def get_validation_data(self):
if self.validation_dataset is not None:
fs = self.validation_dataset.transform(
MergeFeatureLabelFeatureTransformer())
fs = fs.transform(SampleToMiniBatch(self.batch_size))
return fs
return None
def get_training_data(self):
sample_rdd = self.rdd.map(
lambda t: Sample.from_ndarray(nest.flatten(t), np.array([0.0])))
fs = FeatureSet.sample_rdd(sample_rdd,
sequential_order=self.sequential_order,
shuffle=self.shuffle)
fs = fs.transform(SampleToMiniBatch(self.batch_size))
return fs
def get_training_data(self):
fs = FeatureSet.image_set(self.image_set,
sequential_order=self.sequential_order,
shuffle=self.shuffle)
fs = fs.transform(MergeFeatureLabelImagePreprocessing())
fs = fs.transform(ImageFeatureToSample())
fs = fs.transform(SampleToMiniBatch(self.batch_size))
return fs
def get_validation_data(self):
if self.validation_image_set is not None:
fs = FeatureSet.image_set(self.validation_image_set,
sequential_order=self.sequential_order,
shuffle=self.shuffle)
fs = fs.transform(MergeFeatureLabelImagePreprocessing())
fs = fs.transform(ImageFeatureToSample())
fs = fs.transform(SampleToMiniBatch(self.batch_size))
return fs
return None
def get_training_data(self):
sample_rdd = self.text_set.get_samples().map(
lambda sample: Sample.from_jtensor(
features=sample.features + sample.labels,
labels=JTensor.from_ndarray(np.array([0.0]))))
fs = FeatureSet.sample_rdd(sample_rdd,
sequential_order=self.sequential_order,
shuffle=self.shuffle)
fs = fs.transform(SampleToMiniBatch(self.batch_size))
return fs
def get_training_data(self):
fs = self.dataset.transform(MergeFeatureLabelFeatureTransformer())
fs = fs.transform(SampleToMiniBatch(self.batch_size))
return fs