Python 为什么keras模型要编译，而fit#u生成器命令抛出一个'；模型未编译运行时错误'；？

我用tensorflow后端在keras中建立了一个自定义CNN，带有K折叠交叉验证。 在开始培训之前调用model.compile（）函数，但是model.fit_generator（）函数调用会导致运行时错误：“必须在使用模型之前编译模型。”

我使用ImageDataGenerator进行数据扩充，并使用fit_generator函数进行培训

到目前为止，我发现的唯一相关问题与tensorflow特性有关，该特性在Keras中似乎没有启用

代码如下：

模型定义：

model = Sequential()
model.add(Conv2D(24, (5, 5), 
          strides=(1, 1), 
          padding="valid", 
          data_format="channels_last",
          activation='relu', 
          use_bias=True,
          ))#out=96
model.add(Dropout(.25))
model.add(MaxPooling2D(pool_size=(2, 2)))#out=48
model.add(Conv2D(32, (3, 3), 
          strides=(1, 1), 
          padding="valid", 
          data_format="channels_last",
          activation='relu', 
          use_bias=True,
          ))#out=46
model.add(MaxPooling2D(pool_size=(2, 2)))#out=23
model.add(Conv2D(48, (3, 3), 
          strides=(1, 1), 
          padding="valid", 
          data_format="channels_last",
          activation='relu', 
          use_bias=True,
          ))#out=21
model.add(MaxPooling2D(pool_size=(2, 2)))#padding???
model.add(Flatten())
model.add(Dense(3, activation='softmax'))

…这是数据初始化

设置图像生成器：

datagen_training = ImageDataGenerator(
            rotation_range = 20,
            width_shift_range = 0.3,
            height_shift_range=0.3,
            zoom_range=0.2,
            fill_mode = "constant",
            cval = 0,
            vertical_flip = True,
            validation_split = 0.2
            )
datagen_training.fit(data)

模型设置和培训：

rmsprop = optimizers.RMSprop(lr=0.001)#docu says to only tune the learning rate
kf = KFold(n_splits=FOLDS, shuffle = True, random_state=78945)
model.compile(rmsprop, loss = losses.categorical_crossentropy, metrics=[metrics.categorical_accuracy])
acc_hist = []
while True:
    history = object()
    for train_idx, val_idx in kf.split(data, labels):
        x_train, y_train = data[train_idx], labels[train_idx]
        x_val, y_val = data[val_idx], labels[val_idx]
        data_iterator = datagen_training.flow(x_train, y_train, batch_size=BATCH_SIZE)
        history = model.fit_generator(data_iterator, steps_per_epoch=len(x_train) // BATCH_SIZE, epochs=1)
    acc_hist.append(history.history['categorical_accuracy'][0])
    #stop if accuracy doesn't change within 3 epochs
    if stopping_criterion_met:
        break

---

模型没有编译是因为它没有被构建，也没有被构建是因为没有在第一层中指定

input\u shape

。我不确定数据的输入形状，但对于第一层来说，类似这样的东西将使模型得以编译：

model = Sequential()
model.add(Conv2D(24, (5, 5), 
      strides=(1, 1), 
      padding="valid", 
      data_format="channels_last",
      activation='relu', 
      use_bias=True,
      input_shape=(100,100,1)
      ))#out=96