如何在使用自定义学习率时解决"运行时错误：尝试在 EAGER 模式下评估"？

Question

我正在使用自定义学习率调度程序，遇到错误RuntimeError:尝试在EAGER模式下评估时出错。

我制作了一个计算历元学习率的函数，并使用TensorFlow给出的LearningRateScheduler((来实现该模型。

以下是我针对上述问题的代码：

模型架构：

model_learning_rate = models.Sequential()
model_learning_rate.add(Conv2D(16,kernel_size=5,activation='relu',input_shape=(64,64,1)))
model_learning_rate.add(MaxPool2D())
model_learning_rate.add(Dropout(0.4))
model_learning_rate.add(Conv2D(32,kernel_size=5,activation='relu'))
model_learning_rate.add(MaxPool2D())
model_learning_rate.add(Dropout(0.4))
model_learning_rate.add(Conv2D(64,kernel_size=5,activation='relu'))
model_learning_rate.add(MaxPool2D())
model_learning_rate.add(Dropout(0.4))
model_learning_rate.add(Flatten())
model_learning_rate.add(Dense(256, activation='relu'))
model_learning_rate.add(Dense(62, activation='softmax'))
model_learning_rate.compile(loss='categorical_crossentropy',
optimizer=tf.keras.optimizers.Adam(lr=1e-3),
metrics=['accuracy'])

自定义学习率函数：

def lr_schedule(epoch, lr):
"""Learning Rate Schedule
# Arguments
epoch (int): The number of epochs
# Returns
lr (float32): learning rate
"""
global K1, K2
Kz = 0.  # max penultimate activation
S = 0.

sess = tf.compat.v1.keras.backend.get_session()
max_wt = 0.
for weight in model_learning_rate.weights:
norm = np.linalg.norm(weight.eval(sess))
if norm > max_wt:
max_wt = norm

for i in range((len(X_train) - 1) // batch_size + 1):
start_i = i * batch_size
end_i = start_i + batch_size
xb = X_train[start_i:end_i]

tmp = np.array(func([xb]))
activ = np.linalg.norm(tmp)
sq = np.linalg.norm(np.square(tmp))
if sq > S:
S = sq

if activ > Kz:
Kz = activ
K_ = ((num_classes - 1) * Kz) / (num_classes * batch_size) #+ 1e-4 * max_wt
S_ = (num_classes - 1) ** 2 / (num_classes * batch_size) ** 2 * S #+ 2e-4 * max_wt * ((num_classes - 1) * Kz) / (num_classes * batch_size)

K1 = beta1 * K1 + (1 - beta1) * K_
K2 = beta2 * K2 + (1 - beta2) * S_
lr = (np.sqrt(K2) + K.epsilon()) / K1
print('S =', S, ', K1 =', K1,', K2 =', K2, ', K_ =', K_, ', lr =', lr)
lrs.append(lr)
print('Epoch', epoch, 'LR =', lr)
return lr

使用学习率：

lr_scheduler = LearningRateScheduler(lr_schedule)
history_learning_rate = model_learning_rate.fit(datagen.flow(X_train, y_train, batch_size=8), epochs = 30, steps_per_epoch=len(X_train) / 8, validation_data = (X_test,y_test), callbacks=[lr_scheduler])

Answer 1

在导入tensorflow:后，尝试用这个来抑制它

tf.compat.v1.disable_v2_behavior()