在Keras中建立了图像分类模型，并且在调用predict或predict_classes时收到错误消息-expected conv2d_1_input to have 4 dimensions, but got array with shape (150, 150, 3)
这是我的模特：
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation="relu", input_shape=(150, 150, 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(128, (3, 3), activation="relu", input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation="relu"))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation="relu"))
model.add(layers.Dense(5, activation="softmax"))
以及模型摘要：
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 148, 148, 32) 896
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 74, 74, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 72, 72, 64) 18496
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 36, 36, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 34, 34, 128) 73856
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 17, 17, 128) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 15, 15, 128) 147584
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 7, 7, 128) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 6272) 0
_________________________________________________________________
dense_1 (Dense) (None, 512) 3211776
_________________________________________________________________
dense_2 (Dense) (None, 5) 2565
=================================================================
Total params: 3,455,173
Trainable params: 3,455,173
Non-trainable params: 0
_________________________________________________________________
这是我用来在模型上预测的代码：
img = load_img('rose.jpg', target_size=(150, 150, 3))
x = img_to_array(img)
x = np.reshape(x, (150, 150, 3))
model.predict(x)
无论有没有np.reshape，我仍然会收到有关形状不正确的错误。
我不正确地重塑图像吗？还是我需要调整模型以进行预测？