def train_text_classifier():
dataset = args.dataset
x_train = y_train = x_test = y_test = None
if dataset == 'imdb':
train_texts, train_labels, test_texts, test_labels = split_imdb_files()
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif dataset == 'agnews':
train_texts, train_labels, test_texts, test_labels = split_agnews_files(
)
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif dataset == 'yahoo':
train_texts, train_labels, test_texts, test_labels = split_yahoo_files(
)
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(
train_texts, train_labels, test_texts, test_labels, dataset)
x_train, y_train = shuffle(x_train, y_train, random_state=0)
# Take a look at the shapes
print('dataset:', dataset, '; model:', args.model, '; level:', args.level)
print('X_train:', x_train.shape)
print('y_train:', y_train.shape)
print('X_test:', x_test.shape)
print('y_test:', y_test.shape)
log_dir = r'./logs/{}/{}/'.format(dataset, args.model)
tb_callback = keras.callbacks.TensorBoard(log_dir=log_dir,
histogram_freq=0,
write_graph=True)
model_path = r'runs/{}/{}.dat'.format(dataset, args.model)
model = batch_size = epochs = None
assert args.model[:4] == args.level
if args.model == "word_cnn":
model = word_cnn(dataset)
batch_size = config.wordCNN_batch_size[dataset]
epochs = config.wordCNN_epochs[dataset]
elif args.model == "word_bdlstm":
model = bd_lstm(dataset)
batch_size = config.bdLSTM_batch_size[dataset]
epochs = config.bdLSTM_epochs[dataset]
elif args.model == "char_cnn":
model = char_cnn(dataset)
batch_size = config.charCNN_batch_size[dataset]
epochs = config.charCNN_epochs[dataset]
elif args.model == "word_lstm":
model = lstm(dataset)
batch_size = config.LSTM_batch_size[dataset]
epochs = config.LSTM_epochs[dataset]
print('Train...')
print('batch_size: ', batch_size, "; epochs: ", epochs)
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2,
shuffle=True,
callbacks=[tb_callback])
scores = model.evaluate(x_test, y_test)
print('test_loss: %f, accuracy: %f' % (scores[0], scores[1]))
print('Saving model weights...')
model.save_weights(model_path)
def fool_text_classifier():
clean_samples_cap = args.clean_samples_cap # 1000
print('clean_samples_cap:', clean_samples_cap)
# get tokenizer
dataset = args.dataset
tokenizer = get_tokenizer(dataset)
# Read data set
x_test = y_test = None
test_texts = None
if dataset == 'imdb':
train_texts, train_labels, test_texts, test_labels = split_imdb_files()
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(train_texts, train_labels, test_texts, test_labels, dataset)
elif dataset == 'agnews':
train_texts, train_labels, test_texts, test_labels = split_agnews_files()
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(train_texts, train_labels, test_texts, test_labels, dataset)
elif dataset == 'yahoo':
train_texts, train_labels, test_texts, test_labels = split_yahoo_files()
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(train_texts, train_labels, test_texts, test_labels, dataset)
elif dataset == 'yahoo_csv':
train_texts, train_labels, test_texts, test_labels = split_yahoo_csv_files()
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(train_texts, train_labels, test_texts, test_labels, dataset)
# Write clean examples into a txt file
clean_texts_path = r'./fool_result/{}/clean_{}.txt'.format(dataset, str(clean_samples_cap))
if not os.path.isfile(clean_texts_path):
write_origin_input_texts(clean_texts_path, test_texts)
# Select the model and load the trained weights
assert args.model[:4] == args.level
model = None
if args.model == "word_cnn":
model = word_cnn(dataset)
elif args.model == "word_bdlstm":
model = bd_lstm(dataset)
elif args.model == "char_cnn":
model = char_cnn(dataset)
elif args.model == "word_lstm":
model = lstm(dataset)
model_path = r'./runs/{}/{}.dat'.format(dataset, args.model)
model.load_weights(model_path)
print('model path:', model_path)
# evaluate classification accuracy of model on clean samples
scores_origin = model.evaluate(x_test[:clean_samples_cap], y_test[:clean_samples_cap])
print('clean samples origin test_loss: %f, accuracy: %f' % (scores_origin[0], scores_origin[1]))
all_scores_origin = model.evaluate(x_test, y_test)
print('all origin test_loss: %f, accuracy: %f' % (all_scores_origin[0], all_scores_origin[1]))
grad_guide = ForwardGradWrapper(model)
classes_prediction = grad_guide.predict_classes(x_test[: clean_samples_cap])
# load HowNet candidates
with open(os.path.join('hownet_candidates', dataset, 'dataset_50000.pkl'), 'rb') as f:
dataset_dict = pickle.load(f)
with open(os.path.join('hownet_candidates', dataset, 'word_candidates_sense_0515.pkl'), 'rb') as fp:
word_candidate = pickle.load(fp)
print('Crafting adversarial examples...')
total_perturbated_text = 0
successful_perturbations = 0
failed_perturbations = 0
sub_word_list = []
sub_rate_list = []
NE_rate_list = []
start_cpu = time.clock()
adv_text_path = r'./fool_result/{}/{}/adv_{}.txt'.format(dataset, args.model, str(clean_samples_cap))
change_tuple_path = r'./fool_result/{}/{}/change_tuple_{}.txt'.format(dataset, args.model, str(clean_samples_cap))
file_1 = open(adv_text_path, "a")
file_2 = open(change_tuple_path, "a")
for index, text in enumerate(test_texts[: clean_samples_cap]):
if np.argmax(y_test[index]) == classes_prediction[index]:
total_perturbated_text += 1
# If the ground_true label is the same as the predicted label
adv_doc, adv_y, sub_word, sub_rate, NE_rate, change_tuple_list = adversarial_paraphrase(input_text=text,
true_y=np.argmax(y_test[index]),
grad_guide=grad_guide,
tokenizer=tokenizer,
dataset=dataset,
dataset_dict=dataset_dict,
word_candidate=word_candidate,
level=args.level)
if adv_y != np.argmax(y_test[index]):
successful_perturbations += 1
print('{}. Successful example crafted.'.format(index))
else:
failed_perturbations += 1
print('{}. Failure.'.format(index))
text = adv_doc
sub_word_list.append(sub_word)
sub_rate_list.append(sub_rate)
NE_rate_list.append(NE_rate)
file_2.write(str(index) + str(change_tuple_list) + '\n')
file_1.write(text + "\n")
end_cpu = time.clock()
print('CPU second:', end_cpu - start_cpu)
mean_sub_word = sum(sub_word_list) / len(sub_word_list)
mean_fool_rate = successful_perturbations / total_perturbated_text
mean_sub_rate = sum(sub_rate_list) / len(sub_rate_list)
mean_NE_rate = sum(NE_rate_list) / len(NE_rate_list)
print('mean substitution word:', mean_sub_word)
print('mean fooling rate:', mean_fool_rate)
print('mean substitution rate:', mean_sub_rate)
print('mean NE rate:', mean_NE_rate)
file_1.close()
file_2.close()
def fool_text_classifier_pytorch(model, dataset='imdb'):
clean_samples_cap = 100
print('clean_samples_cap:', clean_samples_cap)
# get tokenizer
tokenizer = get_tokenizer(opt)
# Read data set
x_test = y_test = None
test_texts = None
if dataset == 'imdb':
train_texts, train_labels, dev_texts, dev_labels, test_texts, test_labels = split_imdb_files(
opt)
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif dataset == 'agnews':
train_texts, train_labels, test_texts, test_labels = split_agnews_files(
)
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif dataset == 'yahoo':
train_texts, train_labels, test_texts, test_labels = split_yahoo_files(
)
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
grad_guide = ForwardGradWrapper_pytorch(model)
classes_prediction = grad_guide.predict_classes(x_test[:clean_samples_cap])
print('Crafting adversarial examples...')
successful_perturbations = 0
failed_perturbations = 0
sub_rate_list = []
NE_rate_list = []
start_cpu = time.clock()
adv_text_path = r'./fool_result/{}/adv_{}.txt'.format(
dataset, str(clean_samples_cap))
change_tuple_path = r'./fool_result/{}/change_tuple_{}.txt'.format(
dataset, str(clean_samples_cap))
file_1 = open(adv_text_path, "a")
file_2 = open(change_tuple_path, "a")
for index, text in enumerate(test_texts[:clean_samples_cap]):
sub_rate = 0
NE_rate = 0
if np.argmax(y_test[index]) == classes_prediction[index]:
# If the ground_true label is the same as the predicted label
adv_doc, adv_y, sub_rate, NE_rate, change_tuple_list = adversarial_paraphrase(
input_text=text,
true_y=np.argmax(y_test[index]),
grad_guide=grad_guide,
tokenizer=tokenizer,
dataset=dataset,
level='word')
if adv_y != np.argmax(y_test[index]):
successful_perturbations += 1
print('{}. Successful example crafted.'.format(index))
else:
failed_perturbations += 1
print('{}. Failure.'.format(index))
text = adv_doc
sub_rate_list.append(sub_rate)
NE_rate_list.append(NE_rate)
file_2.write(str(index) + str(change_tuple_list) + '\n')
file_1.write(text + " sub_rate: " + str(sub_rate) + "; NE_rate: " +
str(NE_rate) + "\n")
end_cpu = time.clock()
print('CPU second:', end_cpu - start_cpu)
mean_sub_rate = sum(sub_rate_list) / len(sub_rate_list)
mean_NE_rate = sum(NE_rate_list) / len(NE_rate_list)
print('mean substitution rate:', mean_sub_rate)
print('mean NE rate:', mean_NE_rate)
file_1.close()
file_2.close()
if __name__ == '__main__':
args = parser.parse_args()
clean_samples_cap = args.clean_samples_cap # 1000
# get tokenizer
dataset = args.dataset
tokenizer = get_tokenizer(dataset)
# Read data set
x_train = y_train = x_test = y_test = None
test_texts = None
first_get_dataset = False
if dataset == 'imdb':
train_texts, train_labels, test_texts, test_labels = split_imdb_files()
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif dataset == 'agnews':
train_texts, train_labels, test_texts, test_labels = split_agnews_files(
)
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(
train_texts, train_labels, test_texts, test_labels, dataset)
def fool_text_classifier():
clean_samples_cap = args.clean_samples_cap # 1000
print('clean_samples_cap:', clean_samples_cap)
# get tokenizer
dataset = args.dataset
tokenizer = get_tokenizer(dataset)
# Read data set
x_test = y_test = None
test_texts = None
if dataset == 'imdb':
train_texts, train_labels, test_texts, test_labels = split_imdb_files()
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif dataset == 'agnews':
train_texts, train_labels, test_texts, test_labels = split_agnews_files(
)
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif dataset == 'yahoo':
train_texts, train_labels, test_texts, test_labels = split_yahoo_files(
)
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(
train_texts, train_labels, test_texts, test_labels, dataset)
# Write clean examples into a txt file
clean_path = '/content/drive/My Drive/PWWS_Pretrained' #r./fool_result/{}'.format(dataset, str(clean_samples_cap))
if not os.path.exists(clean_path):
os.mkdir(clean_path)
# if not os.path.isfile(clean_path):
write_origin_input_texts(os.path.join(clean_path, 'clean_1000.txt'),
test_texts)
# write_origin_input_texts(os.path.join(clean_path, 'x_processed_1000'), x_test)
# write_origin_input_texts(os.path.join(clean_path, 'y_1000'), y_test)
np.save(os.path.join(clean_path, 'x_processed_1000.npy'), x_test)
np.save(os.path.join(clean_path, 'y_1000.npy'), y_test)
with open(os.path.join(clean_path, 'clean_1000.txt'), 'r') as f:
test_texts = f.readlines()
x_test = np.load(os.path.join(clean_path, 'x_processed_1000.npy'))
y_test = np.load(os.path.join(clean_path, 'y_1000.npy'))
# Select the model and load the trained weights
assert args.model[:4] == args.level
model = None
if args.model == "word_cnn":
model = word_cnn(dataset)
elif args.model == "word_bdlstm":
model = bd_lstm(dataset)
elif args.model == "char_cnn":
model = char_cnn(dataset)
elif args.model == "word_lstm":
model = lstm(dataset)
model_path = r'runs/{}/{}.dat'.format(dataset, args.model)
model.load_weights(model_path)
print('model path:', model_path)
# evaluate classification accuracy of model on clean samples
scores_origin = model.evaluate(x_test, y_test)
print('clean samples origin test_loss: %f, accuracy: %f' %
(scores_origin[0], scores_origin[1]))
all_scores_origin = model.evaluate(x_test, y_test)
print('all origin test_loss: %f, accuracy: %f' %
(all_scores_origin[0], all_scores_origin[1]))
grad_guide = ForwardGradWrapper(model)
classes_prediction = grad_guide.predict_classes(x_test)
print('Crafting adversarial examples...')
successful_perturbations = 0
failed_perturbations = 0
sub_rate_list = []
NE_rate_list = []
start_cpu = time.clock()
adv_text_path = r'./fool_result/{}/{}/adv_{}.txt'.format(
dataset, args.model, str(clean_samples_cap))
change_tuple_path = r'./fool_result/{}/{}/change_tuple_{}.txt'.format(
dataset, args.model, str(clean_samples_cap))
file_1 = open(adv_text_path, "a")
file_2 = open(change_tuple_path, "a")
for index, text in enumerate(test_texts[:clean_samples_cap]):
# print(text)
sub_rate = 0
NE_rate = 0
if np.argmax(y_test[index]) == classes_prediction[index]:
# If the ground_true label is the same as the predicted label
adv_doc, adv_y, sub_rate, NE_rate, change_tuple_list = adversarial_paraphrase(
input_text=text,
true_y=np.argmax(y_test[index]),
grad_guide=grad_guide,
tokenizer=tokenizer,
dataset=dataset,
level=args.level)
if adv_y != np.argmax(y_test[index]):
successful_perturbations += 1
print('{}. Successful example crafted.'.format(index))
else:
failed_perturbations += 1
print('{}. Failure.'.format(index))
text = adv_doc
sub_rate_list.append(sub_rate)
NE_rate_list.append(NE_rate)
file_2.write(str(index) + str(change_tuple_list) + '\n')
file_1.write(text + " sub_rate: " + str(sub_rate) + "; NE_rate: " +
str(NE_rate) + "\n")
end_cpu = time.clock()
print('CPU second:', end_cpu - start_cpu)
mean_sub_rate = sum(sub_rate_list) / len(sub_rate_list)
mean_NE_rate = sum(NE_rate_list) / len(NE_rate_list)
print('mean substitution rate:', mean_sub_rate)
print('mean NE rate:', mean_NE_rate)
print('successful perturbations:', successful_perturbations)
print('failed perturbations:', failed_perturbations)
print(
'success rate:', successful_perturbations /
(failed_perturbations + successful_perturbations))
file_1.close()
file_2.close()
def train_text_classifier():
dataset = args.dataset
x_train = y_train = x_test = y_test = None
if dataset == 'imdb':
train_texts, train_labels, test_texts, test_labels = split_imdb_files()
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif dataset == 'agnews':
train_texts, train_labels, test_texts, test_labels = split_agnews_files(
)
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif dataset == 'yahoo':
train_texts, train_labels, test_texts, test_labels = split_yahoo_files(
)
if args.level == 'word':
x_train, y_train, x_test, y_test = word_process(
train_texts, train_labels, test_texts, test_labels, dataset)
elif args.level == 'char':
x_train, y_train, x_test, y_test = char_process(
train_texts, train_labels, test_texts, test_labels, dataset)
x_train, y_train = shuffle(x_train, y_train, random_state=0)
# Take a look at the shapes
print('dataset:', dataset, '; model:', args.model, '; level:', args.level)
print('X_train:', x_train.shape)
print('y_train:', y_train.shape)
print('X_test:', x_test.shape)
print('y_test:', y_test.shape)
log_dir = r'./logs/{}/{}/'.format(dataset, args.model)
tb_callback = keras.callbacks.TensorBoard(log_dir=log_dir,
histogram_freq=0,
write_graph=True)
model_path = r'./runs/{}/{}.dat_100embed_binary'.format(
dataset, args.model)
model = batch_size = epochs = None
assert args.model[:4] == args.level
if args.model == "word_cnn":
model = word_cnn(dataset)
batch_size = config.wordCNN_batch_size[dataset]
epochs = config.wordCNN_epochs[dataset]
elif args.model == "word_bdlstm":
model = bd_lstm(dataset)
batch_size = config.bdLSTM_batch_size[dataset]
epochs = config.bdLSTM_epochs[dataset]
elif args.model == "char_cnn":
model = char_cnn(dataset)
batch_size = config.charCNN_batch_size[dataset]
epochs = config.charCNN_epochs[dataset]
elif args.model == "word_lstm":
model = lstm(dataset)
batch_size = config.LSTM_batch_size[dataset]
epochs = config.LSTM_epochs[dataset]
print('Train...')
print('batch_size: ', batch_size, "; epochs: ", epochs)
# get threshold
## compute Gilbert–Varshamov Bound ##
def _check_(L, M, d):
left = 0
for i in range(0, d):
left = left + scipy.special.binom(L, i)
tmp = float(pow(2, int(L))) / left
return (tmp > M)
def gvbound(L, M, tight=False):
d = 1
while (_check_(L, M, d)):
d += 1
if not tight: d -= 1
print('Distance for %d classes with %d bits (GV bound): %d' %
(M, L, d))
return d
## compute Hamming Bound ##
def check(L, M, d):
right = float(pow(2, int(L))) / float(M)
x = math.floor(float(d - 1) / 2.0)
left = 0
for i in range(0, x + 1):
left = left + scipy.special.binom(L, i)
return (left <= right)
def hammingbound(L, M, tight=False):
d = 0
while (check(L, M, d)):
d += 1
if not tight: d -= 1
print('Distance for %d classes with %d bits (Hamming bound): %d' %
(M, L, d))
return d
embedding_dims = config.wordCNN_embedding_dims[dataset]
num_words = config.num_words[dataset]
ham_bound = hammingbound(embedding_dims, num_words)
gv_bound = gvbound(embedding_dims, num_words)
bound = ham_bound
def custom_loss_wrapper(y_true, y_pred):
# ce-loss
xent_loss = losses.binary_crossentropy(y_true, y_pred)
# quantization loss
embedding_output = model.layers[0].output
Bbatch = tf.sign(embedding_output)
quan_l = K.mean(
keras.losses.mean_absolute_error(Bbatch, embedding_output))
# ecoc loss
centers = model.layers[0].weights[0]
centers_binary = tf.sign(centers)
norm_centers = tf.math.l2_normalize(centers, axis=1)
prod = tf.matmul(norm_centers,
tf.transpose(norm_centers)) #5000 x 5000
ham_dist = 0.5 * (embedding_dims - prod)
marg_loss = K.mean(ham_dist)
#tmp = K.maximum(0.0, ham_dist - bound)
#non_zero = K.sum(K.cast(tmp > 0, dtype='float32'))
#marg_loss = K.sum(tmp) * 1.0/(non_zero+1.0)
#tmp2 = K.maximum(0.0, gv_bound - ham_dist)
#non_zero2 = K.sum(K.cast(tmp2 > 0, dtype='float32'))
#marg_loss2 = K.sum(tmp2) * 1.0/(non_zero2+1.0)
return xent_loss + 0.1 * quan_l
#centers = model.layers[0].weights[0]
#norm_centers = tf.math.l2_normalize(centers, axis=1)
#prod = tf.matmul(norm_centers, tf.transpose(norm_centers))
#ham_dist = 0.5 * (embedding_dims - prod)
#marg_loss = K.mean(K.maximum(0.0, 26 - ham_dist))
#get_output = K.function([model.layers[0].input],
#[prod])
#x = get_output([x_train])[0]
#centers = centers[0]
#prod = tf.matmul(centers, tf.transpose(centers))
#pdb.set_trace()
def quan_loss(y_true, y_pred):
embedding_output = model.layers[0].output
Bbatch = tf.sign(embedding_output)
quan_l = K.mean(
keras.losses.mean_absolute_error(Bbatch, embedding_output))
return quan_l
def ecoc_loss(y_true, y_pred):
# ecoc loss
centers = model.layers[0].weights[0]
centers_binary = tf.sign(centers)
norm_centers = tf.math.l2_normalize(centers, axis=1)
prod = tf.matmul(norm_centers,
tf.transpose(norm_centers)) #5000 x 5000
#ham_dist = 0.5 * (embedding_dims - prod)
marg_loss = K.mean(prod)
#tmp = K.maximum(0.0, bound - ham_dist)
#non_zero = K.sum(K.cast(tmp > 0, dtype='float32'))
#marg_loss = K.sum(tmp) * 1.0/(non_zero+1.0)
#tmp = K.maximum(0.0, ham_dist - bound)
#non_zero = K.sum(K.cast(tmp > 0, dtype='float32'))
#marg_loss = K.sum(tmp) * 1.0/(non_zero+1.0)
return marg_loss
model.compile(loss=custom_loss_wrapper,
optimizer='adam',
metrics=['accuracy', quan_loss, ecoc_loss])
#tmp = model.predict(x_train,batch_size=batch_size)
#pdb.set_trace()
model.fit(
x_train,
y_train,
batch_size=batch_size, # batch_size => 32
epochs=10, #epochs,
validation_split=0.2,
shuffle=True,
callbacks=[tb_callback])
scores = model.evaluate(x_test, y_test)
print('test_loss: %f, accuracy: %f' % (scores[0], scores[1]))
print('Saving model weights...')
model.save_weights(model_path)
