def test_fit_classification(self):
model = models.Sequential()
model.add(layers.Embedding(input_shape=(None,), input_dim=10, output_dim=100, mask_zero=True))
model.add(layers.Bidirectional(ONLSTM(
units=50,
chunk_size=5,
dropout=0.1,
recurrent_dropconnect=0.1,
use_bias=False,
return_sequences=True,
)))
model.add(layers.Bidirectional(ONLSTM(
units=50,
chunk_size=5,
recurrent_dropout=0.1,
return_sequences=True,
)))
model.add(layers.Bidirectional(ONLSTM(units=50, chunk_size=5, unit_forget_bias=False)))
model.add(layers.Dense(units=2, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
model_path = os.path.join(tempfile.gettempdir(), 'test_on_lstm_%f.h5' % np.random.random())
model.save(model_path)
model = models.load_model(model_path, custom_objects={'ONLSTM': ONLSTM})
data_size, seq_len = 10000, 17
x = np.random.randint(0, 10, (data_size, seq_len))
y = [0] * data_size
for i in range(data_size):
if 3 in x[i].tolist() and 7 in x[i].tolist():
y[i] = 1
y = np.array(y)
model.summary()
model.fit(
x,
y,
epochs=10,
callbacks=[callbacks.EarlyStopping(monitor='loss', min_delta=1e-3, patience=2)],
)
model_path = os.path.join(tempfile.gettempdir(), 'test_on_lstm_%f.h5' % np.random.random())
model.save(model_path)
model = models.load_model(model_path, custom_objects={'ONLSTM': ONLSTM})
predicted = model.predict(x).argmax(axis=-1)
self.assertLess(np.sum(np.abs(y - predicted)), data_size // 100)
def test_invalid_chunk_size(self):
with self.assertRaises(ValueError):
model = models.Sequential()
model.add(ONLSTM(units=13, chunk_size=5, input_shape=(None, 100)))