def test_SetParametersBeforeInit(self):
nn = MLPR(layers=[L("Linear")])
weights = numpy.random.uniform(-1.0, +1.0, (16,4))
biases = numpy.random.uniform(-1.0, +1.0, (4,))
nn.set_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_SetParametersBeforeInit(self):
nn = MLPR(layers=[L("Linear")])
weights = numpy.random.uniform(-1.0, +1.0, (16, 4))
biases = numpy.random.uniform(-1.0, +1.0, (4, ))
nn.set_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_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_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 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())
