ValueError：参数clone_function和input_tensors仅支持顺序模型或功能模型

Question

我正在使用量化感知训练。我参考了网上的lstm代码，想把

QAT

放入lstm中，但是遇到了ValueError。

ValueError                                Traceback (most recent call last)
<ipython-input-11-00669bb76f9d> in <cell line: 6>()
      4     return layer
      5 
----> 6 annotated_model = tf.keras.models.clone_model(
      7     model,
      8     clone_function=apply_quantization_to_dense,

/usr/local/lib/python3.10/dist-packages/tf_keras/src/models/cloning.py in clone_model(model, input_tensors, clone_function)
    544         # Case of a custom model class
    545         if clone_function or input_tensors:
--> 546             raise ValueError(
    547                 "Arguments clone_function and input_tensors "
    548                 "are only supported for Sequential models "

ValueError: Arguments clone_function and input_tensors are only supported for Sequential models or Functional models. Received model of type 'Sequential', with clone_function=<function apply_quantization_to_dense at 0x78b727ec4040> and input_tensors=None

这是我的代码

import keras
from keras.layers import LSTM
from keras.layers import Dense, Activation
from keras.datasets import mnist
from keras.models import Sequential
from keras.optimizers import Adam

learning_rate = 0.001
training_iters = 20
batch_size = 128
display_step = 10

n_input = 28
n_step = 28
n_hidden = 128
n_classes = 10

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(-1, n_step, n_input)
x_test = x_test.reshape(-1, n_step, n_input)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

y_train = keras.utils.to_categorical(y_train, n_classes)
y_test = keras.utils.to_categorical(y_test, n_classes)

model = Sequential()
model.add(LSTM(n_hidden,
               batch_input_shape=(None, n_step, n_input),
               unroll=True))

model.add(Dense(n_classes))
model.add(Activation('softmax'))

adam = Adam(lr=learning_rate)
model.summary()
model.compile(optimizer=adam,
              loss='categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=training_iters,
          verbose=1,
          validation_data=(x_test, y_test))

scores = model.evaluate(x_test, y_test, verbose=0)
print('LSTM test score:', scores[0])
print('LSTM test accuracy:', scores[1])

def apply_quantization_to_dense(layer):
    if isinstance(layer, tf.keras.layers.LSTM):
        return tfmot.quantization.keras.quantize_annotate_layer(layer)
    return layer

annotated_model = tf.keras.models.clone_model(
    model,
    clone_function=apply_quantization_to_dense,
)

Answer 1

我也遇到过同样的问题。为您的模型尝试以下语法：

import tf_keras as keras

model = keras.Sequential([

keras.layers.LSTM(n_hidden,
               batch_input_shape=(None, n_step, n_input),
               unroll=True),
keras.layers.Dense(n_classes),
keras.layers.Activation('softmax')

]) 

艾迪33