def test_batchnorm_infer():
layer = BatchNormalization(input_dim = (4, 5, 5))
layer.accumulate = True
layer.initialize()
x = T.tensor4("features")
x_val = [np.ones((6, 4, 5, 5), dtype=theano.config.floatX) for _ in range(2)]
x_val[0][0,0,0,0] = 10.0
x_val[1][0,0,0,0] = -200.0
y = layer.apply(x)
dataset = IterableDataset(dict(features=x_val))
data_stream = DataStream(dataset)
cg = ComputationGraph([y])
infer_population(data_stream, cg, 2)
assert layer.use_population == True
assert_allclose(layer.u.get_value(), np.array([0.72, 2, 2, 2]))
assert_allclose(layer.n.get_value(), np.array([2]))
def test_batchnorm_get_set():
layer = BatchNormalization(input_dim = (4, 5, 5))
layer.accumulate = True
layer.initialize()
x = T.tensor4("features")
x_val = [np.ones((6, 4, 5, 5), dtype=theano.config.floatX) for _ in range(2)]
x_val[0][0,0,0,0] = 10.0
x_val[1][0,0,0,0] = -200.0
y = layer.apply(x)
dataset = IterableDataset(dict(features=x_val))
data_stream = DataStream(dataset)
cg = Model([y])
infer_population(data_stream, cg, 2)
values_dict = get_batchnorm_parameter_values(cg)
assert len(values_dict.keys()) == 3
assert_allclose(values_dict['/batchnormalization.n'], np.array([2]))
values_dict['/batchnormalization.n'] = np.array([5.], dtype="float32")
set_batchnorm_parameter_values(cg, values_dict)
assert_allclose(layer.n.get_value(), np.array([5.]))
def test_batchnorm_infer():
layer = BatchNormalization(input_dim=(4, 5, 5))
layer.accumulate = True
layer.initialize()
x = T.tensor4("features")
x_val = [
np.ones((6, 4, 5, 5), dtype=theano.config.floatX) for _ in range(2)
]
x_val[0][0, 0, 0, 0] = 10.0
x_val[1][0, 0, 0, 0] = -200.0
y = layer.apply(x)
dataset = IterableDataset(dict(features=x_val))
data_stream = DataStream(dataset)
cg = ComputationGraph([y])
infer_population(data_stream, cg, 2)
assert layer.use_population == True
assert_allclose(layer.u.get_value(), np.array([0.72, 2, 2, 2]))
assert_allclose(layer.n.get_value(), np.array([2]))
def test_batchnorm_get_set():
layer = BatchNormalization(input_dim=(4, 5, 5))
layer.accumulate = True
layer.initialize()
x = T.tensor4("features")
x_val = [
np.ones((6, 4, 5, 5), dtype=theano.config.floatX) for _ in range(2)
]
x_val[0][0, 0, 0, 0] = 10.0
x_val[1][0, 0, 0, 0] = -200.0
y = layer.apply(x)
dataset = IterableDataset(dict(features=x_val))
data_stream = DataStream(dataset)
cg = Model([y])
infer_population(data_stream, cg, 2)
values_dict = get_batchnorm_parameter_values(cg)
assert len(values_dict.keys()) == 3
assert_allclose(values_dict['/batchnormalization.n'], np.array([2]))
values_dict['/batchnormalization.n'] = np.array([5.], dtype="float32")
set_batchnorm_parameter_values(cg, values_dict)
assert_allclose(layer.n.get_value(), np.array([5.]))