def main():
# load the dataset
datasetManager = DatasetManager()
datasetManager.initialize('CNN').load()
#
counter = 0
code_archive = []
languages = []
for languageFolder in FileManager.getLanguagesFolders(
FileManager.datasets['training']['url']):
for exampleFolder in FileManager.getExamplesFolders(
languageFolder.path):
originalFileUrl = FileManager.getOriginalFileUrl(
exampleFolder.path)
originalFileContent = FileManager.readFile(originalFileUrl)
#
counter += 1
code_archive.append(originalFileContent)
languages.append(str(languageFolder.name).lower())
# added - and @
max_fatures = 100000
embed_dim = 128
lstm_out = 64
batch_size = 32
epochs = 30
test_size = 0.001
tokenizer = Tokenizer(num_words=max_fatures)
tokenizer.fit_on_texts(code_archive)
dictionary = tokenizer.word_index
FileManager.createFile(
os.path.join(FileManager.getRootUrl(), 'tmp/wordindex.json'),
json.dumps(dictionary))
X = tokenizer.texts_to_sequences(code_archive)
X = pad_sequences(X, 100)
Y = pd.get_dummies(languages)
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
test_size=test_size)
# LSTM model
model = Sequential()
model.add(Embedding(max_fatures, embed_dim, input_length=100))
model.add(
Conv1D(filters=128,
kernel_size=3,
padding='same',
dilation_rate=1,
activation='relu'))
model.add(MaxPooling1D(pool_size=4))
model.add(
Conv1D(filters=64,
kernel_size=3,
padding='same',
dilation_rate=1,
activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(LSTM(lstm_out))
model.add(Dropout(0.5))
model.add(Dense(64))
model.add(Dense(len(Y.columns), activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
history = model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size)
model.save(os.path.join(FileManager.getRootUrl(), 'tmp/code_model.h5'))
model.save_weights(
os.path.join(FileManager.getRootUrl(), 'tmp/code_model_weights.h5'))
score, acc = model.evaluate(X_test,
Y_test,
verbose=2,
batch_size=batch_size)
print(model.metrics_names)
print("Validation loss: %f" % score)
print("Validation acc: %f" % acc)
from keras.models import load_model
import keras.preprocessing.text as kpt
from keras.preprocessing.sequence import pad_sequences
import sys
import os
import json
import numpy as np
from utils import ConfigurationManager, FileManager
##
global dictionary
global model
dictionaryUrl = os.path.join(FileManager.getRootUrl(), 'tmp/wordindex.json')
dictionary = json.loads(FileManager.readFile(dictionaryUrl))
modelUrl = os.path.join(FileManager.getRootUrl(), 'tmp/code_model.h5')
model = load_model(modelUrl)
def convert_text_to_index_array(text):
# one really important thing that `text_to_word_sequence` does
# is make all texts the same length -- in this case, the length
# of the longest text in the set.
wordvec = []
for word in kpt.text_to_word_sequence(text):
if word in dictionary:
if dictionary[word] <= 100000:
