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all_sequence_script.py
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import numpy as np
import tensorflow as tf
import os, random
from sklearn.model_selection import train_test_split
from keras.models import Model, load_model
from keras.layers import Bidirectional, Lambda, Masking, Dense, Input, Dropout, LSTM, Activation, TimeDistributed, BatchNormalization, concatenate, Concatenate
from keras.layers.embeddings import Embedding
from keras.constraints import max_norm, min_max_norm, unit_norm
from keras import regularizers
from keras.initializers import random_uniform
from keras.callbacks import ModelCheckpoint, CSVLogger, ReduceLROnPlateau, TensorBoard
from keras.utils import to_categorical
from keras import backend as K
from all_elmo_sequence import DataGenerator
# set random seed to seed_value for reproducability
seed_value = 1
os.environ['PYTHONHASHSEED'] = str(seed_value)
random.seed(seed_value)
np.random.seed(seed_value)
tf.random.set_seed(seed_value)
# parameters for generator class
params = {'n_pos1_classes':30,
'n_pos2_classes':32,
'n_super_classes':891,
'shuffle':True,
'batch_size':32}
# filenames for best and last model files
best_file = 'best_gen_elmo_superpos.h5'
current_file = 'current_gen_elmo_superpos.h5'
# number of sentences in the treebank; presupposes the existence of file "sent%06d.npz"
# for i from 0 to treebank_sentences-1 in the TLGbank directory
# treebank_sentences = 1000
treebank_sentences = 15748
all = ["sent%06d" %i for i in range(treebank_sentences)]
# standard 60/20/20 split for train/dev/test
train, testdev = train_test_split(all, test_size=0.4)
test, dev = train_test_split(testdev, test_size=0.5)
print("Train: "+str(len(train)))
print("Dev : "+str(len(dev)))
print("Test : "+str(len(test)))
training_generator = DataGenerator(train, **params)
validation_generator = DataGenerator(dev, **params)
embLen = 1024
numPos1Classes = 30
numPos2Classes = 32
numSuperClasses = 891
l1_value = 0.0001
l2_value = 0.0001
# input layers are the three ELMo output layers
sentence_embeddings0 = Input(shape = (None,embLen,), dtype = 'float32')
sentence_embeddings1 = Input(shape = (None,embLen,), dtype = 'float32')
sentence_embeddings2 = Input(shape = (None,embLen,), dtype = 'float32')
# take weighted average of three inputs
#stacked = Lambda(lambda x: K.stack([x[0],x[1],x[2]], axis=-1))([sentence_embeddings0,sentence_embeddings1,sentence_embeddings2])
# use unit_norm to force sum of 1 and intialize weights close to 0.33
#weighted = Dense(1, kernel_constraint=unit_norm(), kernel_initializer=random_uniform(0.30,0.36), use_bias=False)(stacked)
# use weighted sum with l1l2 regularization
#weighted = Dense(1, kernel_regularizer=regularizers.l1_l2(l1_value,l2_value), use_bias=False)(stacked)
#weighted = Lambda(lambda x: K.squeeze(x, axis=-1))(weighted)
concat = concatenate([sentence_embeddings0, sentence_embeddings1, sentence_embeddings2])
mask = Masking(mask_value=0.0)(concat)
dropout = Dropout(0.5)(mask)
# first bi-directional LSTM layer
X = Bidirectional(LSTM(128, recurrent_dropout=0.2, kernel_constraint=max_norm(4.), return_sequences=True))(dropout)
X = BatchNormalization()(X)
X = Dropout(0.2)(X)
# Pos1 output
Pos1 = TimeDistributed(Dense(32,kernel_constraint=max_norm(5.)))(X)
Pos1 = TimeDistributed(Dropout(0.2))(Pos1)
pos1_output = TimeDistributed(Dense(numPos1Classes, name='pos1_output', activation='softmax',kernel_constraint=max_norm(4.)))(Pos1)
# Pos2 output
Pos2 = TimeDistributed(Dense(32,kernel_constraint=max_norm(5.)))(X)
Pos2 = TimeDistributed(Dropout(0.2))(Pos2)
pos2_output = TimeDistributed(Dense(numPos2Classes, name='pos2_output', activation='softmax',kernel_constraint=max_norm(4.)))(Pos2)
# second bi-directional LSTM layer
X = Bidirectional(LSTM(128, recurrent_dropout=0.2, kernel_constraint=max_norm(4.), return_sequences=True))(X)
X = BatchNormalization()(X)
X = Dropout(0.2)(X)
# concatenate ELMo vectors before output; doesn't improve performance
# X = concatenate([X,dropout])
# supertag output
X = TimeDistributed(Dense(32,kernel_constraint=max_norm(5.)))(X)
#X = TimeDistributed(Dense(32,kernel_regularizer=regularizers.l1_l2(l1_value,l2_value)))(X)
X = TimeDistributed(Dropout(0.2))(X)
super_output = TimeDistributed(Dense(numSuperClasses, name='super_output', activation='softmax',kernel_regularizer=regularizers.l1_l2(l1_value,l2_value)))(X)
model = Model([sentence_embeddings0, sentence_embeddings1, sentence_embeddings2], [pos1_output,pos2_output,super_output])
model.summary()
model.compile(loss=['categorical_crossentropy','categorical_crossentropy','categorical_crossentropy'], optimizer='rmsprop', metrics=['accuracy'])
checkpoint = ModelCheckpoint(best_file, monitor='val_time_distributed_8_accuracy', verbose=1, save_best_only=True, mode='max')
save_current = ModelCheckpoint(current_file, monitor='val_time_distributed_8_accuracy', verbose=1, save_best_only=False, save_weights_only=False, mode='auto', save_freq=1)
reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.2,\
verbose=1,patience=5, min_lr=0.0001)
log = CSVLogger('elmo_training_log.csv')
history = model.fit(training_generator,\
epochs=100, shuffle=True,\
callbacks = [checkpoint,reduce_lr,log,save_current],
validation_data=validation_generator)