问题描述
我有一个非常深的模型：
def get_model2(mask_kind):
decay = 0.0
inp_1 = keras.Input(shape=(64,101,1),name="RST_inputs")
x = layers.Conv2D(256,kernel_size=(3,3),kernel_regularizer=l2(1e-6),strides=(3,padding="same")(inp_1)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Conv2D(128,padding="same")(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Conv2D(64,kernel_size=(2,2),strides=(2,padding="same")(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Conv2D(32,padding="same")(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Flatten()(x)
x = layers.Dense(512)(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Dense(256)(x)
x = layers.LeakyReLU(alpha=0.3)(x)
out1 = layers.Dense(128,name="ls_weights")(x)
if mask_kind == 1: # APPLICA LA PRIMA MASCHERA
binary_mask = layers.Lambda(mask_layer1,name="lambda_layer1",dtype='float64')(out1)
print('shape',binary_mask.shape[0])
elif mask_kind == 2: # APPLICA LA SECONDA MASCHERA
binary_mask = layers.Lambda(mask_layer2,name="lambda_layer2",dtype='float64')(out1)
else: # NON APPLICA NULLA
binary_mask = out1
x = layers.Dense(256)(binary_mask)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Dense(512)(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Dense(192)(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Reshape((2,2,48))(x)
x = layers.Conv2DTranspose(32,padding="same")(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Conv2DTranspose(64,padding="same")(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Conv2DTranspose(128,padding="same")(x)
x = layers.LeakyReLU(alpha=0.3)(x)
x = layers.Conv2DTranspose(256,strides=(5,5),padding="same")(x)
x = layers.LeakyReLU(alpha=0.3)(x)
soundfield_layer = layers.Conv2DTranspose(1,kernel_size=(1,strides=(1,padding='same')(x)
# soundfield_layer = layers.Dense(40000,name="sf_vec")(x)
if mask_kind == 1:
model = keras.Model(inp_1,[binary_mask,soundfield_layer],name="2_out_model")
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.1,decay=decay),# in caso
# rimettere 0.001
loss=["mse","mse"],loss_weights=[1,1])
# plot_model(model,to_file='model.png',show_shapes=True,show_layer_names=True)
model.summary()
else:
model = keras.Model(inp_1,loss_weights=[0,show_layer_names=True)
model.summary()
return model
并且我正在尝试使用学习率阶跃衰减来查看是否可以在训练期间改进我的验证损失函数。我正在为调度程序定义类，如下所示：
class StepDecay:
def __init__(self,initAlpha=0.1,factor=0.25,dropEvery=30):
# store the base initial learning rate,drop factor,and
# epochs to drop every
self.initAlpha = initAlpha
self.factor = factor
self.dropEvery = dropEvery
def __call__(self,epoch):
# compute the learning rate for the current epoch
exp = np.floor((1 + epoch) / self.dropEvery)
alpha = self.initAlpha * (self.factor ** exp)
# return the learning rate
return float(alpha)
然后我开始训练：
schedule = StepDecay(initAlpha=1e-1,dropEvery=30)
es = tf.keras.callbacks.EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=50)
callbacks = [es,LearningRateScheduler(schedule)]
model = get_model2(mask_kind=1)
history = model.fit(X_train,[Y_train,Z_train],validation_data=(X_val,[Y_val,Z_val]),epochs=300,batch_size=32,callbacks=callbacks,verbose=1)
test_loss,_,_ = model.evaluate(X_test,[Y_test,Z_test],verbose=1)
print('Test: %.3f' % test_loss)
但是当我训练时，我得到了“nan”损失：
25/25 [==============================] - 17s 684ms/step - loss: nan - lambda_layer1_loss: nan - conv2d_transpose_4_loss: nan - val_loss: nan - val_lambda_layer1_loss: nan etc....
我不明白为什么。问题可能是衰减率，它是 SGD 优化器中存在的一个参数，但文档中的参数对于 Adam 来说并不存在，但我没有得到任何错误，所以..有什么想法吗？
解决方法
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