json_file = open('model_ImageDataGenerator_myImage.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
model = tf.keras.models.model_from_json(loaded_model_json)
model.load_weights("model_ImageDataGenerator_myImage.h5")
model.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.Adadelta(),
metrics=['accuracy'])

model.summary()

opt1 = tf.keras.optimizers.Adam(lr=0.001)
model.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])

img = cv2.imread('F:/1100317_rotate/glue/IMG_4871.bmp')

w=200
h=200
X=[]

img = cv2.resize(img, (w,h), interpolation=cv2.INTER_AREA)

img=img.reshape(w,h,3)
img = img.astype('uint8')
img = cv2.resize(img, (200, 200), interpolation=cv2.INTER_AREA)
im_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
X.append(im_rgb)
X = np.asarray(X)
X = X.astype('float32')
X=X/255
X=X.reshape(X.shape[0],w,h,3);
predict = model.predict(X)

想詢問一下
ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 960000 but received input with shape [None, 1280000]

不太懂哪邊資料錯誤
模組使用訓練圖片之預處理為

X=[]
Y=[]
print(dirs);
w=200 # 1000
h=200 # 1000
i=0
for name in dirs:
#file_paths = glob.glob(path.join(IMAGEPATH+"/"+name, '.'))
file_paths = glob.glob(path.join(IMAGEPATH,name))
file_name = os.listdir(file_paths[0])
for path3 in file_name :
print(path.join(file_paths[0], path3))
img = cv2.imread(path3)
img = cv2.imread(path.join(file_paths[0], path3))
if img is not None:
img = cv2.resize(img, (w,h), interpolation=cv2.INTER_AREA)
im_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
X.append(im_rgb)
Y.append(i)
i=i+1
print(path3)
X = np.asarray(X)
Y = np.asarray(Y)
print(X)
print(Y)
print("+++++++++Done reading!!+++++++++")

X = X.astype('float32')
X=X/255
X=X.reshape(X.shape[0],w,h,3);

是以相同影像處理方式做預處理
為何無法判別呢