def test_nn_builder_with_training_features(self):
input_features = [('input', datatypes.Array(3))]
output_features = [('output', None)]
training_features = [('input', datatypes.Array(3)),
('target', datatypes.Double)]
builder = NeuralNetworkBuilder(input_features,
output_features,
disable_rank5_shape_mapping=True,
training_features=training_features)
W1 = _np.random.uniform(-0.5, 0.5, (3, 3))
W2 = _np.random.uniform(-0.5, 0.5, (3, 3))
builder.add_inner_product(name='ip1',
W=W1,
b=None,
input_channels=3,
output_channels=3,
has_bias=False,
input_name='input',
output_name='hidden')
builder.add_inner_product(name='ip2',
W=W2,
b=None,
input_channels=3,
output_channels=3,
has_bias=False,
input_name='hidden',
output_name='output')
builder.make_updatable(['ip1', 'ip2']) # or a dict for weightParams
builder.set_mean_squared_error_loss(name='mse',
input='output',
target='target')
builder.set_adam_optimizer(
AdamParams(lr=1e-2, batch=10, beta1=0.9, beta2=0.999, eps=1e-8))
builder.set_epochs(20, allowed_set=[10, 20, 30])
builder.set_training_input([('input', datatypes.Array(3)),
('target', 'Double')])
model_path = os.path.join(self.model_dir, 'updatable_creation.mlmodel')
print(model_path)
save_spec(builder.spec, model_path)
mlmodel = MLModel(model_path)
self.assertTrue(mlmodel is not None)
spec = mlmodel.get_spec()
self.assertEqual(spec.description.trainingInput[0].name, 'input')
self.assertEqual(
spec.description.trainingInput[0].type.WhichOneof('Type'),
'multiArrayType')
self.assertEqual(spec.description.trainingInput[1].name, 'target')
self.assertEqual(
spec.description.trainingInput[1].type.WhichOneof('Type'),
'doubleType')
def test_nn_builder_with_training_features(self):
input_features = [("input", datatypes.Array(3))]
output_features = [("output", datatypes.Array(3))]
builder = NeuralNetworkBuilder(input_features, output_features)
W1 = _np.random.uniform(-0.5, 0.5, (3, 3))
W2 = _np.random.uniform(-0.5, 0.5, (3, 3))
builder.add_inner_product(
name="ip1",
W=W1,
b=None,
input_channels=3,
output_channels=3,
has_bias=False,
input_name="input",
output_name="hidden",
)
builder.add_inner_product(
name="ip2",
W=W2,
b=None,
input_channels=3,
output_channels=3,
has_bias=False,
input_name="hidden",
output_name="output",
)
builder.make_updatable(["ip1", "ip2"]) # or a dict for weightParams
builder.set_mean_squared_error_loss(name="mse",
input_feature=("output",
datatypes.Array(3)))
builder.set_adam_optimizer(
AdamParams(lr=1e-2, batch=10, beta1=0.9, beta2=0.999, eps=1e-8))
builder.set_epochs(20, allowed_set=[10, 20, 30])
model_path = os.path.join(self.model_dir, "updatable_creation.mlmodel")
print(model_path)
save_spec(builder.spec, model_path)
mlmodel = MLModel(model_path)
self.assertTrue(mlmodel is not None)
spec = mlmodel.get_spec()
self.assertEqual(spec.description.trainingInput[0].name, "input")
self.assertEqual(
spec.description.trainingInput[0].type.WhichOneof("Type"),
"multiArrayType")
self.assertEqual(spec.description.trainingInput[1].name, "output_true")
self.assertEqual(
spec.description.trainingInput[1].type.WhichOneof("Type"),
"multiArrayType")
input_features = [('data', datatypes.Array(*input_dim))]
output_features = [('result', datatypes.Array(*output_dim))]
weights = np.random.rand(1, input_max_size)
bias = np.random.rand(1)
builder = NeuralNetworkBuilder(input_features, output_features)
builder.add_inner_product(name='ip_layer',
W=weights,
b=bias,
input_channels=input_max_size,
output_channels=1,
has_bias=True,
input_name='data',
output_name='result')
builder.make_updatable(['ip_layer'])
builder.set_mean_squared_error_loss(name='lossLayer',
input_feature=output_features[0])
optimizerParams = SgdParams(lr=0.01, batch=1)
optimizerParams.set_batch(1, allowed_set=[1, 2, 4, 8, 16, 32])
builder.set_sgd_optimizer(optimizerParams)
builder.set_epochs(16, allowed_set=[2, 4, 8, 16, 32, 64, 128, 256])
#builder.spec = convert_neural_network_spec_weights_to_fp16(builder.spec)
save_spec(builder.spec, '../core/LinearRegressionModel.mlmodel')
