def _load_model(self):
tf.reset_default_graph()
rnn_layers = [RecurrentLayer(num_units=3,
cell_factory=GRUCellFactory())]
fc_layers = [DenseLayer(num_units=5)]
self.model = RNNWrapper(rnn_layers, fc_layers,
embeddings=self.embeddings, num_epochs=6)
def test_dropout_invalid(self):
with pytest.raises(InvalidArgumentError):
rnn_layers = [RecurrentLayer(num_units=5, dropout_rate=-0.34)]
fc_layers = [DenseLayer(num_units=15)]
model = RecurrentNeuralNetwork(rnn_layers, fc_layers,
embeddings=self.embeddings)
trainer = BaseTrainer(model)
trainer.train(self.x_train, self.y_train)
def test_early_stopping(self):
rnn_layers = [RecurrentLayer(num_units=3,
cell_factory=SimpleCellFactory())]
fc_layers = [DenseLayer(num_units=5)]
model = RecurrentNeuralNetwork(rnn_layers, fc_layers,
embeddings=self.embeddings)
trainer = EarlyStoppingTrainer(model, num_epochs=100, batch_size=1,
max_epochs_no_progress=5,
validation_data=(self.x_val, self.y_val))
trainer.train(self.x_train, self.y_train)
assert trainer._epochs_completed < 100 # early stopping
def test_invalid_data(self):
rnn_layers = [RecurrentLayer(num_units=5)]
fc_layers = [DenseLayer(num_units=15)]
model = RecurrentNeuralNetwork(rnn_layers, fc_layers,
embeddings=self.embeddings)
trainer = BaseTrainer(model)
with pytest.raises(InvalidArgumentError):
trainer.train([1, 2, 3, 4], [1, 2, 3, 4]) # x must be 2d
with pytest.raises(InvalidArgumentError):
trainer.train([[1, 2], [2, 3]], 0) # y must be 1d
with pytest.raises(InvalidArgumentError):
trainer.train('asd', 'fgh') # data must be numbers
def test_base_training(self):
rnn_layers = [RecurrentLayer(num_units=3, cell_factory=GRUCellFactory())]
fc_layers = [DenseLayer(num_units=5)]
model = RecurrentNeuralNetwork(rnn_layers, fc_layers,
embeddings=self.embeddings)
trainer = BaseTrainer(model, num_epochs=100, batch_size=1)
trainer.train(self.x_train, self.y_train)
self._assert_op_in_graph(model, 'Adam', 'dnn/rnn_layers')
self._assert_op_in_graph(model, 'rnn_layer', 'dnn/rnn_layers')
self._assert_op_in_graph(model, 'Adam', 'dnn/fc_layers')
self._assert_op_in_graph(model, 'dense_layer', 'dnn/fc_layers')
assert trainer._epochs_completed == 100
def setUp(self):
self.valid_input = [
"This is my friend, he is angry.", "Hello, why are you angry?"
]
self.invalid_input = [["this", "is", "already", "tokenized"]]
self.embeddings_dir = os.path.join('test', 'data', 'embeddings')
self.preprocessor = TextPreprocessor(remove_stop_words=True)
self.preprocessor.fit([])
doc2vec_model = Doc2Vec.load(
os.path.join(self.embeddings_dir, 'd2v_test.model'))
self.doc_emb = DocumentEmbeddingsTransformer(
Doc2VecEmbedding(model=doc2vec_model))
self.word_emb = WordEmbeddingsTransformer(
CrossLingualPretrainedEmbedding(self.embeddings_dir, 'en'))
self.padder = SentencePadder(padding_length=5)
self.rnn_layers = [
RecurrentLayer(num_units=3, cell_factory=GRUCellFactory())
]
self.fc_layers = [DenseLayer(num_units=5)]
def setUp(self):
self.rnn_layers = [RecurrentLayer(num_units=3,
cell_factory=GRUCellFactory())]
self.fc_layers = [DenseLayer(num_units=5)]
embeddings_dir = os.path.join('test', 'data', 'embeddings')
model = Word2Vec.load(os.path.join(embeddings_dir, 'w2v_test.model'))
self.embeddings = Word2VecEmbedding(model=model)
self.x_train = [[0, 2, 1, 0],
[2, 1, 0, 0],
[3, 1, 0, 1],
[2, 0, 0, 0],
[1, 3, 3, 1]]
self.y_train = [0, 0, 1, 0, 1]
self.x_val = [
[0, 1, 2, 0],
[2, 1, 2, 3]
]
self.y_val = [0, 1]
tf.reset_default_graph()
def test_layer_config_invalid(self):
with pytest.raises(InvalidArgumentError):
config = 5
rnn_layer = RecurrentLayer(num_units=2)
rnn_layer.load_config(config)
def test_layer_config_valid(self):
config = LayerConfig(num_units=5)
rnn_layer = RecurrentLayer(num_units=2)
assert rnn_layer.num_units == 2
rnn_layer.load_config(config)
assert rnn_layer.num_units == 5