self.nn.predict(a_in)
def run_EqualityTest(self, copier, asserter):
for activation in ["Rectifier", "Sigmoid", "Maxout", "Tanh"]:
nn1 = MLPR(layers=[L(activation, units=16, pieces=2), L("Linear", units=1)], random_state=1234)
nn1._initialize(self.a_in, self.a_out)
nn2 = copier(nn1, activation)
asserter(numpy.all(nn1.predict(self.a_in) == nn2.predict(self.a_in)))
def ctor(_, activation):
nn = MLPR(layers=[
L(activation, units=16, pieces=2),
L("Linear", units=1)
],
random_state=1234)
nn._initialize(self.a_in, self.a_out)
return nn
def test_SetParametersConstructor(self):
weights = numpy.random.uniform(-1.0, +1.0, (16,4))
biases = numpy.random.uniform(-1.0, +1.0, (4,))
nn = MLPR(layers=[L("Linear")], parameters=[(weights, biases)])
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,4))
nn._initialize(a_in, a_out)
assert_in('Reloading parameters for 1 layer weights and biases.', self.buf.getvalue())
def test_MultiLayerPooling(self):
nn = MLPR(layers=[
C("Rectifier", channels=4, kernel_shape=(3,3), pool_shape=(2,2)),
C("Rectifier", channels=4, kernel_shape=(3,3), pool_shape=(2,2)),
L("Linear")])
a_in, a_out = numpy.zeros((8,32,32,1)), numpy.zeros((8,16))
nn._initialize(a_in, a_out)
assert_equal(nn.unit_counts, [1024, 900, 196, 16])
def test_SetParametersConstructor(self):
weights = numpy.random.uniform(-1.0, +1.0, (16, 4))
biases = numpy.random.uniform(-1.0, +1.0, (4, ))
nn = MLPR(layers=[L("Linear")], parameters=[(weights, biases)])
a_in, a_out = numpy.zeros((8, 16)), numpy.zeros((8, 4))
nn._initialize(a_in, a_out)
assert_in('Reloading parameters for 1 layer weights and biases.',
self.buf.getvalue())
def test_MultiLayerPooling(self):
nn = MLPR(layers=[
C("Rectifier", channels=4, kernel_shape=(3, 3), pool_shape=(2, 2)),
C("ExpLin", channels=4, kernel_shape=(3, 3), pool_shape=(2, 2)),
L("Linear")
])
a_in, a_out = numpy.zeros((8, 32, 32, 1)), numpy.zeros((8, 16))
nn._initialize(a_in, a_out)
assert_equal(nn.unit_counts, [1024, 900, 196, 16])
def make(self, activation, seed=1234, train=False, **keywords):
nn = MLPR(
layers=[L(activation, units=16, **keywords),
L("Linear", units=1)],
random_state=seed,
n_iter=1)
if train:
nn.fit(self.a_in, self.a_out)
else:
nn._initialize(self.a_in, self.a_out)
return nn
def make(self, activation, seed=1234, train=False, **keywords):
nn = MLPR(
layers=[C(activation, channels=16, kernel_shape=(3, 3), **keywords), L("Linear")],
random_state=seed,
n_iter=1,
)
if train:
nn.fit(self.a_in, self.a_out)
else:
nn._initialize(self.a_in, self.a_out)
return nn
def test_SetLayerParamsDict(self):
nn = MLPR(layers=[L("Sigmoid", units=32), L("Linear", name='abcd')])
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,4))
nn._initialize(a_in, a_out)
weights = numpy.random.uniform(-1.0, +1.0, (32,4))
biases = numpy.random.uniform(-1.0, +1.0, (4,))
nn.set_parameters({'abcd': (weights, biases)})
p = nn.get_parameters()
assert_true((p[1].weights.astype('float32') == weights.astype('float32')).all())
assert_true((p[1].biases.astype('float32') == biases.astype('float32')).all())
def test_SetLayerParamsList(self):
nn = MLPR(layers=[L("Linear")])
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,4))
nn._initialize(a_in, a_out)
weights = numpy.random.uniform(-1.0, +1.0, (16,4))
biases = numpy.random.uniform(-1.0, +1.0, (4,))
nn.set_parameters([(weights, biases)])
p = nn.get_parameters()
assert_true((p[0].weights.astype('float32') == weights.astype('float32')).all())
assert_true((p[0].biases.astype('float32') == biases.astype('float32')).all())
def test_GetLayerParams(self):
nn = MLPR(layers=[L("Linear")], n_iter=1)
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,4))
nn._initialize(a_in, a_out)
p = nn.get_parameters()
assert_equals(type(p), list)
assert_true(isinstance(p[0], tuple))
assert_equals(p[0].layer, 'output')
assert_equals(p[0].weights.shape, (16, 4))
assert_equals(p[0].biases.shape, (4,))
def make(self, activation, seed=1234, train=False, **keywords):
nn = MLPR(layers=[
C(activation, channels=16, kernel_shape=(3, 3), **keywords),
L("Linear")
],
random_state=seed,
n_iter=1)
if train:
nn.fit(self.a_in, self.a_out)
else:
nn._initialize(self.a_in, self.a_out)
return nn
def test_GetLayerParams(self):
nn = MLPR(layers=[L("Linear")], n_iter=1)
a_in, a_out = numpy.zeros((8, 16)), numpy.zeros((8, 4))
nn._initialize(a_in, a_out)
p = nn.get_parameters()
assert_equals(type(p), list)
assert_true(isinstance(p[0], tuple))
assert_equals(p[0].layer, 'output')
assert_equals(p[0].weights.shape, (16, 4))
assert_equals(p[0].biases.shape, (4, ))
def test_LayerParamsSkipOneWithNone(self):
nn = MLPR(layers=[L("Sigmoid", units=32), L("Linear", name='abcd')])
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,4))
nn._initialize(a_in, a_out)
weights = numpy.random.uniform(-1.0, +1.0, (32,4))
biases = numpy.random.uniform(-1.0, +1.0, (4,))
nn.set_parameters([None, (weights, biases)])
p = nn.get_parameters()
assert_true((p[1].weights == weights).all())
assert_true((p[1].biases == biases).all())
def run_EqualityTest(self, copier, asserter):
# Only PyLearn2 supports Maxout.
extra = ["Maxout"] if sknn.backend.name == 'pylearn2' else []
for activation in ["Rectifier", "Sigmoid", "Tanh", "ExpLin"] + extra:
nn1 = MLPR(layers=[L(activation, units=16), L("Linear", units=1)], random_state=1234)
nn1._initialize(self.a_in, self.a_out)
nn2 = copier(nn1, activation)
print('activation', activation)
a_out1 = nn1.predict(self.a_in)
a_out2 = nn2.predict(self.a_in)
print(a_out1, a_out2)
asserter(numpy.all(nn1.predict(self.a_in) - nn2.predict(self.a_in) < 1E-6))
def test_GetParamsThenConstructor(self):
nn1 = MLPR(layers=[L("Linear")], n_iter=1)
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,4))
nn1._initialize(a_in, a_out)
p1 = nn1.get_parameters()
print(len(p1))
nn2 = MLPR(layers=[L("Linear")], n_iter=1, parameters=p1)
nn2._initialize(a_in, a_out)
p2 = nn2.get_parameters()
print(len(p2))
assert_true((p1[0].weights.astype('float32') == p2[0].weights.astype('float32')).all())
assert_true((p1[0].biases.astype('float32') == p2[0].biases.astype('float32')).all())
def test_SetLayerParamsDict(self):
nn = MLPR(layers=[L("Sigmoid", units=32), L("Linear", name='abcd')])
a_in, a_out = numpy.zeros((8, 16)), numpy.zeros((8, 4))
nn._initialize(a_in, a_out)
weights = numpy.random.uniform(-1.0, +1.0, (32, 4))
biases = numpy.random.uniform(-1.0, +1.0, (4, ))
nn.set_parameters({'abcd': (weights, biases)})
p = nn.get_parameters()
assert_true((
p[1].weights.astype('float32') == weights.astype('float32')).all())
assert_true(
(p[1].biases.astype('float32') == biases.astype('float32')).all())
def test_SetLayerParamsList(self):
nn = MLPR(layers=[L("Linear")])
a_in, a_out = numpy.zeros((8, 16)), numpy.zeros((8, 4))
nn._initialize(a_in, a_out)
weights = numpy.random.uniform(-1.0, +1.0, (16, 4))
biases = numpy.random.uniform(-1.0, +1.0, (4, ))
nn.set_parameters([(weights, biases)])
p = nn.get_parameters()
assert_true((
p[0].weights.astype('float32') == weights.astype('float32')).all())
assert_true(
(p[0].biases.astype('float32') == biases.astype('float32')).all())
def test_GetParamsThenConstructor(self):
nn1 = MLPR(layers=[L("Linear")], n_iter=1)
a_in, a_out = numpy.zeros((8, 16)), numpy.zeros((8, 4))
nn1._initialize(a_in, a_out)
p1 = nn1.get_parameters()
print(len(p1))
nn2 = MLPR(layers=[L("Linear")], n_iter=1, parameters=p1)
nn2._initialize(a_in, a_out)
p2 = nn2.get_parameters()
print(len(p2))
assert_true(
(p1[0].weights.astype('float32') == p2[0].weights.astype('float32')
).all())
assert_true(
(p1[0].biases.astype('float32') == p2[0].biases.astype('float32')
).all())
def ctor(_, activation):
nn = MLPR(layers=[L(activation, units=16), L("Linear", units=1)], random_state=1234)
nn._initialize(self.a_in, self.a_out)
return nn
