ValueError：没有为任何变量提供渐变：['conv2d / kernel：0'，'conv2d / bias：0'，'conv2d_1 / kernel：0'，'conv2d_1 / bias：0'，

Question

系统信息Colab tensorflow 2.2.0

描述当前行为：当我尝试解决自己的数据问题（即多个标签语义细分）时，我遇到了此错误。

下面是代码

import tensorflow as tf
import tensorflow.keras.backend as K

IMG_WIDTH = 512
IMG_HEIGHT = 512
IMG_CHANNELS = 3

# batch_shape=(512,512,3)
# inputs = Input(batch_shape=(4, 512, 512, 3))
#Build the model
inputs = tf.keras.layers.Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))
#s = tf.keras.layers.Lambda(lambda x: x / 255)(inputs)

#Contraction path
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(inputs)
c1 = tf.keras.layers.Dropout(0.1)(c1)
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c1)
p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)

c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1)
c2 = tf.keras.layers.Dropout(0.1)(c2)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2)
p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)

c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2)
c3 = tf.keras.layers.Dropout(0.2)(c3)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3)
p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)

c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3)
c4 = tf.keras.layers.Dropout(0.2)(c4)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4)
p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)

c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4)
c5 = tf.keras.layers.Dropout(0.3)(c5)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5)

#Expansive path 
u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)
u6 = tf.keras.layers.concatenate([u6, c4])
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6)
c6 = tf.keras.layers.Dropout(0.2)(c6)
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)

u7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)
u7 = tf.keras.layers.concatenate([u7, c3])
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7)
c7 = tf.keras.layers.Dropout(0.2)(c7)
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)

u8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)
u8 = tf.keras.layers.concatenate([u8, c2])
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8)
c8 = tf.keras.layers.Dropout(0.1)(c8)
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8)

u9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)
u9 = tf.keras.layers.concatenate([u9, c1], axis=3)
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)
c9 = tf.keras.layers.Dropout(0.1)(c9)
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9)

outputs = tf.keras.layers.Conv2D(6, (1, 1), activation='softmax')(c9)

model = tf.keras.Model(inputs=[inputs], outputs=[outputs])

# define optomizer
optim = tf.keras.optimizers.Adam()

def dice_coef(y_true, y_pred):
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return (2. * intersection + smooth) / (K.sum(y_true_f*y_true_f) + K.sum(y_pred_f*y_pred_f) + smooth)


def dice_coef_loss(y_true, y_pred):
    return 1.-dice_coef(y_true, y_pred)

smooth = 1.
loss= tf.keras.losses.CategoricalCrossentropy()

model.compile(optim, loss, metrics=[dice_coef,'accuracy'])

#model.compile(optim, metrics, loss)
model.summary()

#SET UP FOR DATA TRAINING

BATCH_SIZE = 4
CLASSES = ['0', '1','2','3','4','5']
LR = 0.0001
EPOCHS = 40
n_classes = len(CLASSES)

# Dataset for train images
train_dataset = Dataset(
    x_train_dir, 
    y_train_dir, 
    classes=CLASSES, 
    augmentation=get_training_augmentation(),
    preprocessing=get_preprocessing(),
    with_shape_assert= True,
)

# Dataset for validation images
valid_dataset = Dataset(
    x_valid_dir, 
    y_valid_dir, 
    classes=CLASSES, 
    augmentation=get_validation_augmentation(),
    preprocessing=get_preprocessing(),
    with_shape_assert= True,
)

train_dataloader = Dataloader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
valid_dataloader = Dataloader(valid_dataset, batch_size=4, shuffle=False)

# check shapes for errors
assert train_dataloader[0][0].shape == (BATCH_SIZE, 512, 512, 3)
assert train_dataloader[0][1].shape == (BATCH_SIZE, 512, 512, n_classes)

# define callbacks for learning rate scheduling and best checkpoints saving
callbacks = [
    tf.keras.callbacks.ModelCheckpoint('./best_model.h5', save_weights_only=False, save_best_only=True, mode='min'),
    tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)
]
results_2704 = model.fit(
    train_dataloader, 
    steps_per_epoch=len(train_dataloader), 
    epochs=EPOCHS, 
    validation_data=valid_dataloader, 
    callbacks=callbacks, 
    validation_steps=len(valid_dataloader),verbose=1
)

这将给出错误：

ValueError: No gradients provided for any variable: ['conv2d/kernel:0', 'conv2d/bias:0', 'conv2d_1/kernel:0', 'conv2d_1/bias:0', 'conv2d_2/kernel:0', 'conv2d_2/bias:0', 'conv2d_3/kernel:0', 'conv2d_3/bias:0', 'conv2d_4/kernel:0', 'conv2d_4/bias:0', 'conv2d_5/kernel:0', 'conv2d_5/bias:0', 'conv2d_6/kernel:0', 'conv2d_6/bias:0', 'conv2d_7/kernel:0', 'conv2d_7/bias:0', 'conv2d_8/kernel:0', 'conv2d_8/bias:0', 'conv2d_9/kernel:0', 'conv2d_9/bias:0', 'conv2d_transpose/kernel:0', 'conv2d_transpose/bias:0', 'conv2d_10/kernel:0', 'conv2d_10/bias:0', 'conv2d_11/kernel:0', 'conv2d_11/bias:0', 'conv2d_transpose_1/kernel:0', 'conv2d_transpose_1/bias:0', 'conv2d_12/kernel:0', 'conv2d_12/bias:0', 'conv2d_13/kernel:0', 'conv2d_13/bias:0', 'conv2d_transpose_2/kernel:0', 'conv2d_transpose_2/bias:0', 'conv2d_14/kernel:0', 'conv2d_14/bias:0', 'conv2d_15/kernel:0', 'conv2d_15/bias:0', 'conv2d_transpose_3/kernel:0', 'conv2d_transpose_3/bias:0', 'conv2d_16/kernel:0', 'conv2d_16/bias:0', 'conv2d_17/kernel:0', 'conv2d_17/bias:0', 'conv2d_18/kernel:0', 'conv2d_18/bias:0'].

我知道这可能是由于死梯度造成的，我一直试图解决这个问题，同时也在Tensorflow github上发布了一个月，但直到现在我仍然不知道解决方案。因此，我在这里发布以寻求其他Tensorflow专家的帮助，他们可能会在等待Tensorflow支持人员进行更新时给我一些提示。我四处搜寻，我知道使用tf.GradientTape()可能有助于解决问题，但我自己仍然找不到正确的方法。

非常期待任何建议。非常感谢]]

系统信息Colab tensorflow 2.2.0描述当前行为：当我尝试解决自己的数据问题（即多个标签语义细分）时，我遇到了此错误。以下是...

Answer 1

仅传递训练数据但未通过model.fit()中的标签时，会出现此错误。我可以使用以下代码重新创建您的错误。您可以从here下载程序中正在使用的数据集。