[已解決]Day 41的sample code 中出現 ValueError,想請問原因跟解決方式?
2020/11/06 下午 11:01
電腦視覺深度學習討論版
林柏宇
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在執行Day 41的sample code時,於下列程式碼第52行也就是 model.fit_generator 的 call_back 部份出現
>ValueError: Tensor conversion requested dtype float32_ref for Tensor with dtype float32:
的錯誤提醒,想請問為什麼及如何處理
```python
annotation_path = '2007_train.txt' # 轉換好格式的標註檔案
log_dir = 'logs/000/' # 訓練好的模型儲存的路徑
classes_path = 'model_data/voc_classes.txt'
anchors_path = 'model_data/yolo_anchors.txt'
class_names = get_classes(classes_path)
num_classes = len(class_names)
anchors = get_anchors(anchors_path)
input_shape = (416,416) # multiple of 32, hw
is_tiny_version = len(anchors)==6 # default setting
if is_tiny_version:
model = create_tiny_model(input_shape, anchors, num_classes,
freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5')
else:
model = create_model(input_shape, anchors, num_classes, freeze_body=2, weights_path='model_data/yolo_weights.h5')
# make sure you know what you freeze
logging = TensorBoard(log_dir=log_dir)
checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)
# 分為 training 以及 validation
val_split = 0.1
with open(annotation_path) as f:
lines = f.readlines()
np.random.seed(10101)
np.random.shuffle(lines)
np.random.seed(None)
num_val = int(len(lines)*val_split)
num_train = len(lines) - num_val
# Train with frozen layers first, to get a stable loss.
# Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
# 一開始先 freeze YOLO 除了 output layer 以外的 darknet53 backbone 來 train
if True:
model.compile(optimizer=Adam(lr=1e-3), loss={
# use custom yolo_loss Lambda layer.
'yolo_loss': lambda y_true, y_pred: y_pred})
batch_size = 16
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
# 模型利用 generator 產生的資料做訓練,強烈建議大家去閱讀及理解 data_generator_wrapper 在 train.py 中的實現
model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
steps_per_epoch=max(1, num_train//batch_size),
validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
validation_steps=max(1, num_val//batch_size),
epochs=50,
initial_epoch=0,
callbacks=[logging, checkpoint])
model.save_weights(log_dir + 'trained_weights_stage_1.h5')
# Unfreeze and continue training, to fine-tune.
# Train longer if the result is not good.
if True:
# 把所有 layer 都改為 trainable
for i in range(len(model.layers)):
model.layers[i].trainable = True
model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change
print('Unfreeze all of the layers.')
batch_size = 16 # note that more GPU memory is required after unfreezing the body
print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))
model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),
steps_per_epoch=max(1, num_train//batch_size),
validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),
validation_steps=max(1, num_val//batch_size),
epochs=100,
initial_epoch=50,
callbacks=[logging, checkpoint, reduce_lr, early_stopping])
model.save_weights(log_dir + 'trained_weights_final.h5')
```
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2020/11/09 上午 00:32
張維元 (WeiYuan)
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