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https://github.com/Steffo99/unimore-bda-6.git
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71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
import tensorflow
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from ..database import Text, Category, DatasetFunc
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from .base import BaseSentimentAnalyzer, AlreadyTrainedError, NotTrainedError
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class TensorflowSentimentAnalyzer(BaseSentimentAnalyzer):
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def __init__(self, *args, **kwargs):
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super().__init__()
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self.trained: bool = False
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self.text_vectorization_layer: tensorflow.keras.layers.TextVectorization = self._build_vectorizer()
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self.model: tensorflow.keras.Sequential = self._build_model()
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def _build_dataset(self, dataset_func: DatasetFunc) -> tensorflow.data.Dataset:
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def dataset_func_with_tensor_tuple():
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for review in dataset_func():
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yield review.to_tensor_tuple()
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return tensorflow.data.Dataset.from_generator(
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dataset_func_with_tensor_tuple,
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output_signature=(
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tensorflow.TensorSpec(shape=(), dtype=tensorflow.string, name="text"),
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tensorflow.TensorSpec(shape=(), dtype=tensorflow.float32, name="category"),
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)
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)
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def _build_model(self) -> tensorflow.keras.Sequential:
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return tensorflow.keras.Sequential([
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tensorflow.keras.layers.Embedding(input_dim=self.MAX_FEATURES + 1, output_dim=self.EMBEDDING_DIM),
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tensorflow.keras.layers.Dropout(0.2),
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tensorflow.keras.layers.GlobalAveragePooling1D(),
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tensorflow.keras.layers.Dropout(0.2),
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tensorflow.keras.layers.Dense(1),
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])
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def _build_vectorizer(self) -> tensorflow.keras.layers.TextVectorization:
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return tensorflow.keras.layers.TextVectorization(max_tokens=self.MAX_FEATURES)
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def __vectorize_data(self, text, category):
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text = tensorflow.expand_dims(text, -1) # TODO: ??????
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return self.text_vectorization_layer(text), category
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MAX_FEATURES = 1000
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EMBEDDING_DIM = 16
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EPOCHS = 10
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def train(self, dataset_func: DatasetFunc) -> None:
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if self.trained:
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raise AlreadyTrainedError()
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training_set = self._build_dataset(dataset_func)
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only_text_set = training_set.map(lambda text, category: text)
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self.text_vectorization_layer.adapt(only_text_set)
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training_set = training_set.map(self.__vectorize_data)
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self.model.compile(loss=tensorflow.keras.losses.CosineSimilarity(axis=0), metrics=["accuracy"])
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history = self.model.fit(training_set, epochs=self.EPOCHS)
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...
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self.trained = True
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def use(self, text: Text) -> Category:
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if not self.trained:
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raise NotTrainedError()
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prediction = self.model.predict(text)
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breakpoint()
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