python - 分割模型生成 NaN 作为输出
问题描述
我正在尝试使用 u-net 在 kits19 数据集上执行肾脏和肾脏肿瘤分割。我正在加载每个案例（3D 数据），对它们进行 MinMaxScaling，然后生成 8 个批次，与验证数据相同。除了缩放之外没有任何预处理。然后我在每个卷积层之后通过批量标准化将数据提供给 u-net。并在此代码中执行多个时代：
NAME = "unet_without_edge_detection_with_batch_norm_{}".format(int(time.time()))
callbacks = [
tf.keras.callbacks.TensorBoard(log_dir='logs/{}'.format(NAME)),
tf.keras.callbacks.ModelCheckpoint(filepath='saved_models/checkpoints/{}'.format(NAME),
save_weights_only=False, monitor='val_acc', mode='max', save_best_only=False)
]
# '------lists data points for train and validation'
# deleting broken cases
case_numbers = np.delete(np.arange(0, 209, 1), [158, 159, 170, 202])
case_numbers_val = case_numbers[179:]
case_numbers_train = case_numbers[:179]
batch_size = 8
data_size = 38650
val_data_size = 4520
results = model.fit(
trainGenerator(case_numbers_train, batch_size),
steps_per_epoch = int(np.floor( data_size / (batch_size) )),
epochs = 16,
callbacks = callbacks,
verbose = 1,
validation_data = validationGenerator(case_numbers_val, batch_size),
validation_steps = int(np.floor( val_data_size / (batch_size) ))
)
model.save('saved_models\model_from_{}'.format(time.time()))
在训练过程中，损失突然下降到 1.1921e-7 并保持这种状态，而准确度在 0.99 左右波动（我想这是可以的，因为大多数不包含任何肾脏）。当我尝试使用该模型预测某些内容时，它会输出 NaN 数组。
这是网络的摘要（我在所有层中使用 relu，在输出中使用 sigmoid）：
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 512, 512, 1) 0
__________________________________________________________________________________________________
lambda (Lambda) (None, 512, 512, 1) 0 input_1[0][0]
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 512, 512, 16) 160 lambda[0][0]
__________________________________________________________________________________________________
batch_normalization (BatchNorma (None, 512, 512, 16) 64 conv2d[0][0]
__________________________________________________________________________________________________
dropout (Dropout) (None, 512, 512, 16) 0 batch_normalization[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 512, 512, 16) 2320 dropout[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 512, 512, 16) 64 conv2d_1[0][0]
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 256, 256, 16) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 256, 256, 32) 4640 max_pooling2d[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 256, 256, 32) 128 conv2d_2[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 256, 256, 32) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 256, 256, 32) 9248 dropout_1[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 256, 256, 32) 128 conv2d_3[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 128, 128, 32) 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 128, 128, 64) 18496 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 128, 128, 64) 256 conv2d_4[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 128, 128, 64) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 128, 128, 64) 36928 dropout_2[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 128, 128, 64) 256 conv2d_5[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 64, 64, 64) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 64, 64, 128) 73856 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 64, 64, 128) 512 conv2d_6[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 64, 64, 128) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 64, 64, 128) 147584 dropout_3[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 64, 64, 128) 512 conv2d_7[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 32, 32, 128) 0 batch_normalization_7[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 32, 32, 256) 295168 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 32, 32, 256) 1024 conv2d_8[0][0]
__________________________________________________________________________________________________
dropout_4 (Dropout) (None, 32, 32, 256) 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 32, 32, 256) 590080 dropout_4[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 32, 32, 256) 1024 conv2d_9[0][0]
__________________________________________________________________________________________________
conv2d_transpose (Conv2DTranspo (None, 64, 64, 128) 131200 batch_normalization_9[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 64, 64, 256) 0 conv2d_transpose[0][0]
batch_normalization_7[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 64, 64, 128) 295040 concatenate[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 64, 64, 128) 512 conv2d_10[0][0]
__________________________________________________________________________________________________
dropout_5 (Dropout) (None, 64, 64, 128) 0 batch_normalization_10[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 64, 64, 128) 147584 dropout_5[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 64, 64, 128) 512 conv2d_11[0][0]
__________________________________________________________________________________________________
conv2d_transpose_1 (Conv2DTrans (None, 128, 128, 64) 32832 batch_normalization_11[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 128, 128, 128 0 conv2d_transpose_1[0][0]
batch_normalization_5[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 128, 128, 64) 73792 concatenate_1[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 128, 128, 64) 256 conv2d_12[0][0]
__________________________________________________________________________________________________
dropout_6 (Dropout) (None, 128, 128, 64) 0 batch_normalization_12[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 128, 128, 64) 36928 dropout_6[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 128, 128, 64) 256 conv2d_13[0][0]
__________________________________________________________________________________________________
conv2d_transpose_2 (Conv2DTrans (None, 256, 256, 32) 8224 batch_normalization_13[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 256, 256, 64) 0 conv2d_transpose_2[0][0]
batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 256, 256, 32) 18464 concatenate_2[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 256, 256, 32) 128 conv2d_14[0][0]
__________________________________________________________________________________________________
dropout_7 (Dropout) (None, 256, 256, 32) 0 batch_normalization_14[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 256, 256, 32) 9248 dropout_7[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 256, 256, 32) 128 conv2d_15[0][0]
__________________________________________________________________________________________________
conv2d_transpose_3 (Conv2DTrans (None, 512, 512, 16) 2064 batch_normalization_15[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 512, 512, 32) 0 conv2d_transpose_3[0][0]
batch_normalization_1[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 512, 512, 16) 4624 concatenate_3[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 512, 512, 16) 64 conv2d_16[0][0]
__________________________________________________________________________________________________
dropout_8 (Dropout) (None, 512, 512, 16) 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 512, 512, 16) 2320 dropout_8[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 512, 512, 16) 64 conv2d_17[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 512, 512, 3) 51 batch_normalization_17[0][0]
==================================================================================================
Total params: 1,946,739
Trainable params: 1,943,795
Non-trainable params: 2,944
def soft_dice_loss(y_true, y_pred, epsilon=1e-6):
axes = tuple(range(1, len(y_pred.shape)-1))
numerator = 2. * np.sum(y_pred * y_true, axes)
denominator = np.sum(np.square(y_pred) + np.square(y_true), axes)
return 1 - np.mean(numerator / (denominator + epsilon))
optimizer = tf.keras.optimizers.Adam()
model.compile(optimizer=optimizer, loss =['categorical_crossentropy', soft_dice_loss], metrics=['accuracy'])
这是生成器：
def trainGenerator(case_nums, batch_size):
#takes data from begining of data set to
while True:
for case_num in case_nums:
volume, segmentation = load_case(case_num)
#preprocessing input
X_file = preprocess_X(volume)
y_file = preprocess_y(segmentation)
L = X_file.shape[0]
batch_start = 0
batch_end = batch_size
while batch_start < L:
limit = min(batch_end, L)
X = X_file[batch_start:limit, :, :, :]
y = y_file[batch_start:limit, :, :, :]
yield (X.astype(np.float32), y.astype(np.float32))
batch_start += batch_size
batch_end += batch_size
if case_num == case_nums[-1]:
# breaks loop so it starts again infinietly
break
我尝试将输出层的激活函数更改为 softmax 和 tanh，优化器并尝试不使用 bash 标准化，但它没有改变任何东西。由于数据中没有 nan，可能是什么问题？我使用 TensorFlow 2.2.0
解决方案
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