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cnn.py
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cnn.py
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from keras.models import Model, model_from_json, Sequential
from keras.layers import Input, SpatialDropout1D, GlobalMaxPool1D
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers import Dropout
from keras.layers import Embedding
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from keras.layers.merge import concatenate
from keras.callbacks import ModelCheckpoint
import numpy as np
import os
from keras.preprocessing.sequence import pad_sequences
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
from keras_en_parser_and_analyzer.library.utility.tokenizer_utils import word_tokenize
class WordVecCnn(object):
model_name = 'wordvec_cnn'
def __init__(self):
self.model = None
self.word2idx = None
self.idx2word = None
self.max_len = None
self.config = None
self.vocab_size = None
self.labels = None
@staticmethod
def get_weight_file_path(model_dir_path):
return model_dir_path + '/' + WordVecCnn.model_name + '_weights.h5'
@staticmethod
def get_config_file_path(model_dir_path):
return model_dir_path + '/' + WordVecCnn.model_name + '_config.npy'
@staticmethod
def get_architecture_file_path(model_dir_path):
return model_dir_path + '/' + WordVecCnn.model_name + '_architecture.json'
def load_model(self, model_dir_path):
json = open(self.get_architecture_file_path(model_dir_path), 'r').read()
self.model = model_from_json(json)
self.model.load_weights(self.get_weight_file_path(model_dir_path))
self.model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
config_file_path = self.get_config_file_path(model_dir_path)
self.config = np.load(config_file_path).item()
self.idx2word = self.config['idx2word']
self.word2idx = self.config['word2idx']
self.max_len = self.config['max_len']
self.vocab_size = self.config['vocab_size']
self.labels = self.config['labels']
def create_model(self):
embedding_size = 100
self.model = Sequential()
self.model.add(Embedding(input_dim=self.vocab_size, input_length=self.max_len, output_dim=embedding_size))
self.model.add(SpatialDropout1D(0.2))
self.model.add(Conv1D(filters=256, kernel_size=5, padding='same', activation='relu'))
self.model.add(GlobalMaxPool1D())
self.model.add(Dense(units=len(self.labels), activation='softmax'))
self.model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
def predict(self, sentence):
xs = []
tokens = [w.lower() for w in word_tokenize(sentence)]
wid = [self.word2idx[token] if token in self.word2idx else len(self.word2idx) for token in tokens]
xs.append(wid)
x = pad_sequences(xs, self.max_len)
output = self.model.predict(x)
return output[0]
def predict_class(self, sentence):
predicted = self.predict(sentence)
idx2label = dict([(idx, label) for label, idx in self.labels.items()])
return idx2label[np.argmax(predicted)]
def fit(self, text_data_model, text_label_pairs, model_dir_path, batch_size=None, epochs=None,
test_size=None, random_state=None):
if batch_size is None:
batch_size = 64
if epochs is None:
epochs = 20
if test_size is None:
test_size = 0.3
if random_state is None:
random_state = 42
self.config = text_data_model
self.idx2word = self.config['idx2word']
self.word2idx = self.config['word2idx']
self.max_len = self.config['max_len']
self.vocab_size = self.config['vocab_size']
self.labels = self.config['labels']
np.save(self.get_config_file_path(model_dir_path), self.config)
self.create_model()
json = self.model.to_json()
open(self.get_architecture_file_path(model_dir_path), 'w').write(json)
xs = []
ys = []
for text, label in text_label_pairs:
tokens = [x.lower() for x in word_tokenize(text)]
wid_list = list()
for w in tokens:
wid = 0
if w in self.word2idx:
wid = self.word2idx[w]
wid_list.append(wid)
xs.append(wid_list)
ys.append(self.labels[label])
X = pad_sequences(xs, maxlen=self.max_len)
Y = np_utils.to_categorical(ys, len(self.labels))
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=random_state)
print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)
weight_file_path = self.get_weight_file_path(model_dir_path)
checkpoint = ModelCheckpoint(weight_file_path)
history = self.model.fit(x=x_train, y=y_train, batch_size=batch_size, epochs=epochs,
validation_data=[x_test, y_test], callbacks=[checkpoint],
verbose=1)
self.model.save_weights(weight_file_path)
np.save(model_dir_path + '/' + WordVecCnn.model_name + '-history.npy', history.history)
score = self.model.evaluate(x=x_test, y=y_test, batch_size=batch_size, verbose=1)
print('score: ', score[0])
print('accuracy: ', score[1])
return history
def test_run(self, sentence):
print(self.predict(sentence))
class WordVecMultiChannelCnn(object):
model_name = 'wordvec_multi_channel_cnn'
def __init__(self):
self.model = None
self.config = None
self.word2idx = None
self.idx2word = None
self.max_len = None
self.config = None
self.vocab_size = None
self.labels = None
@staticmethod
def get_weight_file_path(model_dir_path):
return model_dir_path + os.path.sep + WordVecMultiChannelCnn.model_name + '_weights.h5'
@staticmethod
def get_config_file_path(model_dir_path):
return model_dir_path + os.path.sep + WordVecMultiChannelCnn.model_name + '_config.npy'
@staticmethod
def get_architecture_file_path(model_dir_path):
return model_dir_path + os.path.sep + WordVecMultiChannelCnn.model_name + '_architecture.npy'
def load_model(self, model_dir_path):
config_file_path = self.get_config_file_path(model_dir_path)
self.config = np.load(config_file_path).item()
self.idx2word = self.config['idx2word']
self.word2idx = self.config['word2idx']
self.max_len = self.config['max_len']
self.vocab_size = self.config['vocab_size']
self.labels = self.config['labels']
max_input_tokens = len(self.word2idx)
self.model = self.define_model(self.max_len, max_input_tokens)
self.model.load_weights(self.get_weight_file_path(model_dir_path))
def define_model(self, length, vocab_size):
embedding_size = 100
cnn_filter_size = 32
inputs1 = Input(shape=(length,))
embedding1 = Embedding(vocab_size, embedding_size)(inputs1)
conv1 = Conv1D(filters=cnn_filter_size, kernel_size=4, activation='relu')(
embedding1)
drop1 = Dropout(0.5)(conv1)
pool1 = MaxPooling1D(pool_size=2)(drop1)
flat1 = Flatten()(pool1)
inputs2 = Input(shape=(length,))
embedding2 = Embedding(vocab_size, embedding_size)(inputs2)
conv2 = Conv1D(filters=cnn_filter_size, kernel_size=6, activation='relu')(
embedding2)
drop2 = Dropout(0.5)(conv2)
pool2 = MaxPooling1D(pool_size=2)(drop2)
flat2 = Flatten()(pool2)
inputs3 = Input(shape=(length,))
embedding3 = Embedding(vocab_size, embedding_size)(inputs3)
conv3 = Conv1D(filters=cnn_filter_size, kernel_size=8, activation='relu')(
embedding3)
drop3 = Dropout(0.5)(conv3)
pool3 = MaxPooling1D(pool_size=2)(drop3)
flat3 = Flatten()(pool3)
merged = concatenate([flat1, flat2, flat3])
# interpretation
dense1 = Dense(10, activation='relu')(merged)
outputs = Dense(units=len(self.labels), activation='softmax')(dense1)
model = Model(inputs=[inputs1, inputs2, inputs3], outputs=outputs)
# compile
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# summarize
print(model.summary())
return model
def fit(self, text_data_model, text_label_pairs, model_dir_path,
test_size=None, random_state=None,
epochs=None, batch_size=None):
if epochs is None:
epochs = 10
if batch_size is None:
batch_size = 16
if test_size is None:
test_size = 0.3
if random_state is None:
random_state = 42
self.config = text_data_model
self.idx2word = self.config['idx2word']
self.word2idx = self.config['word2idx']
self.max_len = self.config['max_len']
self.vocab_size = self.config['vocab_size']
self.labels = self.config['labels']
verbose = 1
config_file_path = WordVecMultiChannelCnn.get_config_file_path(model_dir_path)
np.save(config_file_path, text_data_model)
max_input_tokens = len(self.word2idx)
self.model = self.define_model(self.max_len, max_input_tokens)
open(self.get_architecture_file_path(model_dir_path), 'wt').write(self.model.to_json())
xs = []
ys = []
for text, label in text_label_pairs:
tokens = [x.lower() for x in word_tokenize(text)]
wid_list = list()
for w in tokens:
wid = 0
if w in self.word2idx:
wid = self.word2idx[w]
wid_list.append(wid)
xs.append(wid_list)
ys.append(self.labels[label])
X = pad_sequences(xs, maxlen=self.max_len)
Y = np_utils.to_categorical(ys, len(self.labels))
weight_file_path = WordVecMultiChannelCnn.get_weight_file_path(model_dir_path)
checkpoint = ModelCheckpoint(weight_file_path)
history = self.model.fit([X, X, X], Y, epochs=epochs, batch_size=batch_size,
validation_split=test_size,
verbose=verbose, callbacks=[checkpoint])
# save the model
self.model.save(weight_file_path)
np.save(model_dir_path + '/' + WordVecMultiChannelCnn.model_name + '-history.npy', history.history)
return history
def predict(self, sentence):
xs = []
tokens = [w.lower() for w in word_tokenize(sentence)]
wid = [self.word2idx[token] if token in self.word2idx else len(self.word2idx) for token in tokens]
xs.append(wid)
x = pad_sequences(xs, self.max_len)
output = self.model.predict([x, x, x])
return output[0]
def predict_class(self, sentence):
predicted = self.predict(sentence)
idx2label = dict([(idx, label) for label, idx in self.labels.items()])
return idx2label[np.argmax(predicted)]
def test_run(self, sentence):
print(self.predict(sentence))