Python 层的不兼容输入（ndim=4，发现ndim=3）

我试图在Keras重新创建花卉识别CNN。该模型似乎可以工作，至少在笔记本中是这样（同时在验证集上获得预测），但我需要在其他地方使用该模型。照片是150x150，这是我如何建立CNN的：

model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(5,5), padding='Same', activation='relu', input_shape=(150, 150, 3)))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(filters=64, kernel_size=(3,3), padding='Same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))

model.add(Conv2D(filters=96, kernel_size=(3,3), padding='Same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))

model.add(Conv2D(filters=96, kernel_size=(3,3), padding='Same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dense(5, activation="softmax"))

尽管如此，当我试图通过样本照片和加载的模型在本地预测结果时：

model = model_from_json(open("model.json", "r").read())
model.load_weights('model.h5')

img = cv2.imread('/path/to/the/dir/testimage.jpg')
img = cv2.resize(img, (150, 150))
data = np.array(img)
model.summary()
result = model.predict(data)

我收到这个错误：

ValueError: Input 0 of layer sequential_1 is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: [None, 150, 3]

模型汇总的输出为：

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 150, 150, 32)      2432      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 75, 75, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 75, 75, 64)        18496     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 37, 37, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 37, 37, 96)        55392     
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 18, 18, 96)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 18, 18, 96)        83040     
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 9, 9, 96)          0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 7776)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 512)               3981824   
_________________________________________________________________
activation_1 (Activation)    (None, 512)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 5)                 2565      
=================================================================
Total params: 4,143,749
Trainable params: 4,143,749
Non-trainable params: 0

---

我不明白为什么它会收到一个不同的形状。我已经读到重塑可能会做这件事，但我正在使用

fit\u generator

来适应训练集，我不确定这是否可行。

确保第一个维度为1（要预测的图像为1）。您的模型期望无维度为采样数，第二+第三维度为图像分辨率，第四维度为图像的RGB通道

data=np.展开尺寸（数据，轴=0）

将向第一个轴添加额外尺寸

见：