基于新图像的路缘石模型预测误差

在Keras中建立了图像分类模型,并在调用 predict 或 predict_classes 我收到一个错误- expected conv2d_1_input to have 4 dimensions, but got array with shape (150, 150, 3)

这是我的模型:

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
_________________________________________________________________

下面是我用来预测模型的代码:

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)

有或没有 np.reshape 我还是觉得形状不对。

我是否不正确地重塑了图像?或者我需要调整我的模型以便能够预测?