我有一个用于图像分割的 Unet 神经网络。有时它训练成功，如图所示 training 1
但有时它会像这样崩溃
我添加了学习率的衰减，但训练不稳定，有些运行收敛，有些则不
可能是什么原因？我怎样才能使训练更加一致？
我在具有相同种子的 tensorflow keras 中使用 unet 来比较结果。
def unet_no_dropout(pretrained_weights=None, input_size=(512, 512, 1), act = 'elu'):
inputs = tf.keras.layers.Input(input_size)
conv1 = tf.keras.layers.Conv2D(64, 3 , activation=act,padding='same', kernel_initializer='he_normal')(inputs)
#conv1 = tf.keras.layers.Activation(swish)(conv1)
conv1 = tf.keras.layers.Conv2D(64, 3, activation=act, padding='same', kernel_initializer='he_normal')(conv1)
pool1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = tf.keras.layers.Conv2D(128, 3, activation=act, padding='same', kernel_initializer='he_normal')(pool1)
conv2 = tf.keras.layers.Conv2D(128, 3, activation=act, padding='same', kernel_initializer='he_normal')(conv2)
pool2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = tf.keras.layers.Conv2D(256, 3, activation=act, padding='same', kernel_initializer='he_normal')(pool2)
conv3 = tf.keras.layers.Conv2D(256, 3, activation=act, padding='same', kernel_initializer='he_normal')(conv3)
pool3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = tf.keras.layers.Conv2D(512, 3, activation=act, padding='same', kernel_initializer='he_normal')(pool3)
conv4 = tf.keras.layers.Conv2D(512, 3, activation=act, padding='same', kernel_initializer='he_normal')(conv4)
#drop4 = tf.keras.layers.Dropout(0.5)(conv4)
pool4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = tf.keras.layers.Conv2D(1024, 3, activation=act, padding='same', kernel_initializer='he_normal')(pool4)
conv5 = tf.keras.layers.Conv2D(1024, 3, activation=act, padding='same', kernel_initializer='he_normal')(conv5)
#drop5 = tf.keras.layers.Dropout(0.5)(conv5)
up6 = tf.keras.layers.Conv2D(512, 2, activation=act, padding='same', kernel_initializer='he_normal')(
tf.keras.layers.UpSampling2D(size=(2, 2))(conv5))
merge6 = tf.keras.layers.concatenate([conv4, up6], axis=3)
#merge6 = tf.keras.layers.concatenate([conv4, up6], axis=3)
conv6 = tf.keras.layers.Conv2D(512, 3, activation=act, padding='same', kernel_initializer='he_normal')(merge6)
conv6 = tf.keras.layers.Conv2D(512, 3, activation=act, padding='same', kernel_initializer='he_normal')(conv6)
up7 = tf.keras.layers.Conv2D(256, 2, activation=act, padding='same', kernel_initializer='he_normal')(
tf.keras.layers.UpSampling2D(size=(2, 2))(conv6))
merge7 = tf.keras.layers.concatenate([conv3, up7], axis=3)
conv7 = tf.keras.layers.Conv2D(256, 3, activation=act, padding='same', kernel_initializer='he_normal')(merge7)
conv7 = tf.keras.layers.Conv2D(256, 3, activation=act, padding='same', kernel_initializer='he_normal')(conv7)
up8 = tf.keras.layers.Conv2D(128, 2, activation=act, padding='same', kernel_initializer='he_normal')(
tf.keras.layers.UpSampling2D(size=(2, 2))(conv7))
merge8 = tf.keras.layers.concatenate([conv2, up8], axis=3)
conv8 = tf.keras.layers.Conv2D(128, 3, activation=act, padding='same', kernel_initializer='he_normal')(merge8)
conv8 = tf.keras.layers.Conv2D(128, 3, activation=act, padding='same', kernel_initializer='he_normal')(conv8)
up9 = tf.keras.layers.Conv2D(64, 2, activation=act, padding='same', kernel_initializer='he_normal')(
tf.keras.layers.UpSampling2D(size=(2, 2))(conv8))
merge9 = tf.keras.layers.concatenate([conv1, up9], axis=3)
conv9 = tf.keras.layers.Conv2D(64, 3, activation=act, padding='same', kernel_initializer='he_normal')(merge9)
conv9 = tf.keras.layers.Conv2D(64, 3, activation=act, padding='same', kernel_initializer='he_normal')(conv9)
conv9 = tf.keras.layers.Conv2D(2, 3, activation=act, padding='same', kernel_initializer='he_normal')(conv9)
conv10 = tf.keras.layers.Conv2D(1, 1, activation='sigmoid')(conv9)
model = tf.keras.Model(inputs=inputs, outputs=conv10)
#model.compile(optimizer = tf.keras.optimizers.Adam(lr = 2e-4), loss = 'binary_crossentropy', metrics = [tf.keras.metrics.Accuracy()])
#model.compile(optimizer=tf.keras.optimizers.Adam(lr = 5e-6), loss=combo_loss(alpha=1, beta=0.4), metrics=[dice_accuracy])
model.compile(optimizer=tf.keras.optimizers.Adam(lr_schedule), loss = 'binary_crossentropy',metrics=[dice_accuracy])
#model.compile(optimizer=RMSprop(lr=0.00001), loss=combo_loss, metrics=[dice_accuracy])
if (pretrained_weights):
model.load_weights(pretrained_weights)
return model
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate=5e-5,
decay_steps=2720,
decay_rate=0.9)