我想使用具有 LSTM 层的 keras 模型进行量化感知训练。但是，似乎只支持 LSTM 层。Alan Chiao 似乎在这里建议可以使用 Quantize Config 来允许使用该层。但是，我似乎无法制作允许使用 LSTM 层的正确量化配置。

我在我的模型和量化配置的代码下面附上了。

任何人都可以确认

1. 如何使用带有 keras 的 LSTM 层正确地制作量化感知模型
2. 如果 keras 根本不支持 LSTM 量化

感谢您的反馈！

from tensorflow.keras import layers as L
quant_layer = tfmot.quantization.keras.quantize_annotate_layer
quant_apply = tfmot.quantization.keras.quantize_apply
quantize_scope = tfmot.quantization.keras.quantize_scope

######################  Simple Quantized Model   ####################

i = tf.keras.Input(shape=(32,32,1))
x = (L.Conv2D(1, (5, 1), activation='relu', padding='same'))(i)
x = L.BatchNormalization()(x)
x = L.Lambda(lambda q: K.squeeze(q, -1), name='squeeze_last_dim')(x)
x = quant_layer(L.LSTM(64, return_sequences=True),DefaultLSTMQuantizeConfig())(x)
x = tf.keras.layers.Flatten()(x)
x = L.Dense(64, activation='relu')(x)
o = L.Dense(12, activation='softmax')(x)

simple_model = Model(inputs=[i], outputs=[o])
simple_model.compile(optimizer='adam', loss=['sparse_categorical_crossentropy'], metrics=['sparse_categorical_accuracy'])

with quantize_scope(
    {'DefaultLSTMQuantizeConfig': DefaultLSTMQuantizeConfig}):
    simple_model_quant = quant_apply(simple_model)
     
simple_model_quant.summary()

##################### Quantize Config  #################
LastValueQuantizer = tfmot.quantization.keras.quantizers.LastValueQuantizer
MovingAverageQuantizer = tfmot.quantization.keras.quantizers.MovingAverageQuantizer

class DefaultLSTMQuantizeConfig(tfmot.quantization.keras.QuantizeConfig):
    # Configure how to quantize weights.
    def get_weights_and_quantizers(self, layer):
        return [(layer.layer.recurrent_kernel, LastValueQuantizer(num_bits=8, symmetric=True, narrow_range=False, per_axis=False))]

    # Configure how to quantize activations.
    def get_activations_and_quantizers(self, layer):
        return [(layer.activation, MovingAverageQuantizer(num_bits=8, symmetric=False, narrow_range=False, per_axis=False))]

    def set_quantize_weights(self, layer, quantize_weights):
      # Add this line for each item returned in `get_weights_and_quantizers`
      # , in the same order
        layer.kernel = quantize_weights[0]

    def set_quantize_activations(self, layer, quantize_activations):
      # Add this line for each item returned in `get_activations_and_quantizers`
      # , in the same order.
        layer.activation = quantize_activations[0]

    # Configure how to quantize outputs (may be equivalent to activations).
    def get_output_quantizers(self, layer):
        return []

    def get_config(self):
        return {}