我试图在此代码上测试我自己的图像，我可以看到模型具有很高的验证准确性，但我想看看它将如何在其他图像上执行，我已经尝试了model.predict，但它给出了一个错误，因为输入是不兼容的，整个代码工作正常，但预测部分（最后4行）是问题发生的地方-这是错误
“层“conv 2d”的输入0与层不兼容：预期的min_ndim=4，找到的ndim=1。收到的完整图形：（无）”

import numpy as np
import os
import PIL
import PIL.Image
import tensorflow as tf
import cv2 as cv
import tensorflow as tf

from tensorflow.keras import datasets, layers, models

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import matplotlib.pyplot as plt

data_dir = "/Users/anastalib/PycharmProjects/pythonProject1/BananaData"
# test_dir = "/Users/anastalib/PycharmProjects/pythonProject1/test_dataset"

batch_size = 50
img_height = 32
img_width = 32

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="training",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

print(train_ds)

test_ds = tf.keras.preprocessing.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="validation",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)
print(test_ds)

class_names = train_ds.class_names
print(class_names)

# plt.figure(figsize=(10, 10))
# for images, labels in train_ds.take(1):
#   for i in range(9):
#     ax = plt.subplot(3, 3, i + 1)
#     plt.imshow(images[i].numpy().astype("uint8"))
#     plt.title(class_names[labels[i]])
#     plt.axis("off")

num_classes = 3
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))

model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(num_classes))

model.summary()

model.compile(
    optimizer='adam',
    loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=['accuracy'])

history = model.fit(
    train_ds,
    epochs=20
)
# history = model_1.fit(train_ds, epochs=4)
# predictions = model_1.predict(test_ds)
# print(class_names[np.argmax(predictions)])
#

train_loss, train_accuracy = model.evaluate(train_ds, verbose=2)
print('\nTrain Accuracy:', train_loss)
print('\nTrain Loss:', train_accuracy)

test_loss, test_acc = model.evaluate(test_ds, verbose=2)

print('\nTest Accuracy:', test_acc)
print('\nTest Loss:', test_loss)
#
# Plot training accuracy vs loss values
plt.plot(history.history['accuracy'])
plt.plot(history.history['loss'])  # this is ripe
plt.title('Train Accuracy vs validation accuracy')
plt.ylabel('Percentage')
plt.xlabel('Epoch')
plt.legend(['Training accuracy', 'Training Loss'], loc='upper left')
plt.show()

image = '/Users/anastalib/PycharmProjects/pythonProject1/green.jpeg'
prediction = model.predict(np.expand_dims(image, axis=0))

# argclass = np.argmax(prediction, axis=1)

print('predictions:', prediction)
print(class_names[np.argmax(prediction)])