如何修复此tensorflow代码中的TypeError?

代码:

import tensorflow as tf
from tensorflow import keras


fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

model = keras.Sequential([
    keras.layers.Flatten(input_shape = (28,28)),
    keras.layers.Dense(128,activation = tf.nn.relu),
    keras.layers.Dense(10,activation = tf.nn.softmax)
])

model.compile(optimizer=tf.optimizers.Adam(),),
              loss = 'sparse_categorical_crossentropy')

model.fit(train_images,train_labels,epochs = 5)

test_loss, test_acc = model.evaluate (test_images, test_labels)

首先,我有“AttributeError:module”tensorflow_api.v2.train“没有属性”AdamOptimizer“” 并更改了优化器=tf.train。AdamOptimizer()'到'tf.optimizers。Adam()'(工作),然后我又犯了一个错误。(在找到解决方案[https://stackoverflow.com/questions/55318273/tensorflow-api-v2-train-has-no-attribute-adamoptimizer] )

错误:

line 25, in <module> test_loss, test_acc = model.evaluate(test_images, test_labels) ^^^^^^^^^^^^^^^^^^^ TypeError: cannot unpack non-iterable float object

以下是ChatGPT建议我的内容(同样不起作用):

import tensorflow as tf
from tensorflow import keras


fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

# Reshape the input data
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28,28)),
    keras.layers.Dense(128, activation=tf.nn.relu),
    keras.layers.Dense(10, activation=tf.nn.softmax)
])

model.compile(optimizer=tf.optimizers.Adam(),
              loss='sparse_categorical_crossentropy')

model.fit(train_images, train_labels, epochs=5)

# Reshape the test data
test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)

test_loss, test_acc = model.evaluate(test_images, test_labels)

还有我从Zero到Hero tensorflow YT视频中获得的所有代码