def predict(model: Model, dataset: DatasetBase, restore_path: Path):
restore_path = restore_path.expanduser().absolute()
model.build_graph()
saver = tf.train.Saver(save_relative_paths=True)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, str(restore_path))
while True:
sess.run(dataset.test_init_op)
try:
pred, x, y = sess.run([model.predict(), dataset.x, dataset.y],
feed_dict={tf.keras.backend.learning_phase(): 0})
pred = np.argmax(pred)
print(pred)
io.imshow(x[0, :, :, 0], cmap="gray")
io.show()
except tf.errors.OutOfRangeError:
continue
def fit(model: Model, dataset: DatasetBase, save_path: Path, save_interval_minute: int = 15, epochs: int = 1):
save_path = save_path.expanduser().absolute()
model.build_graph()
train = True
test = not train
switch = True
sum_loss = 0
sum_acc = 0
saver = tf.train.Saver(save_relative_paths=True)
epoch = -1
now = datetime.now().minute
logging.info(f"Number of trainable parameters: {get_num_of_parameters()}")
with tf.Session() as sess, tqdm() as progress:
sess.run(tf.global_variables_initializer())
sum_writer = tf.summary.FileWriter(save_path / str(model) / "logdir", sess.graph)
while epoch <= epochs:
# Switch to test or train dataset
if switch:
switch = False
if train:
sess.run(dataset.train_init_op)
progress.total = dataset.train_size
epoch += 1
elif test:
sess.run(dataset.test_init_op)
progress.total = dataset.test_size
try:
phase = 'train' if train else 'test'
loss = 0
acc = 0
if phase == 'train':
loss, acc, _, = sess.run([model.loss(), model.accuracy(), model.optimize()],
feed_dict={tf.keras.backend.learning_phase(): 1})
elif phase == 'test':
loss, acc = sess.run([model.loss(), model.accuracy()],
feed_dict={tf.keras.backend.learning_phase(): 0})
sum_loss += loss
sum_acc += acc
batches = (progress.n / dataset.batch_size + 1)
desc = f"Epoch: {epoch:<5}| Phase: {phase :<10}| " \
f"loss: {sum_loss / batches :<25}| " \
f"acc: {sum_acc / batches :<25}| "
progress.set_description(desc=desc)
progress.update(dataset.batch_size)
except tf.errors.OutOfRangeError:
progress.write("")
train = not train
test = not test
switch = True
progress.n = 0
sum_loss = 0
sum_acc = 0
if now - datetime.now().minute >= save_interval_minute:
now = datetime.now().minute
saver.save(sess, str(save_path / str(model) / str(model)))
sum_writer.flush()
sum_writer.close()
sum_writer.reopen()
continue
sum_writer.flush()
sum_writer.close()
saver.save(sess, str(save_path / str(model) / str(model)))