def test_multi_lstm(self):
model = keras.models.Sequential()
model.add(keras.layers.Embedding(input_dim=5, output_dim=3, mask_zero=True, name='Embed'))
model.add(MultiHead(
layer=keras.layers.Bidirectional(keras.layers.LSTM(units=16, return_sequences=True), name='LSTM'),
layer_num=5,
reg_index=[1, 4],
reg_slice=(slice(None, None), slice(32, 48)),
reg_factor=0.1,
name='Multi-Head-LSTM',
))
model.add(keras.layers.TimeDistributed(MaskFlatten(name='Flatten-1')))
model.add(MultiHead(
layer=Attention(name='Attention'),
layer_num=5,
reg_index=0,
reg_factor=0.1,
name='Multi-Head-Attention',
))
model.add(keras.layers.Flatten(name='Flatten-2'))
model.add(keras.layers.Dense(units=2, activation='softmax', name='Dense'))
model.build()
model.compile(
optimizer='adam',
loss=keras.losses.sparse_categorical_crossentropy,
metrics=[keras.metrics.sparse_categorical_accuracy],
)
model.fit_generator(
generator=self.data_generator(),
steps_per_epoch=100,
epochs=100,
validation_data=self.data_generator(),
validation_steps=10,
callbacks=[
keras.callbacks.EarlyStopping(monitor='val_sparse_categorical_accuracy', patience=5),
],
)
model_path = os.path.join(tempfile.gettempdir(), 'test_save_load_%f.h5' % np.random.random())
model.save(model_path)
model = keras.models.load_model(model_path, custom_objects={
'MaskFlatten': MaskFlatten,
'SeqWeightedAttention': Attention,
'MultiHead': MultiHead,
})
model.summary()
for data, tag in self.data_generator():
predicts = model.predict(data)
predicts = np.argmax(predicts, axis=-1)
self.assertGreaterEqual(np.sum(tag == predicts), 30)
break
def test_multi_attention(self):
model = keras.models.Sequential()
model.add(
keras.layers.Embedding(input_dim=5,
output_dim=3,
mask_zero=True,
name='Embed'))
model.add(
MultiHead(
layer=Attention(name='Attention'),
layer_num=5,
hidden_dim=3,
use_bias=True,
name='Multi-Head-Attention',
))
model.add(keras.layers.TimeDistributed(MaskFlatten(), name='Flatten'))
model.add(
keras.layers.Bidirectional(keras.layers.GRU(units=8),
name='Bi-GRU'))
model.add(
keras.layers.Dense(units=2, activation='softmax', name='Dense'))
model.build()
model.compile(
optimizer='adam',
loss=keras.losses.sparse_categorical_crossentropy,
metrics=[keras.metrics.sparse_categorical_accuracy],
)
model.summary()
model.fit_generator(
generator=self.data_generator(),
steps_per_epoch=100,
epochs=100,
validation_data=self.data_generator(),
validation_steps=10,
callbacks=[
keras.callbacks.EarlyStopping(
monitor='val_sparse_categorical_accuracy', patience=5),
],
)
model.layers[1].set_weights(model.layers[1].get_weights())
model_path = os.path.join(tempfile.gettempdir(),
'test_save_load_%f.h5' % np.random.random())
model.save(model_path)
model = keras.models.load_model(model_path,
custom_objects={
'MaskFlatten': MaskFlatten,
'SeqSelfAttention': Attention,
'MultiHead': MultiHead,
})
model.summary()
for data, tag in self.data_generator():
predicts = model.predict(data)
predicts = np.argmax(predicts, axis=-1)
self.assertGreaterEqual(np.sum(tag == predicts), 30,
(tag, predicts))
break
def test_multi_pooling(self):
data = [
[1, 3, 2, 4],
[2, 8, 3, 5],
]
positions = [
[1, 3],
[2, 4],
]
data_input = keras.layers.Input(shape=(4, ), name='Input-Data')
pos_input = keras.layers.Input(shape=(2, ), name='Input-Pos')
pooling = MultiHead(
[
PiecewisePooling1D(pool_type=PiecewisePooling1D.POOL_TYPE_MAX),
PiecewisePooling1D(
pool_type=PiecewisePooling1D.POOL_TYPE_AVERAGE),
],
name='Multi-Head-Pooling',
)([data_input, pos_input])
model = keras.models.Model(inputs=[data_input, pos_input],
outputs=pooling)
model.summary()
predicts = model.predict([np.asarray(data),
np.asarray(positions)]).tolist()
expected = [
[[1.0, 1.0], [3.0, 2.5]],
[[8.0, 5.0], [5.0, 4.0]],
]
self.assertTrue(np.allclose(expected, predicts))
model_path = os.path.join(tempfile.gettempdir(),
'test_save_load_%f.h5' % random.random())
model.save(model_path)
custom_objects = PiecewisePooling1D.get_custom_objects()
custom_objects['MultiHead'] = MultiHead
model = keras.models.load_model(model_path,
custom_objects=custom_objects)
predicts = model.predict([np.asarray(data),
np.asarray(positions)]).tolist()
expected = [
[[1.0, 1.0], [3.0, 2.5]],
[[8.0, 5.0], [5.0, 4.0]],
]
self.assertTrue(np.allclose(expected, predicts))
#embedding_matrix = joblib.load('embedding_matrix.vec')
#padded_test = joblib.load('padded_test.vec')
#test_labels = joblib.load('test_labels.vec')
#padded_train = joblib.load('padded_train.vec')
#encoded_train_labels = joblib.load('encoded_train_labels.vec')
#le = joblib.load('label_encoder_le_task2.vec')
# define the model
input = Input(shape=(64, ))
m = Embedding(vocab_size,
300,
weights=[embedding_matrix],
input_length=64,
trainable=False)(input)
bi = MultiHead(LSTM(64, activation='tanh', return_sequences=True),
layer_num=5,
name='Multi-LSTMs')(m)
bi = Flatten()(bi)
ff = Dense(3000)(bi)
ff = Dropout(0.1)(ff)
ff = Dense(len(le.category_mapping[0]['mapping']), activation='softmax')(ff)
model = keras.models.Model(inputs=[input], outputs=[ff])
from keras.callbacks import CSVLogger
filepath = "TASK2_multiheadAtt_Fasttext_03052020weights.{epoch:05d}-{val_loss:.5f}.hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
def MultiHead_self_attention(X_train,
y_train,
X_val,
y_val,
X_test,
num_classes,
dropout=0.5,
batch_size=68,
learning_rate=0.0001,
epochs=20,
optimizer='Adam'):
"""Multi-Head attention 模型"""
lstm_unit = 256
model = tf.keras.models.Sequential()
model.add(
Masking(mask_value=0.0,
input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(
MultiHead(Bidirectional(LSTM(units=lstm_unit, dropout=dropout)),
layer_num=10,
name='Multi-LSTMs'))
model.add(
SeqSelfAttention(
attention_type=SeqSelfAttention.ATTENTION_TYPE_MUL,
attention_activation='sigmoid',
kernel_regularizer=keras.regularizers.l2(1e-2),
use_attention_bias=False,
name='Attention',
))
model.add(keras.layers.Flatten())
model.add(Dense(num_classes, activation='softmax'))
print(model.summary())
opt = opt_select(optimizer, learning_rate)
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
callbacks = [
EarlyStopping(monitor='val_loss', patience=5, verbose=0, mode='min'),
ModelCheckpoint('.mdl_wts.hdf5',
save_best_only=True,
monitor='val_loss',
mode='min')
]
history = model.fit(X_train,
y_train,
batch_size=batch_size,
epochs=epochs,
callbacks=callbacks,
validation_data=(X_val, y_val),
verbose=0)
model.load_weights(filepath='.mdl_wts.hdf5')
model.save('/mnt/lxr/SER/paper/fiji_binary.h5')
yhat = model.predict(X_test)
return history, yhat