def _keras_2_mlmodel_image():
"""
Converts a Keras h5 model into ML Model for image data and saves it on
disk.
NOTE: Image configuration must be specified from Explora.
NOTE: Currently, only categorical cross entropy loss is supported.
"""
model = get_keras_model()
ios_config = state.state["ios_config"]
class_labels = ios_config["class_labels"]
mlmodel = keras_converter.convert(model, input_names=['image'],
output_names=['output'],
class_labels=class_labels,
predicted_feature_name='label')
mlmodel.save(state.state["mlmodel_path"])
image_config = ios_config["image_config"]
spec = coremltools.utils.load_spec(state.state["mlmodel_path"])
builder = coremltools.models.neural_network.NeuralNetworkBuilder(spec=spec)
dims = image_config["dims"]
spec.description.input[0].type.imageType.width = dims[0]
spec.description.input[0].type.imageType.height = dims[1]
cs = _FeatureTypes_pb2.ImageFeatureType.ColorSpace.Value(image_config["color_space"])
spec.description.input[0].type.imageType.colorSpace = cs
trainable_layer_names = [layer.name for layer in model.layers if layer.get_weights()]
builder.make_updatable(trainable_layer_names)
builder.set_categorical_cross_entropy_loss(name='loss', input='output')
if isinstance(model.optimizer, SGD):
params = SgdParams(
lr=K.eval(model.optimizer.lr),
batch=state.state["hyperparams"]["batch_size"],
)
builder.set_sgd_optimizer(params)
elif isinstance(model.optimizer, Adam):
params = AdamParams(
lr=K.eval(model.optimizer.lr),
batch_size=state.state["hyperparams"]["batch_size"],
beta1=model.optimizer.beta1,
beta2=model.optimizer.beta2,
eps=model.optimizer.eps,
)
builder.set_adam_optimizer(params)
else:
raise Exception("iOS optimizer must be SGD or Adam!")
builder.set_epochs(UNLIMITED_EPOCHS)
builder.set_shuffle(state.state["hyperparams"]["shuffle"])
mlmodel_updatable = MLModel(spec)
mlmodel_updatable.save(state.state["mlmodel_path"])
K.clear_session()
def make_updatable(builder, mlmodel_url, mlmodel_updatable_path):
"""This method makes an existing non-updatable mlmodel updatable.
mlmodel_url - the path the Core ML model is stored.
mlmodel_updatable_path - the path the updatable Core ML model will be saved.
"""
import coremltools
model_spec = builder.spec
# make_updatable method is used to make a layer updatable. It requires a list of layer names.
# dense_1 and dense_2 are two innerProduct layer in this example and we make them updatable.
builder.make_updatable(['dense_1', 'dense_2'])
# Categorical Cross Entropy or Mean Squared Error can be chosen for the loss layer.
# Categorical Cross Entropy is used on this example. CCE requires two inputs: 'name' and 'input'.
# name must be a string and will be the name associated with the loss layer
# input must be the output of a softmax layer in the case of CCE.
# The loss's target will be provided automatically as a part of the model's training inputs.
builder.set_categorical_cross_entropy_loss(name='lossLayer',
input='digitProbabilities')
# in addition of the loss layer, an optimizer must also be defined. SGD and Adam optimizers are supported.
# SGD has been used for this example. To use SGD, one must set lr(learningRate) and batch(miniBatchSize) (momentum is an optional parameter).
from coremltools.models.neural_network import SgdParams
builder.set_sgd_optimizer(SgdParams(lr=0.01, batch=32))
# Finally, the number of epochs must be set as follows.
builder.set_epochs(10)
# Set training inputs descriptions
model_spec.description.trainingInput[
0].shortDescription = 'Example image of handwritten digit'
model_spec.description.trainingInput[
1].shortDescription = 'Associated true label (digit) of example image'
# save the updated spec
from coremltools.models import MLModel
mlmodel_updatable = MLModel(model_spec)
mlmodel_updatable.save(mlmodel_updatable_path)
def test_updatable_model_creation_ce_sgd(self):
builder = self.create_base_builder()
builder.add_softmax(name="softmax",
input_name="output",
output_name="softmax_output")
builder.set_categorical_cross_entropy_loss(name="cross_entropy",
input="softmax_output")
builder.set_sgd_optimizer(SgdParams(lr=1e-2, batch=10, momentum=0.0))
builder.set_epochs(20, allowed_set=[10, 20, 30, 40])
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.assertTrue(spec.isUpdatable)
self.assertTrue(spec.neuralNetwork.layers[0].isUpdatable)
self.assertTrue(
spec.neuralNetwork.layers[0].innerProduct.weights.isUpdatable)
self.assertTrue(spec.neuralNetwork.layers[1].isUpdatable)
self.assertTrue(
spec.neuralNetwork.layers[1].innerProduct.weights.isUpdatable)
self.assertTrue(spec.neuralNetwork.updateParams.lossLayers[0].
categoricalCrossEntropyLossLayer is not None)
self.assertTrue(
spec.neuralNetwork.updateParams.optimizer.sgdOptimizer is not None)
self.assertTrue(
_np.isclose(
spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
learningRate.defaultValue,
1e-2,
atol=1e-4,
))
self.assertTrue(
_np.isclose(
spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
miniBatchSize.defaultValue,
10,
atol=1e-4,
))
self.assertTrue(
_np.isclose(
spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
momentum.defaultValue,
0,
atol=1e-8,
))
self.assertTrue(
_np.isclose(spec.neuralNetwork.updateParams.epochs.defaultValue,
20,
atol=1e-4))
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
learningRate.range.minValue == 0)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
learningRate.range.maxValue == 1)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
miniBatchSize.set.values == [10])
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
momentum.range.minValue == 0)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
momentum.range.maxValue == 1)
labels = ["user" + str(i) for i in range(num_classes - 3)] layer.innerProduct.weights.floatValue.extend(weights) layer.innerProduct.bias.floatValue.extend(biases) spec.neuralNetworkClassifier.stringClassLabels.vector.extend(labels) # Make this model trainable. builder = NeuralNetworkBuilder(spec=model._spec) builder.make_updatable(["fullyconnected0"]) builder.set_categorical_cross_entropy_loss(name="lossLayer", input="labelProbability") builder.set_epochs(10, [1, 10, 50]) # Using the SDG optimizer: sgd_params = SgdParams(lr=0.001, batch=8, momentum=0) sgd_params.set_batch(8, [1, 2, 8, 16]) builder.set_sgd_optimizer(sgd_params) # Using the Adam optimizer: # adam_params = AdamParams(lr=0.001, batch=8, beta1=0.9, beta2=0.999, eps=1e-8) # adam_params.set_batch(8, [1, 2, 8, 16]) # builder.set_adam_optimizer(adam_params) builder.spec.description.trainingInput[0].shortDescription = "Example image" builder.spec.description.trainingInput[1].shortDescription = "True label" coremltools.utils.save_spec(builder.spec, "../Models/HandsTuri.mlmodel") # Replace the weights of the last layer with random weights.
def test_updatable_model_creation_mse_sgd(self):
builder = self.create_base_builder()
builder.set_mean_squared_error_loss(name='mse',
input='output',
target='target')
builder.set_sgd_optimizer(SgdParams(lr=1e-2, batch=10, momentum=0.0))
builder.set_epochs(20)
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.assertTrue(spec.isUpdatable)
self.assertTrue(spec.neuralNetwork.layers[0].isUpdatable)
self.assertTrue(
spec.neuralNetwork.layers[0].innerProduct.weights.isUpdatable)
self.assertTrue(spec.neuralNetwork.layers[1].isUpdatable)
self.assertTrue(
spec.neuralNetwork.layers[1].innerProduct.weights.isUpdatable)
self.assertTrue(spec.neuralNetwork.updateParams.lossLayers[0].
categoricalCrossEntropyLossLayer is not None)
self.assertTrue(
spec.neuralNetwork.updateParams.optimizer.sgdOptimizer is not None)
self.assertTrue(
_np.isclose(spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
learningRate.defaultValue,
1e-2,
atol=1e-4))
self.assertTrue(
_np.isclose(spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
miniBatchSize.defaultValue,
10,
atol=1e-4))
self.assertTrue(
_np.isclose(spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
momentum.defaultValue,
0,
atol=1e-8))
self.assertTrue(
_np.isclose(spec.neuralNetwork.updateParams.epochs.defaultValue,
20,
atol=1e-4))
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
learningRate.range.minValue == 0)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
learningRate.range.maxValue == 1)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
miniBatchSize.set.values == [10])
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
momentum.range.minValue == 0)
self.assertTrue(spec.neuralNetwork.updateParams.optimizer.sgdOptimizer.
momentum.range.maxValue == 1)
neuralnetwork_spec.description.metadata.shortDescription = (
'Cat Dog Classifier converted from a Keras model')
model_spec = builder.spec
# make_updatable method is used to make a layer updatable. It requires a list of layer names.
# dense_5 and dense_6 are two innerProduct layer in this example and we make them updatable.
builder.make_updatable(['dense_5', 'dense_6'])
# Categorical Cross Entropy or Mean Squared Error can be chosen for the loss layer.
builder.set_categorical_cross_entropy_loss(name='lossLayer', input='output')
# in addition of the loss layer, an optimizer must also be defined. SGD and Adam optimizers are supported.
# SGD has been used for this example. To use SGD, one must set lr(learningRate) and batch(miniBatchSize) (momentum is an optional parameter).
from coremltools.models.neural_network import SgdParams
builder.set_sgd_optimizer(SgdParams(lr=0.01, batch=5))
# The number of epochs must be set as follows.
builder.set_epochs(1)
model_spec.isUpdatable = True
model_spec.specificationVersion = coremltools._MINIMUM_UPDATABLE_SPEC_VERSION
# Set training inputs descriptions
model_spec.description.trainingInput[
0].shortDescription = 'Image for training and updating the model'
model_spec.description.trainingInput[
1].shortDescription = 'Set the value as Cat or Dog and update the model'
# save the updated spec
coremltools.utils.save_spec(model_spec, "CatDogUpdatable.mlmodel")
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')