def main():
X, y, words, tags = load_dataset(DATA_PATH)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15)
inp = Input(shape=(MAX_LEN, ))
model = Embedding(input_dim=len(words) + 2,
output_dim=EMBEDDING_SIZE,
input_length=MAX_LEN,
mask_zero=True)(inp)
model = Bidirectional(
LSTM(units=50, return_sequences=True, recurrent_dropout=0.1))(model)
model = TimeDistributed(Dense(50, activation="relu"))(model)
crf = CRF(len(tags) + 1) # CRF layer
out = crf(model) # output
model = Model(inp, out)
model.compile(optimizer="rmsprop", loss=crf.loss, metrics=[crf.accuracy])
model.summary()
checkpointer = ModelCheckpoint(filepath='model.h5',
verbose=0,
mode='auto',
save_best_only=True,
monitor='val_loss')
history = model.fit(X_train,
np.array(y_train),
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_split=0.1,
callbacks=[checkpointer])
test_size=0.2,
random_state=1)
from tensorflow.keras import Model, Input
from tensorflow.keras.layers import LSTM, Embedding, Dense
from tensorflow.keras.layers import TimeDistributed, SpatialDropout1D, Bidirectional
input_word = Input(shape=(max_len, ))
model = Embedding(input_dim=num_words, output_dim=50,
input_length=max_len)(input_word)
model = SpatialDropout1D(0.1)(model)
model = Bidirectional(
LSTM(units=100, return_sequences=True, recurrent_dropout=0.1))(model)
out = TimeDistributed(Dense(num_tags, activation="softmax"))(model)
model = Model(input_word, out)
model.summary()
model.compile(optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=["accuracy"])
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from livelossplot.tf_keras import PlotLossesCallback
# chkpt = ModelCheckpoint("model_weights.h5", monitor='val_loss',verbose=1, save_best_only=True, save_weights_only=True, mode='min')
#
# early_stopping = EarlyStopping(monitor='val_accuracy', min_delta=0, patience=1, verbose=0, mode='max', baseline=None, restore_best_weights=False)
#
# callbacks = [PlotLossesCallback(), chkpt, early_stopping]
#
# history = model.fit(
def model_akty(seq_len_middle, tag_index, embedding_dim, word_index,
word_index_morf, embedding_matrix_1, embedding_matrix_2):
## main model
input = Input(shape=(seq_len_middle, ))
model = Embedding(
len(word_index) + 1,
embedding_dim,
embeddings_initializer=initializers.Constant(embedding_matrix_1),
input_length=seq_len_middle,
trainable=False)(input)
# model = Bidirectional (LSTM (units=85, return_sequences=True, dropout=drop), merge_mode="mul") (model)
# model = TimeDistributed (Dense (100, activation="relu")) (model)
model = Bidirectional(
LSTM(128, return_sequences=True, dropout=0.2,
recurrent_dropout=0.2))(model)
model = LSTM(128,
return_sequences=True,
dropout=0.2,
recurrent_dropout=0.2)(model)
model = GRU(64,
dropout=0.1,
recurrent_dropout=0.5,
activation='tanh',
recurrent_activation='sigmoid')(model)
model = Flatten()(model)
input3 = Input(shape=(seq_len_middle, ))
model3 = Embedding(
len(word_index_morf) + 1,
embedding_dim,
embeddings_initializer=initializers.Constant(embedding_matrix_2),
input_length=seq_len_middle,
trainable=True)(input3)
model3 = Bidirectional(
LSTM(128, return_sequences=True, dropout=0.2,
recurrent_dropout=0.2))(model3)
model3 = LSTM(128,
return_sequences=True,
dropout=0.2,
recurrent_dropout=0.2)(model3)
model3 = GRU(64,
dropout=0.1,
recurrent_dropout=0.5,
activation='tanh',
recurrent_activation='sigmoid')(model3)
model3 = Flatten()(model3)
merged = tf.keras.layers.concatenate([model, model3])
model = Dense(64, activation='relu')(merged)
# model = Dropout(0.20) (model)
# model = Dense (10, activation='relu') (model)
# out = (Dense (2, activation='softmax')) (model)
out = (Dense(len(tag_index) + 1, activation='softmax'))(model)
# model = Model (input, out)
model = Model(inputs=[input, input3], outputs=out)
# model.compile (loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# model.compile (loss='binary_crossentropy', optimizer='Adadelta', metrics=['accuracy'])
model.compile(loss='mse', optimizer=rmsprop, metrics=['accuracy'])
model.summary()
return model