def createModel(vocab_size, tag_size, max_len, emb_matrix=None):
input = Input(shape=(max_len, ))
if emb_matrix is None:
model = Embedding(input_dim=vocab_size,
output_dim=param.EMBEDDING_DIMENSION,
input_length=max_len)(input)
else:
model = Embedding(input_dim=vocab_size,
output_dim=param.EMBEDDING_DIMENSION,
weights=[emb_matrix],
input_length=max_len,
trainable=False)(input)
model = Dropout(0.1)(model)
model = Bidirectional(
LSTM(units=param.LSTM_UNITS,
return_sequences=True,
recurrent_dropout=0.1))(model)
model = SeqSelfAttention(attention_activation='sigmoid')(model)
model = TimeDistributed(Dense(tag_size, activation="softmax"))(model)
crf = CRF(tag_size, sparse_target=False)
out = crf(model)
model = Model(input, out)
model.compile(optimizer="adam",
loss=crf.loss_function,
metrics=[crf.accuracy])
return model
def train(self):
input = Input(shape=(self.max_len, ))
model = Embedding(input_dim=self.num_word + 1,
output_dim=20,
input_length=self.max_len,
mask_zero=True)(input)
model = Dropout(0.1)(model)
model = Bidirectional(
LSTM(units=50, return_sequences=True,
recurrent_dropout=0.1))(model)
model = TimeDistributed(Dense(50, activation="relu"))(
model) # softmax output layer
crf = CRF(self.num_tag)
out = crf(model)
model = Model(input, out)
model.compile(optimizer="rmsprop",
loss=crf.loss_function,
metrics=[crf.accuracy])
self.pad_process()
train_X, test_X, train_y, test_y = self.train_test_split()
history = model.fit(train_X,
np.array(train_y),
batch_size=32,
epochs=1,
validation_split=0.1,
verbose=1)
self.make_plot(history)
return model
def models(self, input_length, vocab_size, embedding_dim, conv_layer_info, connected_layer_info, batch_size,epochs):
model_input = Input(shape = (input_length,), dtype = 'int64')
model = Embedding(vocab_size, embedding_dim, input_length = input_length)(model_input)
for i in range( len(conv_layer_info) ):
model = Convolution1D(filters= conv_layer_info[i][0], kernel_size=conv_layer_info[i][1],
padding="valid",
activation="relu",
strides=1)(model)
if len(conv_layer_info[i]) > 2:
model = MaxPooling1D(pool_size = conv_layer_info[i][2])(model)
model = Flatten()(model)
for i in range(len(connected_layer_info)):
model = Dense(connected_layer_info[i][0],activation = "relu")(model)
model = Dropout(connected_layer_info[i][1])(model)
model_output = Dense(self.num_of_classes, activation = "softmax")(model)
model =Model(inputs=model_input,outputs=model_output)
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
print("Started Training : ")
model.fit(self.x_train, self.y_train, batch_size = batch_size, epochs= epochs, validation_data=(self.x_test, self.y_test),
verbose=2)
print("Training Completed")
self.model = model
def get_model():
""" Definition du model cnn utilise """
inp = Input(shape=(MAX_TEXT_LENGTH,))
model = Embedding(MAX_TEXT_LENGTH, EMBED_SIZE)(inp)
model = Conv1D(filters=64, kernel_size=7, padding='same', activation='relu')(model)
model = MaxPooling1D(pool_size=3)(model)
model = BatchNormalization(axis=1)(model)
model = Dropout(0.25)(model)
model = Conv1D(filters=128, kernel_size=5, padding='same', activation='relu')(model)
model = MaxPooling1D(pool_size=5)(model)
model = BatchNormalization(axis=1)(model)
model = Dropout(0.3)(model)
model = Flatten()(model)
model = Dense(1024, activation="relu")(model)
model = Dense(10, activation="softmax")(model)
model = Model(inputs=inp, outputs=model)
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
model.summary()
return model
def train(X_tr, y_tr):
output_dim = 50
word_input = Input(shape=(MAXLEN, ))
model = Embedding(input_dim=VOCAB_SIZE,
output_dim=output_dim,
input_length=MAXLEN)(word_input)
model = SpatialDropout1D(0.1)(model)
model = Bidirectional(
LSTM(units=100, return_sequences=True, recurrent_dropout=0.1))(model)
out = TimeDistributed(Dense(len(tags), activation="softmax"))(model)
model = Model(word_input, out)
model.compile(optimizer="rmsprop",
loss="sparse_categorical_crossentropy",
metrics=["accuracy"])
checkpointer = ModelCheckpoint(filepath='NER_model.h5',
verbose=0,
mode='auto',
save_best_only=True,
monitor='val_loss')
history = model.fit(X_tr,
y_tr.reshape(*y_tr.shape, 1),
batch_size=BATCH_SIZE,
epochs=EPOCHS,
shuffle=True,
validation_split=VALIDATION_SPLIT,
verbose=1,
callbacks=[checkpointer])
return history
def train():
input = Input(shape=(max_len, ))
model = Embedding(input_dim=n_words, output_dim=50,
input_length=max_len)(input)
model = Dropout(0.1)(model)
model = Bidirectional(
LSTM(units=100, return_sequences=True, recurrent_dropout=0.1))(model)
out = TimeDistributed(Dense(n_tags, activation='softmax'))(
model) # softmax output layer
model = Model(input, out)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
# checkpoint
# filepath = "../result/bilstm-weights-{epoch:02d}-{val_acc:.2f}.hdf5"
# checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
# history=model.fit(X_train,np.array(y_train),batch_size=32,epochs=5,validation_split=0.1,verbose=1,callbacks=[checkpoint])
history = model.fit(X_train,
np.array(y_train),
batch_size=32,
epochs=5,
validation_split=0.1,
verbose=1)
# 保存模型
model.save(filepath="../result/bi-lstm.h5")
hist = pd.DataFrame(history.history)
plt.figure(figsize=(12, 12))
plt.plot(hist["acc"])
plt.plot(hist["val_acc"])
plt.show()
def create_model(train=True):
if train:
(train_x, train_y, train_max_len, train_length), (
test_x, test_y, test_max_len, test_length), \
(vocab, maxlen, chunk_tags, embedding_weights) = process_data.load_lstm_data()
else:
with open('model/chars-config.pkl', 'rb') as inp:
(vocab, chunk_tags, embedding_weights) = pickle.load(inp)
input = Input(shape=(train_max_len, ))
model = Embedding(len(vocab) + 1, EMBED_DIM, mask_zero=True)(input)
model = Dropout(0.1)(model)
model = Bidirectional(
LSTM(units=200, return_sequences=True, recurrent_dropout=0.1))(model)
model = Dropout(0.7)(model)
out = TimeDistributed(Dense(len(chunk_tags) + 1,
activation="softmax"))(model)
model = Model(input, out)
model.summary()
model.compile('adam',
loss="categorical_crossentropy",
metrics=["accuracy"])
if train:
return model, (train_x, train_y,
train_max_len), (test_x, test_y,
test_max_len), (vocab, chunk_tags)
else:
return model, (vocab, chunk_tags)
def createModel(vocab_size, tag_size, max_len, emb_matrix=None):
input = Input(shape=(max_len, ))
if emb_matrix is None:
model = Embedding(input_dim=vocab_size,
output_dim=param.EMBEDDING_DIMENSION,
input_length=max_len)(input)
else:
model = Embedding(input_dim=vocab_size,
output_dim=param.EMBEDDING_DIMENSION,
weights=[emb_matrix],
input_length=max_len,
trainable=False)(input)
model = Dropout(0.1)(model)
model = Bidirectional(
LSTM(units=param.LSTM_UNITS,
return_sequences=True,
recurrent_dropout=0.1))(model)
out = TimeDistributed(Dense(tag_size, activation="softmax"))(
model) # softmax output layer
model = Model(input, out)
model.compile(optimizer="adam",
loss="categorical_crossentropy",
metrics=["accuracy"])
return model
def run(X_train,
Y_train,
X_val,
Y_val,
embedding_matrix,
vocab_size,
maxlen=40,
emb_dim=300,
neg_ratio=0,
hidden_dim=300,
drop=0.2,
r_drop=0.1):
##build model
input = Input(shape=(maxlen, ))
model = Embedding(vocab_size,
emb_dim,
weights=[embedding_matrix],
input_length=maxlen,
trainable=False)(input)
model = Dropout(drop)(model)
model = Bidirectional(
LSTM(hidden_dim, return_sequences=True,
recurrent_dropout=r_drop))(model)
model = Dropout(drop)(model)
out = TimeDistributed(Dense(1, activation='sigmoid'))(model)
model = Model(input, out)
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['acc'])
earlyStop = [EarlyStopping(monitor='val_loss', patience=1)]
history = model.fit(X_train,
Y_train,
batch_size=64,
epochs=10,
validation_data=(X_val, Y_val),
callbacks=earlyStop)
pred = model.predict(X_val)
Y_pred = np.squeeze(pred)
test = [[1 if y >= threshold else 0 for y in x] for x in Y_pred]
test_arr = np.asarray(test)
test_arr = np.reshape(test_arr, (-1))
target = np.reshape(Y_val, (-1))
print(
metrics.precision_recall_fscore_support(target,
test_arr,
average=None,
labels=[0, 1]))
# Y_pred_ = [[1 if y>=threshold else 0 for y in x] for x in Y_pred]
Y_val_ = np.squeeze(Y_val)
print("Evaluate: dev seg exact")
pred_out_dir = out_dir + 'seg_' + str(neg_ratio) + 'neg'
gold_dir = '../../data/val_segs/' + 'seg_' + str(neg_ratio) + 'neg'
p, r, f = seg_exact_match(test, Y_val_, pred_out_dir, gold_dir)
return model, history, p, r, f
def build(self, hp):
# Model definition
inpt = Input(shape=(MAX_LEN, )) # MAX_LEN, VECT_SIZE
# input_dim: Size of the vocabulary, i.e. maximum integer index + 1
# output_dim: Dimension of the dense embedding
# input_shape: 2D tensor with shape (batch_size, input_length)
# doc_vocab: vocabulary - number of words - of the train dataset
model = Embedding(
doc_vocab,
output_dim=100,
input_length=MAX_LEN, # n_words + 2 (PAD & UNK)
weights=[embedding_matrix], # use GloVe vectors as initial weights
mask_zero=True,
trainable=True,
activity_regularizer=l1(0.0000001))(inpt) # name='word_embedding'
# hp.Choice('activity_regularizer_1', values=[0.0, 0.00001, 0.000001, 0.0000001])
# , activity_regularizer=l1(0.0000001) hp.Choice('activity_regularizer_2', values=[0.0, 0.0000001, 0.00000001, 0.000000001])
# recurrent_dropout=0.1 (recurrent_dropout: 10% possibility to drop of the connections that simulate LSTM memory cells)
# units = 100 / 0.55 = 182 neurons (to account for 0.55 dropout)
model = Bidirectional(
LSTM(units=100,
return_sequences=True,
activity_regularizer=l1(0.000000001),
recurrent_constraint=max_norm(2)))(
model) # input_shape=(1, MAX_LEN, VECT_SIZE)
model = Dropout(hp.Choice('dropout', values=[0.0, 0.3, 0.5]))(model)
# model = TimeDistributed(Dense(number_labels, activation="relu"))(model) # a dense layer as suggested by neuralNer
model = Dense(number_labels, activation=None)(
model) # activation='linear' (they are the same)
crf = CRF(
) # CRF layer { SHOULD I SET -> number_labels+1 (+1 -> PAD) }
out = crf(model) # output
model = Model(inputs=inpt, outputs=out)
# set learning rate
# lr_rate = InverseTimeDecay(initial_learning_rate=0.05, decay_rate=4, decay_steps=steps_per_epoch)
# lr_rate = ExponentialDecay(initial_learning_rate=0.01, decay_rate=0.5, decay_steps=10000)
# set optimizer
# decay=learning_rate / epochs
# CASE 1: decay=0.01
# CASE 2: decay=0.1/5
opt = SGD(
learning_rate=0.0, momentum=0.9, clipvalue=5.0
) # clipvalue (Gradient Clipping): clip the gradient to [-5 to 5]
# opt = SGD(learning_rate=0.01, decay=0.01/steps_per_epoch, momentum=0.9, clipvalue=10.0) # clipvalue (Gradient Clipping): clip the gradient to [-5 to 5]
# opt = SGD(learning_rate=lr_rate, clipvalue=3.0, clipnorm=2.0, momentum=0.9) # clipvalue (Gradient Clipping): clip the gradient to [-5 to 5]
# compile Bi-LSTM-CRF
model.compile(optimizer=opt, loss=crf.loss,
metrics=[crf.accuracy]) # , f1score()
# model.compile(optimizer=opt, loss=crf.loss, metrics=[crf.viterbi_accuracy])
self.initial_lrate = hp.Choice('learning_rate', [0.05, 0.01])
return model
def train():
input = Input(shape=(max_len, ))
model = Embedding(input_dim=n_words + 1,
output_dim=100,
input_length=max_len,
mask_zero=True)(input) # 20-dim embedding
model = Bidirectional(
LSTM(units=50, return_sequences=True,
recurrent_dropout=0.1))(model) # variational biLSTM
model = TimeDistributed(Dense(50, activation="relu"))(
model) # a dense layer as suggested by neuralNer
crf = CRF(n_tags) # CRF layer
out = crf(model) # output
model = Model(input, out)
model.compile(optimizer="rmsprop",
loss=crf.loss_function,
metrics=[crf.accuracy])
model.summary()
history = model.fit(x_train,
np.array(y_train),
batch_size=64,
epochs=5,
validation_split=0.1,
verbose=1)
save_load_utils.save_all_weights(model, filepath="models/bilstm-crf.h5")
hist = pd.DataFrame(history.history)
print(hist)
plt.figure(figsize=(12, 12))
plt.plot(hist["crf_viterbi_accuracy"])
plt.plot(hist["val_crf_viterbi_accuracy"])
plt.show()
def create_model(vocabulary_size,
num_classes,
max_length,
units=100,
dense_neurons=16,
embedding_vector_length=300):
input = Input(shape=(max_length, ))
model = Embedding(input_dim=vocabulary_size + 1,
output_dim=embedding_vector_length,
input_length=max_length)(input)
model = Dropout(0.1)(model)
model = Bidirectional(
LSTM(units=100, return_sequences=True, recurrent_dropout=0.1))(model)
#model = TimeDistributed(Dense(dense_neurons, activation='relu'))(model)
#model = Dense(num_classes, activation="softmax")(model)
out = TimeDistributed(Dense(3, activation="softmax"))(model)
#crf = CRF(3, name="output")
#out = crf(model)
# softmax output layer
model = Model(input, out)
#model.compile(optimizer="adam", loss=crf.loss_function, metrics=[crf.accuracy],sample_weight_mode="temporal")
model.compile(optimizer="adam",
loss="categorical_crossentropy",
metrics=['accuracy'],
sample_weight_mode="temporal")
return model
def define_model(self):
"""Define the Bi-LSTM/CRF model"""
input_layer = Input(shape=(self.MAX_SEQ_LEN, ))
model = Embedding(
input_dim=self.n_words,
output_dim=self.EMBEDDING, # actual n_words + 2 (PAD & UNK)
input_length=self.MAX_SEQ_LEN)(
input_layer) # default: 300-dim embedding
model = Bidirectional(
LSTM(units=self.LSTM_HIDDEN_UNITS,
return_sequences=True,
recurrent_dropout=0.1))(model) # variational biLSTM
model = TimeDistributed(Dense(self.LSTM_DENSE_DIM, activation="relu"))(
model) # a dense layer as suggested by neuralNer
crf = CRF(self.n_tags) # CRF layer, actual n_tags+1(PAD)
output_layer = crf(model) # output
model = Model(input_layer, output_layer)
model.compile(optimizer="rmsprop",
loss=crf.loss_function,
metrics=[crf.accuracy])
model.summary()
return model
def base_lstm_user(vocabulary_size: int, embedding_size: int,
history_size: int, max_seq_length: int,
embedding_matrix: np.array, y_dictionary: dict) -> Model:
lstm_output_shape = 256
input = Input(shape=(max_seq_length, ), name='main_input')
history = Input(shape=(history_size, ), name='history_input')
model = Embedding(input_dim=vocabulary_size,
output_dim=embedding_size,
weights=[embedding_matrix],
input_length=max_seq_length,
trainable=True,
embeddings_regularizer=regularizers.l2(0.000001))(input)
model = Dropout(0.4)(model)
model = Bidirectional(LSTM(lstm_output_shape,
return_sequences=False))(model)
h_model = history
for i in range(2):
h_model = Dense(256,
activation='tanh',
kernel_regularizer=regularizers.l2(0.00001))(h_model)
model = Concatenate()([model, h_model])
model = Dense(len(y_dictionary), activation='softmax')(model)
model = Model([input, history], model)
optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, decay=0.001)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
return model
def create_model(number_of_words_total, number_of_tags, len_max,
embedding_size, lstm_units, dropout, recurrent_dropout):
# Input
input_layer = Input(shape=(len_max, ))
# Embedding Layer
model = Embedding(input_dim=number_of_words_total,
output_dim=embedding_size,
input_length=len_max)(input_layer)
# BI-LSTM Layer
model = Bidirectional(
LSTM(units=lstm_units,
return_sequences=True,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
kernel_initializer=keras.initializers.he_normal()))(model)
# TimeDistributed layer
model = TimeDistributed(Dense(number_of_tags, activation="relu"))(model)
# CRF Layer
crf = CRF(number_of_tags)
# Output
output_layer = crf(model)
model = Model(input_layer, output_layer)
# Optimiser
adam = Adam(lr=0.0005, beta_1=0.9, beta_2=0.999)
model.compile(optimizer=adam,
loss=crf.loss_function,
metrics=[crf.accuracy, 'accuracy'])
model.summary()
return model
def cross_validate(self, X, y):
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.1)
input = Input(shape=(self.max_len, ))
model = Embedding(input_dim=self.n_words,
output_dim=50,
input_length=self.max_len)(input)
model = Dropout(0.1)(model)
model = Bidirectional(
LSTM(units=100, return_sequences=True,
recurrent_dropout=0.1))(model)
out = TimeDistributed(Dense(self.n_labels, activation="softmax"))(
model) # softmax output layer
model = Model(input, out)
model.compile(optimizer="rmsprop",
loss="categorical_crossentropy",
metrics=["accuracy"])
history = model.fit(X_tr,
np.array(y_tr),
batch_size=32,
epochs=1,
validation_split=0.1,
verbose=1)
p = model.predict(np.array([X_te[10]]))
p = np.argmax(p, axis=-1)
for w, pred in zip(X_te[10], p[0]):
if self.words[w] != 'PADGARBAGE':
print("{:15}: {}".format(self.words[w], self.labels[pred]))
def fit(self, X, y):
input = Input(shape=(self.max_len, ))
model = Embedding(input_dim=self.n_words + 1,
output_dim=20,
input_length=self.max_len,
mask_zero=True)(input)
model = Dropout(0.1)(model)
model = Bidirectional(
LSTM(units=50, return_sequences=True,
recurrent_dropout=0.1))(model)
model = TimeDistributed(Dense(50, activation="relu"))(
model) # softmax output layer
crf = CRF(self.n_labels) # CRF layer
out = crf(model) # output
model = Model(input, out)
model.compile(optimizer="adam",
loss=crf.loss_function,
metrics=[crf.accuracy])
self.model = model
history = self.model.fit(X,
np.array(y),
batch_size=32,
epochs=20,
validation_split=0.1,
verbose=1)
def get_model(units, dout, emb, mtl, n_out):
"""
Build Model
:param units: cells number of conv1D
:param dout: rate for dropout
:param emb: output size of embedding layer
:param mlt: input size
:param n_out: number of classes to dense layer
:return: model
"""
inp = Input(shape=(mtl, ))
model = Embedding(mtl, emb)(inp)
model = Dropout(dout)(model)
model = Conv1D(filters=units,
kernel_size=emb,
padding='same',
activation='relu')(model)
model = MaxPooling1D(pool_size=2)(model)
model = Flatten()(model)
model = Dense(n_out, activation="softmax")(model)
model = Model(inputs=inp, outputs=model)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
return model
def lstm_crf(x, y, vocab_size, n_tags, batch_size, epochs):
output_dim = 30
hid_size = 50
dense1 = 50
seq_len = x.shape[1]
input_ = Input(shape=(seq_len, ))
model = Embedding(input_dim=vocab_size,
output_dim=output_dim,
input_length=seq_len,
mask_zero=True)(input_)
model = Bidirectional(
LSTM(units=hid_size, return_sequences=True,
recurrent_dropout=0.1))(model)
model = TimeDistributed(Dense(dense1, activation='relu'))(model)
crf = CRF(n_tags, learn_mode='marginal')
out = crf(model) # prob
model = Model(inputs=input_, outputs=out)
model.compile(optimizer='rmsprop', loss=crf_loss, metrics=[crf.accuracy])
model.summary()
history = model.fit(x,
np.array(y),
batch_size=batch_size,
epochs=epochs,
validation_split=0.1,
verbose=1)
return model, history
def trainLSTM():
# fit a LSTM network with an embedding layer
from keras.models import Model, Input
from keras.layers import LSTM, Embedding, Dense, TimeDistributed, Dropout, Bidirectional
input = Input(shape=(max_len, ))
model = Embedding(input_dim=n_words, output_dim=50,
input_length=max_len)(input)
model = Dropout(0.1)(model)
model = Bidirectional(
LSTM(units=100, return_sequences=True, recurrent_dropout=0.1))(model)
out = TimeDistributed(Dense(n_tags, activation="softmax"))(model)
model = Model(input, out)
model.compile(optimizer="rmsprop",
loss="categorical_crossentropy",
metrics=["accuracy"])
history = model.fit(X_tr,
np.array(y_tr),
batch_size=32,
epochs=5,
validation_split=0.1,
verbose=1)
hist = pd.DataFrame(history.history)
plt.figure(figsize=(12, 12))
plt.plot(hist["acc"])
plt.plot(hist["val_acc"])
plt.show()
return model
def createArchitecture(parameters):
optimizer = 0
if parameters["optimizer"] == 'rmsprop':
optimizer = optimizers.rmsprop(lr=parameters["learning_rate"],
epsilon=parameters["epsilon"])
elif parameters["optimizer"] == 'adam':
optimizer = optimizers.adam(lr=parameters["learning_rate"],
epsilon=parameters["epsilon"])
elif parameters["optimizer"] == 'nadam':
optimizer = optimizers.nadam(lr=parameters["learning_rate"],
epsilon=parameters["epsilon"])
elif parameters["optimizer"] == 'sgd':
optimizer = optimizers.sgd(lr=parameters["learning_rate"])
#else:
# optimizer = parameters["optimizer"]
if parameters["use_embedding_layer"]:
input = Input(shape=(parameters["max_seq_len"], ))
model = Embedding(input_dim=parameters["one_hot_vector_len"],
output_dim=parameters["embedding_layer_output"],
input_length=parameters["max_seq_len"])(input)
if parameters["embedding_dropout"] > 0:
model = Dropout(rate=parameters["embedding_dropout"])(model)
else:
input = Input(shape=(parameters["max_seq_len"],
parameters["one_hot_vector_len"]))
model = input
if parameters["bi_lstm1_units"] > 0:
model = Bidirectional(
CuDNNLSTM(units=parameters["bi_lstm1_units"],
return_sequences=True))(model)
if parameters["bi_lstm2_units"] > 0:
model = Bidirectional(
CuDNNLSTM(units=parameters["bi_lstm2_units"],
return_sequences=True))(model)
if parameters["bi_lstm3_units"] > 0:
model = Bidirectional(
CuDNNLSTM(units=parameters["bi_lstm3_units"],
return_sequences=True))(model)
if parameters["use_crf_layer"]:
crf = CRF(parameters["num_tags"], learn_mode="marginal")
out = crf(model) # output
model = Model(input, out)
model.compile(optimizer=optimizer,
loss=losses.crf_loss,
metrics=[metrics.crf_accuracy,
avg_proximity_metric()])
else:
out = TimeDistributed(
Dense(parameters["num_tags"], activation="softmax"))(model)
model = Model(input, out)
model.compile(optimizer=optimizer,
loss="categorical_crossentropy",
metrics=["accuracy", avg_proximity_metric()])
model.summary()
return model
def BUILD_MODEL(X,MAX,n_words,n_tags,embedding_matrix):
input_word = Input(shape = (MAX,))
model=Embedding(input_dim=n_words,input_length=X.shape[1], output_dim=embedding_matrix.shape[1], weights=[embedding_matrix],trainable=False)(input_word)
model=Bidirectional(LSTM(64, return_sequences=True, dropout=0.2, recurrent_dropout=0.2))(model)
model=TimeDistributed(Dense(32, activation ='relu'))(model)
crf = CRF(n_tags) # CRF layer
out = crf(model) # output
model = Model(input_word, out)
model.summary()
model.compile(optimizer='adam', loss=crf.loss_function, metrics=[crf.accuracy, 'accuracy'])
return model
def buildmodel(cls, max_len: int, n_words: int, n_tags: int):
# define the LSTM network to fit an embedding layer
input = Input(shape=(max_len,))
model = Embedding(input_dim=n_words, output_dim=50, input_length=max_len)(input)
model = Dropout(0.1)(model)
model = Bidirectional(LSTM(units=100, return_sequences=True, recurrent_dropout=0.1))(model)
out = TimeDistributed(Dense(n_tags, activation="softmax"))(model) # softmax out layer
# use defitions to compile and train the model
model = Model(input, out)
model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"])
return model
def createModel(MAX_LEN,
n_words,
n_tags,
embedding_matrix,
lstm_cells=LSTM_CELLS,
trainable=False,
lstm_layers=1,
bi_direc=False,
activation="softmax",
optimizer="rmsprop",
loss="categorical_crossentropy"):
inputt = Input(shape=(MAX_LEN, ))
if not trainable:
model = Embedding(input_dim=n_words,
output_dim=embedding_matrix.shape[1],
weights=[embedding_matrix],
trainable=False,
input_length=MAX_LEN)(inputt)
else:
model = Embedding(input_dim=n_words,
output_dim=embedding_matrix.shape[1],
weights=[embedding_matrix],
trainable=True,
input_length=MAX_LEN)(model)
model = Dropout(0.1)(model)
# If want to add multiple LSTM layers
if lstm_layers > 1:
for i in range(lstm_layers - 1):
model = LSTM(units=lstm_cells,
return_sequences=True,
recurrent_dropout=0.1)(model)
if bi_direc:
model = Bidirectional(
LSTM(units=lstm_cells,
return_sequences=True,
recurrent_dropout=0.1))(model) # variational biLSTM
else:
model = LSTM(units=lstm_cells,
return_sequences=True,
recurrent_dropout=0.1)(model)
out = TimeDistributed(Dense(n_tags, activation=activation))(
model) # softmax output layer
model = Model(inputt, out)
model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])
return model
def buildmodel(cls, max_len: int, n_words: int, n_tags: int):
input = Input(shape=(max_len,))
model = Embedding(input_dim=n_words + 1, output_dim=20, # TODO: should this be 20 here and 50 in LSTM?
input_length=max_len, mask_zero=True)(input) # 20-dim embedding
model = Bidirectional(LSTM(units=50, return_sequences=True,
recurrent_dropout=0.1))(model)
model = TimeDistributed(Dense(50, activation="relu"))(model)
crf = kcCRF(n_tags)
out = crf(model)
model = Model(input, out)
model.compile(optimizer="rmsprop", loss=crf.loss_function, metrics=[crf.accuracy])
return model
def model_stuff():
global model
word_embedding_size = 100
input = Input(shape=(maxlen,))
model = Embedding(input_dim=n_words, output_dim=word_embedding_size, weights=[glove_embedding_matrix()], input_length=maxlen, trainable=False)(input)
model = Bidirectional(LSTM(units=word_embedding_size,return_sequences=True,dropout=0.5,recurrent_dropout=0.5,kernel_initializer=k.initializers.he_normal()))(model)
model = Bidirectional(LSTM(units=word_embedding_size*2,return_sequences=True,dropout=0.5,recurrent_dropout=0.5,kernel_initializer=k.initializers.he_normal()))(model)
model = TimeDistributed(Dense(n_tags, activation="relu"))(model)
crf = CRF(n_tags)
out = crf(model)
model = Model(input, out)
# adam = k.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999)
model.compile(optimizer='adam', loss=crf.loss_function, metrics=[crf.accuracy, 'accuracy'])
model.summary()
def cnn_non_static(self):
x_train, y_train, x_test, y_test, vocabolary_dict = data_load_and_preproccess(
self.word_size, self.sequence_length)
embedding_weights = word_to_vector(np.vstack(
(x_train, x_test)), vocabolary_dict, self.embedding_dim,
self.min_word_count,
self.context_window_size)
embedding_weights = np.array(
[value for value in embedding_weights.values()])
model_input = Input(shape=(self.sequence_length, ))
model = Embedding(len(vocabolary_dict),
self.embedding_dim,
weights=[embedding_weights],
input_length=self.sequence_length,
name="embedding")(model_input)
model = Dropout(self.drop_prob[0])(model)
multi_cnn_channel = []
for kernal in self.kernal_size:
conv_channel = Convolution1D(filters=self.filters,
kernel_size=kernal,
padding="valid",
activation="relu",
strides=1)(model)
conv_channel = MaxPooling1D(pool_size=2)(conv_channel)
conv_channel = Flatten()(conv_channel)
multi_cnn_channel.append(conv_channel)
model = Concatenate()(multi_cnn_channel)
model = Dropout(self.drop_prob[1])(model)
for dimension in self.hidden_dims:
model = Dense(dimension, activation="relu")(model)
model_output = Dense(1, activation="sigmoid")(model)
model = Model(model_input, model_output)
model.compile(loss="binary_crossentropy",
optimizer="adam",
metrics=["accuracy"])
print("Started Training : ")
model.fit(x_train,
y_train,
batch_size=self.batch_size,
epochs=self.epochs,
validation_data=(x_test, y_test),
verbose=2)
print("Training Completed")
self.model = model
def existing_model(self, embedding_matrix, output_neurons):
inp = Input(shape=(self.maxlength, ))
model = Embedding(input_dim=embedding_matrix.shape[0],
output_dim=embedding_matrix.shape[1],
input_length=self.maxlength,
weights=[embedding_matrix],
trainable=False)(inp)
lstm = Bidirectional(LSTM(50, return_sequences=False),
merge_mode='concat')(model)
outputs = Dense(output_neurons, activation='sigmoid',
trainable=True)(lstm)
model = Model(inp, outputs)
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
return model
def create(self, train_file_path: str, output_summary: bool = False):
if not os.path.exists(train_file_path):
raise FileNotFoundError
with open(train_file_path, 'r') as train_file:
self.max_words_count_in_sentence = pd.read_csv(train_file).groupby(
"article_id").size().max()
input_layer = Input(shape=(self.max_words_count_in_sentence, ))
word_embedding_size = 150
model = Embedding(
input_dim=len(self.lang.vocab),
output_dim=word_embedding_size,
input_length=self.max_words_count_in_sentence)(input_layer)
model = Bidirectional(
LSTM(units=word_embedding_size,
return_sequences=True,
dropout=0.5,
recurrent_dropout=0.5,
kernel_initializer=keras.initializers.he_normal()))(model)
model = LSTM(units=word_embedding_size * 2,
return_sequences=True,
dropout=0.5,
recurrent_dropout=0.5,
kernel_initializer=keras.initializers.he_normal())(model)
model = TimeDistributed(Dense(len(self._tags),
activation="relu"))(model)
crf = CRF(len(self._tags))
model = Model(input_layer, crf(model))
model.compile(optimizer=keras.optimizers.Adam(lr=0.0005,
beta_1=0.9,
beta_2=0.999),
loss=crf.loss_function,
metrics=[crf.accuracy, 'accuracy'])
if output_summary:
model.summary()
self._model = model
def run(X_train, Y_train, X_val, Y_val, embedding_matrix, vocab_size, maxlen=40, emb_dim=300, neg_ratio=0, hidden_dim=300, drop=0.2, r_drop=0.1):
##build model
# input = Input(shape=(maxlen,))
# model = Embedding(vocab_size, emb_dim, weights=[embedding_matrix], input_length=maxlen, trainable=False)(input)
# model = Dropout(drop)(model)
# model = Bidirectional(LSTM(hidden_dim, return_sequences=True, recurrent_dropout=r_drop))(model)
# model = Dropout(drop)(model)
# out = TimeDistributed(Dense(1, activation='sigmoid'))(model)
input = Input(shape=(maxlen,))
model = Embedding(vocab_size, emb_dim, weights=[embedding_matrix], input_length=maxlen, trainable=False)(input)
model = Bidirectional(LSTM(hidden_dim, return_sequences=True, recurrent_dropout=r_drop))(model)
model = TimeDistributed(Dense(hidden_dim//4, activation='relu'))(model)
model = TimeDistributed(Dropout(drop))(model)
##use CRF instead of Dense
crf = CRF(2)
out = crf(model)
model = Model(input, out)
Y_train_2 = keras.utils.to_categorical(Y_train)
Y_val_2 = keras.utils.to_categorical(Y_val)
model.compile(optimizer='adam', loss=crf.loss_function, metrics=[crf.accuracy])
earlyStop = [EarlyStopping(monitor='val_loss', patience=1)]
history = model.fit(X_train, Y_train_2, batch_size=64, epochs=10,
validation_data=(X_val, Y_val_2), callbacks=earlyStop)
preds = model.predict(X_val)
test = [[np.argmax(y) for y in x] for x in preds]
test_arr = np.asarray(test)
test_arr = np.reshape(test_arr, (-1))
print (metrics.precision_recall_fscore_support(np.reshape(Y_val,(-1)), test_arr, average=None,
labels=[0, 1]))
# Y_pred_ = [[1 if y>=threshold else 0 for y in x] for x in Y_pred]
Y_val_ = np.squeeze(Y_val)
print ("Evaluate: dev seg exact")
pred_out_dir = out_dir+'seg_'+str(neg_ratio)+'neg'
gold_dir = '../../data/val_segs/'+'seg_'+str(neg_ratio)+'neg'
p, r, f = seg_exact_match(test, Y_val_, pred_out_dir, gold_dir)
return model, history, p, r, f
