def fit(self):
with tf.device("/cpu:0"):
self.init_pt_model()
parallel_pt_model = ModelMGPU(self.pt_model, gpus=cnt.USE_NUM_GPUS)
adam = optimizers.Adam(lr=0.001)
parallel_pt_model.compile(optimizer=adam,
loss="categorical_crossentropy",
metrics=['accuracy'])
callbacks = [
ModelCheckpoint(filepath=cnt.PT_MODEL_PATH,
monitor='val_loss',
save_best_only=True),
]
parallel_pt_model.fit_generator(
self.data_generator(self.num_train, 'train', 'pt'),
callbacks=callbacks,
steps_per_epoch=shutils.get_num_batches(self.num_train,
cnt.BATCH_SIZE),
validation_data=self.data_generator(self.num_test, 'test', 'pt'),
validation_steps=shutils.get_num_batches(self.num_test,
cnt.BATCH_SIZE),
epochs=cnt.NUM_EPOCHS,
verbose=1,
use_multiprocessing=True)
with tf.device("/cpu:0"):
self.init_color_model()
parallel_color_model = ModelMGPU(self.color_model,
gpus=cnt.USE_NUM_GPUS)
adam = optimizers.Adam(lr=0.001)
parallel_color_model.compile(optimizer=adam,
loss="categorical_crossentropy",
metrics=['accuracy'])
callbacks = [
ModelCheckpoint(filepath=cnt.COLOR_MODEL_PATH,
monitor='val_loss',
save_best_only=True),
]
parallel_color_model.fit_generator(
self.data_generator(self.num_train, 'train', 'color'),
callbacks=callbacks,
steps_per_epoch=shutils.get_num_batches(self.num_train,
cnt.BATCH_SIZE),
validation_data=self.data_generator(self.num_test, 'test',
'color'),
validation_steps=shutils.get_num_batches(self.num_test,
cnt.BATCH_SIZE),
epochs=cnt.NUM_EPOCHS,
verbose=1,
use_multiprocessing=True)
def fit(self):
curr_best_validation_loss = float("Inf")
for epoch in range(cnt.NUM_EPOCHS):
train_iter = self.data_generator(self.num_train, 'train')
valid_iter = self.data_generator(self.num_valid, 'valid')
steps_per_epoch_train = shutils.get_num_batches(
self.num_train, cnt.BATCH_SIZE)
steps_per_epoch_valid = shutils.get_num_batches(
self.num_valid, cnt.BATCH_SIZE)
total_loss = 0
for batch in range(steps_per_epoch_train):
src, trg = next(train_iter)
batch_size = src.shape[0]
batch_loss = train_step(src,
trg,
self.trg_lang,
self.encoder,
self.decoder,
self.optimizer,
batch_size,
type='train')
total_loss += batch_loss
if batch % 100 == 0:
print('Epoch {} Batch {} Loss {:.8f}'.format(
epoch + 1, batch, batch_loss.numpy()))
print('Epoch {} Mean Training Loss {:.8f}'.format(
epoch + 1, total_loss / self.num_train))
for batch in range(steps_per_epoch_valid):
src, trg = next(valid_iter)
batch_size = src.shape[0]
batch_loss = train_step(src,
trg,
self.trg_lang,
self.encoder,
self.decoder,
self.optimizer,
batch_size,
type='valid')
total_loss += batch_loss
validation_loss = total_loss / self.num_valid
if validation_loss < curr_best_validation_loss:
curr_best_validation_loss = validation_loss
self.checkpoint.save(file_prefix=cnt.MODEL_PATH)
print('Epoch {} Mean Validation Loss {:.8f}'.format(
epoch + 1, validation_loss))
def fit(self):
self.init_model()
callbacks = [
ModelCheckpoint(filepath=cnt.MODEL_PATH,
monitor='val_loss',
save_best_only=True),
]
self.model.fit_generator(
self.data_generator(self.num_train, 'train'),
callbacks=callbacks,
steps_per_epoch=shutils.get_num_batches(self.num_train,
cnt.BATCH_SIZE),
validation_data=self.data_generator(self.num_test, 'test'),
validation_steps=shutils.get_num_batches(self.num_test,
cnt.BATCH_SIZE),
epochs=cnt.NUM_EPOCHS,
verbose=1,
use_multiprocessing=True)
def scoring(self, type='pt', save_imgs=False, save_cams=False):
test_labels, pred_labels, total_batches = [], [], shutils.get_num_batches(
self.num_test, cnt.BATCH_SIZE)
if type == 'pt':
encoder = shutils.load_data_pkl(cnt.PT_ENCODER_PATH)
pred_out_dir = cnt.PT_PREDS_PATH
cam_dir = cnt.PT_CAMS_PATH
self.init_pt_model()
model = self.pt_model
model.load_weights(cnt.PT_MODEL_PATH)
else:
encoder = shutils.load_data_pkl(cnt.COLOR_ENCODER_PATH)
pred_out_dir = cnt.COLOR_PREDS_PATH
cam_dir = cnt.COLOR_CAMS_PATH
self.init_color_model()
model = self.color_model
model.load_weights(cnt.COLOR_MODEL_PATH)
num_batches, start = 0, 0
for batch_data, batch_labels in self.data_generator(
self.num_test, 'test', type):
test_labels += batch_labels.tolist()
predictions = self.predict(batch_data, type)
pred_labels += predictions
num_batches += 1
indices = [start + i for i in range(len(batch_labels))]
if save_imgs:
utils.save_imgs(batch_data, indices, np.array(batch_labels),
np.array(predictions), encoder, pred_out_dir)
if save_cams:
utils.cam(model, batch_data, indices, np.array(batch_labels),
np.array(predictions), encoder, cam_dir)
start += len(batch_labels)
if num_batches == total_batches:
break
h = np.sum(np.array(pred_labels), axis=1)
idx = np.nonzero(h > 0)[0]
t_labels = encoder.inverse_transform(np.array(test_labels)[idx])
p_labels = encoder.inverse_transform(np.array(pred_labels)[idx])
print(classification_report(t_labels, p_labels))
def scoring(self):
test_labels, pred_labels, total_batches = [], [], shutils.get_num_batches(
self.num_test, cnt.BATCH_SIZE)
num_batches = 0
for batch_data, batch_labels in self.data_generator(
self.num_test, 'test'):
test_labels += batch_labels.tolist()
pred_labels += self.predict(batch_data).tolist()
num_batches += 1
if num_batches == total_batches:
break
print(classification_report(test_labels, pred_labels))
def get_data_as_generator(num_data, prefix='train'):
random.seed(42)
word_vector_model = utils.get_vector_model(cnt.VECTOR_MODEL,
char_tokens=False)
char_vector_model = utils.get_vector_model(cnt.VECTOR_MODEL,
char_tokens=True)
data_pairs = shutils.load_data_pkl(
os.path.join(cnt.PERSISTENCE_PATH, prefix + "_data_pairs.pkl"))
random.shuffle(data_pairs)
num_batches = shutils.get_num_batches(num_data, cnt.BATCH_SIZE)
batch_num = 0
while True:
m = batch_num % num_batches
start, end = m * cnt.BATCH_SIZE, min((m + 1) * cnt.BATCH_SIZE,
num_data)
word_tokens1, word_tokens2, char_tokens1, char_tokens2, labels = zip(
*data_pairs[start:end])
labels = np.array(labels)
labels = np.expand_dims(labels, -1)
word_data_1 = shutils.get_vectors(word_vector_model, word_tokens1,
cnt.WORD_VECTOR_DIM)
word_data_2 = shutils.get_vectors(word_vector_model, word_tokens2,
cnt.WORD_VECTOR_DIM)
char_data_1 = np.array([
shutils.get_vectors(char_vector_model, x, cnt.CHAR_VECTOR_DIM)
for x in char_tokens1
])
char_data_2 = np.array([
shutils.get_vectors(char_vector_model, x, cnt.CHAR_VECTOR_DIM)
for x in char_tokens2
])
batch_num += 1
yield [word_data_1, word_data_2, char_data_1, char_data_2], labels
def scoring(self):
test_labels, pred_labels, total_batches = [], [], shutils.get_num_batches(
self.num_test, cnt.BATCH_SIZE)
encoder = shutils.load_data_pkl(cnt.ENCODER_PATH)
num_batches = 0
for batch_data, batch_labels in self.data_generator(
self.num_test, 'test'):
test_labels += batch_labels.tolist()
predictions = self.predict(batch_data)
pred_labels += predictions
num_batches += 1
if num_batches == total_batches:
break
t_labels = encoder.inverse_transform(np.array(test_labels))
p_labels = encoder.inverse_transform(np.array(pred_labels))
print(
classification_report(t_labels,
p_labels,
target_names=encoder.classes_))
def fit(self):
with self.sess.as_default():
optimizer, cost, accuracy = get_loss_accuracy(
self.input_img, self.input_txt, self.output, self.num_classes,
self.vocab_size, self.training)
train_summary_writer = tf.summary.FileWriter(
cnt.TF_TRAIN_SUMMARY_PATH, self.sess.graph)
test_summary_writer = tf.summary.FileWriter(
cnt.TF_TEST_SUMMARY_PATH, self.sess.graph)
saver = tf.train.Saver()
self.sess.run(tf.global_variables_initializer())
steps_per_epoch_train = shutils.get_num_batches(
self.num_train, cnt.BATCH_SIZE)
steps_per_epoch_test = shutils.get_num_batches(
self.num_test, cnt.BATCH_SIZE)
min_test_loss = float("Inf")
for i in range(cnt.NUM_EPOCHS):
train_iter = self.data_generator(self.num_train, 'train')
test_iter = self.data_generator(self.num_test, 'test')
train_c_loss, train_c_acc, self.sess = process_batches(
steps_per_epoch_train,
train_iter,
self.input_img,
self.input_txt,
self.output,
self.training,
optimizer,
cost,
accuracy,
self.sess,
mode='train')
train_summary = tf.Summary()
train_summary.value.add(tag="Accuracy",
simple_value=train_c_acc)
train_summary.value.add(tag="Loss", simple_value=train_c_loss)
train_summary_writer.add_summary(train_summary, i)
test_c_loss, test_c_acc, self.sess = process_batches(
steps_per_epoch_test,
test_iter,
self.input_img,
self.input_txt,
self.output,
self.training,
optimizer,
cost,
accuracy,
self.sess,
mode='test')
test_summary = tf.Summary()
test_summary.value.add(tag="Accuracy", simple_value=test_c_acc)
test_summary.value.add(tag="Loss", simple_value=test_c_loss)
test_summary_writer.add_summary(test_summary, i)
if cnt.SAVE_BEST_LOSS_MODEL:
if test_c_loss < min_test_loss:
min_test_loss = test_c_loss
saver.save(self.sess, cnt.MODEL_PATH)
else:
saver.save(self.sess, cnt.MODEL_PATH)
print("Iter " + str(i) + ", Training Loss= " +
"{:.6f}".format(train_c_loss) + ", Training Accuracy= " +
"{:.5f}".format(train_c_acc))
print("Iter " + str(i) + ", Validation Loss= " +
"{:.6f}".format(test_c_loss) +
", Validation Accuracy= " + "{:.5f}".format(test_c_acc))
print()
train_summary_writer.close()
test_summary_writer.close()