Numpy错误可以使用**tf.convert_to_tensor（）**方法修复。

img_array = tf.convert_to_tensor(img_array)

您请求的图像工作参考代码如下：

import tensorflow as tf
# Load the training and test split of the Fashion MNIST dataset
(training_images, training_labels), (test_images, test_labels) = fmnist.load_data()

# Normalize the pixel values of the train and test images
training_images  = training_images / 255.0
test_images = test_images / 255.0

# Build the classification model
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(), 
                                    tf.keras.layers.Dense(128, activation=tf.nn.relu), 
                                    tf.keras.layers.Dense(10, activation=tf.nn.softmax)])

# Declare sample inputs and convert to a tensor
inputs = np.array([[1.0, 3.0, 4.0, 2.0]])
inputs = tf.convert_to_tensor(inputs)
print(f'input to softmax function: {inputs.numpy()}')

# Feed the inputs to a softmax activation function
outputs = tf.keras.activations.softmax(inputs)
print(f'output of softmax function: {outputs.numpy()}')

prediction = np.argmax(outputs)
print(f'class with highest probability: {prediction}')

model.compile(optimizer = tf.optimizers.Adam(),
              loss = 'sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(training_images, training_labels, epochs=5)

# Evaluate the model on unseen data
model.evaluate(test_images, test_labels)

希望这能解决你的疑问。