使用@tf.function 时，在 Tensorflow 2.0 中训练从相同 class 定义的多个模型失败
Training multiple models defined from the same class in Tensorflow 2.0 fails when using @tf.function
我正在使用 Tensorflow 2.1 创建自定义模型和自定义训练循环。我的目标是比较神经网络不同配置的准确性。具体来说，在这种情况下，我正在比较具有不同潜在维度的自动编码器的重建误差。因此，我正在为一个潜在维度训练我的网络，然后计算测试误差，然后我为另一个潜在维度重做这个过程，依此类推。通过这个过程，我想创建这样的图：
绘图示例：
为了加快训练速度，我想在训练循环的反向传播部分使用 @tf.function 装饰器。但是，当我尝试训练多个不同的网络时，循环遍历潜在维度时出现错误。见下文：
ValueError: in converted code:
<ipython-input-19-78bafad21717>:41 grad *
loss_value = tf.losses.mean_squared_error(inputs, model(inputs))
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py:778 __call__
outputs = call_fn(cast_inputs, *args, **kwargs)
<ipython-input-19-78bafad21717>:33 call *
x_enc = self.encoder(inp)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py:778 __call__
outputs = call_fn(cast_inputs, *args, **kwargs)
<ipython-input-19-78bafad21717>:9 call *
x = self.dense1(inp)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py:748 __call__
self._maybe_build(inputs)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py:2116 _maybe_build
self.build(input_shapes)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/layers/core.py:1113 build
trainable=True)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py:446 add_weight
caching_device=caching_device)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/training/tracking/base.py:744 _add_variable_with_custom_getter
**kwargs_for_getter)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer_utils.py:142 make_variable
shape=variable_shape if variable_shape else None)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/variables.py:258 __call__
return cls._variable_v1_call(*args, **kwargs)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/variables.py:219 _variable_v1_call
shape=shape)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/variables.py:65 getter
return captured_getter(captured_previous, **kwargs)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py:502 invalid_creator_scope
"tf.function-decorated function tried to create "
ValueError: tf.function-decorated function tried to create variables on non-first call.
当我删除@tf.function 装饰器时，我没有得到这个错误。我相信当我使用装饰器时它是否与 Tensorflow 创建计算图有关，并且当我创建我的网络的另一个实例时该图仍然存在。因此，由于旧图与网络的新实例不匹配而引发错误。但我对此完全不确定，因为我相信我在这里遗漏了一些关于 Tensorflow 的基本知识！
下面是我的代码重现错误的一个非常简单的版本。我试图删除代码中所有不必要的部分，以使其更易于阅读和调试。此外，为了这个问题，我正在生成一个非常简单的训练和测试集。
我已经尝试过 tf.keras.backend.clear_session() 函数但没有任何运气。
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# Encoder
class build_encoder(tf.keras.Model):
def __init__(self,latent_dim):
super(build_encoder, self).__init__()
self.dense1 = tf.keras.layers.Dense(32, activation='relu',use_bias=True)
self.dense2 = tf.keras.layers.Dense(latent_dim, activation='relu',use_bias=True)
def call(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
return x
# Decoder
class build_decoder(tf.keras.Model):
def __init__(self,):
super(build_decoder, self).__init__()
self.dense1 = tf.keras.layers.Dense(32, activation='relu',use_bias=True)
self.dense2 = tf.keras.layers.Dense(10, activation='relu',use_bias=True)
def call(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
return x
# Full Autoencoder
class Autoencoder(tf.keras.Model):
def __init__(self,latent_dim=5):
super(Autoencoder, self).__init__()
self.encoder = build_encoder(latent_dim)
self.decoder = build_decoder()
def call(self, inp):
x_enc = self.encoder(inp)
x_dec = self.decoder(x_enc)
return x_dec
#### Here is the backpropagation with @tf.function decorator ####
@tf.function
def grad(model, inputs):
with tf.GradientTape() as tape:
loss_value = tf.losses.mean_squared_error(inputs, model(inputs))
return loss_value, tape.gradient(loss_value, model.trainable_variables)
# Training loop function
def train(x_train, model, num_epochs, batch_size,optimizer):
train_loss = []
for epoch in range(num_epochs):
tf.random.shuffle(x_train)
for i in range(0, len(x_train), batch_size):
x_inp = x_train[i: i + batch_size]
loss_value, grads = grad(model, x_inp)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
train_loss.append(tf.reduce_mean(tf.losses.mean_squared_error(x_train, model(x_train))).numpy())
if epoch % 100 == 0:
print("Epoch: {}, Train loss: {:.9f}".format(epoch, train_loss[epoch]))
return train_loss
#### Generating simple training and test data
num_train = 10000
num_test = 1000
x_train = s = np.random.uniform(0,1,(num_train,10)).astype(np.float32)
x_train[:,6:10] = 0
x_test = s = np.random.uniform(0,1,(num_test,10)).astype(np.float32)
x_test[:,6:10] = 0
###
batch_size = 8
num_epochs = 10000
test_loss = []
# Looping over the latent dimensions
for latent_dim in range(1,10):
model = Autoencoder(latent_dim=3) # Creating an instance of my Autoencoder
optimizer = tf.keras.optimizers.Adam(learning_rate=0.00005) # Defining an optimizer
train_loss = train(x_train, model=model, num_epochs=num_epochs, batch_size=batch_size, optimizer=optimizer) # Training the network
test_loss.append(tf.reduce_mean(tf.losses.mean_squared_error(x_test, model(x_test))).numpy())
plt.figure()
plt.plot(test_loss,linewidth=1.5)
plt.grid(True)
plt.show()
您提供的代码片段有误。
我将最后一个密集层单元从 6 更改为 10。
# Decoder
class build_decoder(tf.keras.Model):
def __init__(self,):
super(build_decoder, self).__init__()
self.dense1 = tf.keras.layers.Dense(32, activation='relu',use_bias=True)
self.dense2 = tf.keras.layers.Dense(10, activation='relu',use_bias=True)
def call(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
return x
关于你关于训练多模型的问题。
报错信息"ValueError: tf.function-decorated function tried to create variables on non-first call"表示@tf.function修饰的函数正在创建一个新变量在其下一次迭代中，这是不允许的，因为此函数已转换为图表。
我修改了你的反向传播方法，我把你原来的代码注释掉了，观察区别。
#### Here is the backpropagation with @tf.function decorator ####
# @tf.function
# def grad(model, inputs):
# with tf.GradientTape() as tape:
# loss_value = tf.losses.mean_squared_error(inputs, model(inputs))
# return loss_value, tape.gradient(loss_value, model.trainable_variables)
@tf.function
def MSE(y_true, y_pred):
return tf.keras.losses.MSE(y_true, y_pred)
def backprop(inputs, model):
with tf.GradientTape() as tape:
loss_value = MSE(inputs, model(inputs))
return loss_value, tape.gradient(loss_value, model.trainable_variables)
def gradient_func(model, inputs):
return backprop(inputs, model)
原始代码的罪魁祸首是调用 model(inputs) 作为损失函数的输入，当您在函数中装饰 @tf.function它继承了里面所有的函数，这意味着损失函数被优化了。
还有一种无需重写单个变量即可训练多个模型的方法，就是将它们放入数组中。
model_array = [0]
# Looping over the latent dimensions
for latent_dim in range(1,10):
model_array.append(Autoencoder(latent_dim))
# Creating an instance of my Autoencoder
optimizer = tf.keras.optimizers.Adam(learning_rate=0.00005) # Defining an optimizer
train_loss = train(x_train, model=model_array[latent_dim], num_epochs=num_epochs, batch_size=batch_size, optimizer=optimizer) # Training the network
test_loss.append(tf.reduce_mean(tf.losses.mean_squared_error(x_test, model_array[latent_dim](x_test))).numpy())
这会将模型排列成数组，更容易访问和调试。
这里是完整的修改代码。
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# Encoder
class build_encoder(tf.keras.Model):
def __init__(self,latent_dim):
super(build_encoder, self).__init__()
self.dense1 = tf.keras.layers.Dense(32, activation='relu',use_bias=True)
self.dense2 = tf.keras.layers.Dense(latent_dim, activation='relu',use_bias=True)
def call(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
return x
# Decoder
class build_decoder(tf.keras.Model):
def __init__(self,):
super(build_decoder, self).__init__()
self.dense1 = tf.keras.layers.Dense(32, activation='relu',use_bias=True)
self.dense2 = tf.keras.layers.Dense(10, activation='relu',use_bias=True)
def call(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
return x
# Full Autoencoder
class Autoencoder(tf.keras.Model):
def __init__(self,latent_dim=5):
super(Autoencoder, self).__init__()
self.encoder = build_encoder(latent_dim)
self.decoder = build_decoder()
def call(self, inp):
x_enc = self.encoder(inp)
x_dec = self.decoder(x_enc)
return x_dec
#### Here is the backpropagation with @tf.function decorator ####
# @tf.function
# def grad(model, inputs):
# with tf.GradientTape() as tape:
# loss_value = tf.losses.mean_squared_error(inputs, model(inputs))
# return loss_value, tape.gradient(loss_value, model.trainable_variables)
@tf.function
def MSE(y_true, y_pred):
return tf.keras.losses.MSE(y_true, y_pred)
def backprop(inputs, model):
with tf.GradientTape() as tape:
loss_value = MSE(inputs, model(inputs))
return loss_value, tape.gradient(loss_value, model.trainable_variables)
def gradient_func(model, inputs):
return backprop(inputs, model)
# Training loop function
def train(x_train, model, num_epochs, batch_size,optimizer):
train_loss = []
for epoch in range(num_epochs):
tf.random.shuffle(x_train)
for i in range(0, len(x_train), batch_size):
x_inp = x_train[i: i + batch_size]
loss_value, grads = gradient_func(model, x_inp)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
train_loss.append(tf.reduce_mean(tf.losses.mean_squared_error(x_train, model(x_train))).numpy())
if epoch % 100 == 0:
print("Epoch: {}, Train loss: {:.9f}".format(epoch, train_loss[epoch]))
return train_loss
#### Generating simple training and test data
num_train = 10000
num_test = 1000
x_train = s = np.random.uniform(0,1,(num_train,10)).astype(np.float32)
x_train[:,6:10] = 0
x_test = s = np.random.uniform(0,1,(num_test,10)).astype(np.float32)
x_test[:,6:10] = 0
###
batch_size = 8
num_epochs = 10000
test_loss = []
model_array = [0]
# Looping over the latent dimensions
for latent_dim in range(1,10):
model_array.append(Autoencoder(latent_dim))
# Creating an instance of my Autoencoder
optimizer = tf.keras.optimizers.Adam(learning_rate=0.00005) # Defining an optimizer
train_loss = train(x_train, model=model_array[latent_dim], num_epochs=num_epochs, batch_size=batch_size, optimizer=optimizer) # Training the network
test_loss.append(tf.reduce_mean(tf.losses.mean_squared_error(x_test, model_array[latent_dim](x_test))).numpy())
plt.figure()
plt.plot(range(1,10),test_loss,linewidth=1.5)
plt.grid(True)
plt.show()
在此 link 中的 TF 文档中也有关于 @tf.function 和 AutoGraphs 的简短讨论.
欢迎提问，希望对您有所帮助。
我正在使用 Tensorflow 2.1 创建自定义模型和自定义训练循环。我的目标是比较神经网络不同配置的准确性。具体来说，在这种情况下，我正在比较具有不同潜在维度的自动编码器的重建误差。因此，我正在为一个潜在维度训练我的网络，然后计算测试误差，然后我为另一个潜在维度重做这个过程，依此类推。通过这个过程，我想创建这样的图：
绘图示例：
为了加快训练速度，我想在训练循环的反向传播部分使用 @tf.function 装饰器。但是，当我尝试训练多个不同的网络时，循环遍历潜在维度时出现错误。见下文：
ValueError: in converted code:
<ipython-input-19-78bafad21717>:41 grad *
loss_value = tf.losses.mean_squared_error(inputs, model(inputs))
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py:778 __call__
outputs = call_fn(cast_inputs, *args, **kwargs)
<ipython-input-19-78bafad21717>:33 call *
x_enc = self.encoder(inp)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py:778 __call__
outputs = call_fn(cast_inputs, *args, **kwargs)
<ipython-input-19-78bafad21717>:9 call *
x = self.dense1(inp)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py:748 __call__
self._maybe_build(inputs)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py:2116 _maybe_build
self.build(input_shapes)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/layers/core.py:1113 build
trainable=True)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer.py:446 add_weight
caching_device=caching_device)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/training/tracking/base.py:744 _add_variable_with_custom_getter
**kwargs_for_getter)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/base_layer_utils.py:142 make_variable
shape=variable_shape if variable_shape else None)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/variables.py:258 __call__
return cls._variable_v1_call(*args, **kwargs)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/variables.py:219 _variable_v1_call
shape=shape)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/variables.py:65 getter
return captured_getter(captured_previous, **kwargs)
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py:502 invalid_creator_scope
"tf.function-decorated function tried to create "
ValueError: tf.function-decorated function tried to create variables on non-first call.
当我删除@tf.function 装饰器时，我没有得到这个错误。我相信当我使用装饰器时它是否与 Tensorflow 创建计算图有关，并且当我创建我的网络的另一个实例时该图仍然存在。因此，由于旧图与网络的新实例不匹配而引发错误。但我对此完全不确定，因为我相信我在这里遗漏了一些关于 Tensorflow 的基本知识！
下面是我的代码重现错误的一个非常简单的版本。我试图删除代码中所有不必要的部分，以使其更易于阅读和调试。此外，为了这个问题，我正在生成一个非常简单的训练和测试集。
我已经尝试过 tf.keras.backend.clear_session() 函数但没有任何运气。
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# Encoder
class build_encoder(tf.keras.Model):
def __init__(self,latent_dim):
super(build_encoder, self).__init__()
self.dense1 = tf.keras.layers.Dense(32, activation='relu',use_bias=True)
self.dense2 = tf.keras.layers.Dense(latent_dim, activation='relu',use_bias=True)
def call(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
return x
# Decoder
class build_decoder(tf.keras.Model):
def __init__(self,):
super(build_decoder, self).__init__()
self.dense1 = tf.keras.layers.Dense(32, activation='relu',use_bias=True)
self.dense2 = tf.keras.layers.Dense(10, activation='relu',use_bias=True)
def call(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
return x
# Full Autoencoder
class Autoencoder(tf.keras.Model):
def __init__(self,latent_dim=5):
super(Autoencoder, self).__init__()
self.encoder = build_encoder(latent_dim)
self.decoder = build_decoder()
def call(self, inp):
x_enc = self.encoder(inp)
x_dec = self.decoder(x_enc)
return x_dec
#### Here is the backpropagation with @tf.function decorator ####
@tf.function
def grad(model, inputs):
with tf.GradientTape() as tape:
loss_value = tf.losses.mean_squared_error(inputs, model(inputs))
return loss_value, tape.gradient(loss_value, model.trainable_variables)
# Training loop function
def train(x_train, model, num_epochs, batch_size,optimizer):
train_loss = []
for epoch in range(num_epochs):
tf.random.shuffle(x_train)
for i in range(0, len(x_train), batch_size):
x_inp = x_train[i: i + batch_size]
loss_value, grads = grad(model, x_inp)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
train_loss.append(tf.reduce_mean(tf.losses.mean_squared_error(x_train, model(x_train))).numpy())
if epoch % 100 == 0:
print("Epoch: {}, Train loss: {:.9f}".format(epoch, train_loss[epoch]))
return train_loss
#### Generating simple training and test data
num_train = 10000
num_test = 1000
x_train = s = np.random.uniform(0,1,(num_train,10)).astype(np.float32)
x_train[:,6:10] = 0
x_test = s = np.random.uniform(0,1,(num_test,10)).astype(np.float32)
x_test[:,6:10] = 0
###
batch_size = 8
num_epochs = 10000
test_loss = []
# Looping over the latent dimensions
for latent_dim in range(1,10):
model = Autoencoder(latent_dim=3) # Creating an instance of my Autoencoder
optimizer = tf.keras.optimizers.Adam(learning_rate=0.00005) # Defining an optimizer
train_loss = train(x_train, model=model, num_epochs=num_epochs, batch_size=batch_size, optimizer=optimizer) # Training the network
test_loss.append(tf.reduce_mean(tf.losses.mean_squared_error(x_test, model(x_test))).numpy())
plt.figure()
plt.plot(test_loss,linewidth=1.5)
plt.grid(True)
plt.show()
您提供的代码片段有误。
我将最后一个密集层单元从 6 更改为 10。
# Decoder
class build_decoder(tf.keras.Model):
def __init__(self,):
super(build_decoder, self).__init__()
self.dense1 = tf.keras.layers.Dense(32, activation='relu',use_bias=True)
self.dense2 = tf.keras.layers.Dense(10, activation='relu',use_bias=True)
def call(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
return x
关于你关于训练多模型的问题。
报错信息"ValueError: tf.function-decorated function tried to create variables on non-first call"表示@tf.function修饰的函数正在创建一个新变量在其下一次迭代中，这是不允许的，因为此函数已转换为图表。
我修改了你的反向传播方法，我把你原来的代码注释掉了，观察区别。
#### Here is the backpropagation with @tf.function decorator ####
# @tf.function
# def grad(model, inputs):
# with tf.GradientTape() as tape:
# loss_value = tf.losses.mean_squared_error(inputs, model(inputs))
# return loss_value, tape.gradient(loss_value, model.trainable_variables)
@tf.function
def MSE(y_true, y_pred):
return tf.keras.losses.MSE(y_true, y_pred)
def backprop(inputs, model):
with tf.GradientTape() as tape:
loss_value = MSE(inputs, model(inputs))
return loss_value, tape.gradient(loss_value, model.trainable_variables)
def gradient_func(model, inputs):
return backprop(inputs, model)
原始代码的罪魁祸首是调用 model(inputs) 作为损失函数的输入，当您在函数中装饰 @tf.function它继承了里面所有的函数，这意味着损失函数被优化了。
还有一种无需重写单个变量即可训练多个模型的方法，就是将它们放入数组中。
model_array = [0]
# Looping over the latent dimensions
for latent_dim in range(1,10):
model_array.append(Autoencoder(latent_dim))
# Creating an instance of my Autoencoder
optimizer = tf.keras.optimizers.Adam(learning_rate=0.00005) # Defining an optimizer
train_loss = train(x_train, model=model_array[latent_dim], num_epochs=num_epochs, batch_size=batch_size, optimizer=optimizer) # Training the network
test_loss.append(tf.reduce_mean(tf.losses.mean_squared_error(x_test, model_array[latent_dim](x_test))).numpy())
这会将模型排列成数组，更容易访问和调试。
这里是完整的修改代码。
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# Encoder
class build_encoder(tf.keras.Model):
def __init__(self,latent_dim):
super(build_encoder, self).__init__()
self.dense1 = tf.keras.layers.Dense(32, activation='relu',use_bias=True)
self.dense2 = tf.keras.layers.Dense(latent_dim, activation='relu',use_bias=True)
def call(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
return x
# Decoder
class build_decoder(tf.keras.Model):
def __init__(self,):
super(build_decoder, self).__init__()
self.dense1 = tf.keras.layers.Dense(32, activation='relu',use_bias=True)
self.dense2 = tf.keras.layers.Dense(10, activation='relu',use_bias=True)
def call(self, inp):
x = self.dense1(inp)
x = self.dense2(x)
return x
# Full Autoencoder
class Autoencoder(tf.keras.Model):
def __init__(self,latent_dim=5):
super(Autoencoder, self).__init__()
self.encoder = build_encoder(latent_dim)
self.decoder = build_decoder()
def call(self, inp):
x_enc = self.encoder(inp)
x_dec = self.decoder(x_enc)
return x_dec
#### Here is the backpropagation with @tf.function decorator ####
# @tf.function
# def grad(model, inputs):
# with tf.GradientTape() as tape:
# loss_value = tf.losses.mean_squared_error(inputs, model(inputs))
# return loss_value, tape.gradient(loss_value, model.trainable_variables)
@tf.function
def MSE(y_true, y_pred):
return tf.keras.losses.MSE(y_true, y_pred)
def backprop(inputs, model):
with tf.GradientTape() as tape:
loss_value = MSE(inputs, model(inputs))
return loss_value, tape.gradient(loss_value, model.trainable_variables)
def gradient_func(model, inputs):
return backprop(inputs, model)
# Training loop function
def train(x_train, model, num_epochs, batch_size,optimizer):
train_loss = []
for epoch in range(num_epochs):
tf.random.shuffle(x_train)
for i in range(0, len(x_train), batch_size):
x_inp = x_train[i: i + batch_size]
loss_value, grads = gradient_func(model, x_inp)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
train_loss.append(tf.reduce_mean(tf.losses.mean_squared_error(x_train, model(x_train))).numpy())
if epoch % 100 == 0:
print("Epoch: {}, Train loss: {:.9f}".format(epoch, train_loss[epoch]))
return train_loss
#### Generating simple training and test data
num_train = 10000
num_test = 1000
x_train = s = np.random.uniform(0,1,(num_train,10)).astype(np.float32)
x_train[:,6:10] = 0
x_test = s = np.random.uniform(0,1,(num_test,10)).astype(np.float32)
x_test[:,6:10] = 0
###
batch_size = 8
num_epochs = 10000
test_loss = []
model_array = [0]
# Looping over the latent dimensions
for latent_dim in range(1,10):
model_array.append(Autoencoder(latent_dim))
# Creating an instance of my Autoencoder
optimizer = tf.keras.optimizers.Adam(learning_rate=0.00005) # Defining an optimizer
train_loss = train(x_train, model=model_array[latent_dim], num_epochs=num_epochs, batch_size=batch_size, optimizer=optimizer) # Training the network
test_loss.append(tf.reduce_mean(tf.losses.mean_squared_error(x_test, model_array[latent_dim](x_test))).numpy())
plt.figure()
plt.plot(range(1,10),test_loss,linewidth=1.5)
plt.grid(True)
plt.show()
在此 link 中的 TF 文档中也有关于 @tf.function 和 AutoGraphs 的简短讨论.
欢迎提问，希望对您有所帮助。