我正在尝试在 Tensorflow 2.0 中实现多层 RNN 模型。尝试两者tf.keras.layers.StackedRNNCells并tf.keras.layers.RNN得出相同的结果。谁能帮我理解和之间的tf.keras.layers.RNN区别tf.keras.layers.StackedRNNCells？
# driving parameters
sz_batch = 128
sz_latent = 200
sz_sequence = 196
sz_feature = 2
n_units = 120
n_layers = 3
多层 RNN tf.keras.layers.RNN：
inputs = tf.keras.layers.Input(batch_shape=(sz_batch, sz_sequence, sz_feature))
cells = [tf.keras.layers.GRUCell(n_units) for _ in range(n_layers)]
outputs = tf.keras.layers.RNN(cells, stateful=True, return_sequences=True, return_state=False)(inputs)
outputs = tf.keras.layers.Dense(1)(outputs)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.summary()
返回：
Model: "model_13"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_88 (InputLayer) [(128, 196, 2)] 0
_________________________________________________________________
rnn_61 (RNN) (128, 196, 120) 218880
_________________________________________________________________
dense_19 (Dense) (128, 196, 1) 121
=================================================================
Total params: 219,001
Trainable params: 219,001
Non-trainable params: 0
多层 RNNtf.keras.layers.RNN和tf.keras.layers.StackedRNNCells：
inputs = tf.keras.layers.Input(batch_shape=(sz_batch, sz_sequence, sz_feature))
cells = [tf.keras.layers.GRUCell(n_units) for _ in range(n_layers)]
outputs = tf.keras.layers.RNN(tf.keras.layers.StackedRNNCells(cells),
stateful=True,
return_sequences=True,
return_state=False)(inputs)
outputs = tf.keras.layers.Dense(1)(outputs)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.summary()
返回：
Model: "model_14"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_89 (InputLayer) [(128, 196, 2)] 0
_________________________________________________________________
rnn_62 (RNN) (128, 196, 120) 218880
_________________________________________________________________
dense_20 (Dense) (128, 196, 1) 121
=================================================================
Total params: 219,001
Trainable params: 219,001
Non-trainable params: 0