keras ConvLSTM2D 的簡單應用

import tensorflow as tf

import numpy as np
import keras
from keras.layers import convlstm2d
lstm_input = np.random.random((4,6,30,30,3)).astype(np.float32)
lstm_input = tf.convert_to_tensor(lstm_input)
lstm_out1 = convlstm2d(filters=1,kernel_size=[5,5],strides=(1,1),padding='valid',activation='relu',
input_shape=(6,30,30,3),return_sequences=true)(lstm_input)
lstm_out2 = convlstm2d(filters=2,kernel_size=[5,5],strides=(1,1),padding='valid',activation='relu',
return_sequences=true)(lstm_out1)
lstm_out3 = convlstm2d(filters=3,kernel_size=[5,5],strides=(1,1),padding='valid',activation='relu',
return_sequences=true)(lstm_out2)
with tf.session() as sess:
sess.run(tf.global_variables_initializer())
lstm_out1_,lstm_out2_,lstm_out3_ = sess.run([lstm_out1,lstm_out2,lstm_out3])
print(lstm_out1_.shape)
print(lstm_out2_.shape)
print(lstm_out3_.shape)
"""返回：
(4, 6, 26, 26, 1)
(4, 6, 22, 22, 2)
(4, 6, 18, 18, 3)
"""

return_sequences: 預設是false，控制lstm的輸出：

用return_state=true控制 

import tensorflow as tf

import numpy as np
import keras
from keras.layers import convlstm2d
lstm_input = np.random.random((4,6,30,30,3)).astype(np.float32)
lstm_input = tf.convert_to_tensor(lstm_input)
lstm_out,state_h,state_c = convlstm2d(filters=1,kernel_size=[5,5],strides=(1,1),padding='valid',activation='relu',
batch_input_shape=(-1,6,30,30,3),return_sequences=false,return_state=true)(lstm_input)
with tf.session() as sess:
sess.run(tf.global_variables_initializer())
lstm_out_,state_h_,state_c_= sess.run([lstm_out,state_h,state_c])
print(lstm_out_==state_h_)
print(lstm_out_.shape)
print(state_h_.shape)
print(state_c_.shape)
"""返回：
[ true]]]]
(4, 26, 26, 1)
(4, 26, 26, 1)
(4, 26, 26, 1)
"""

return_sequences: 決定是否返回所有時刻的狀態

return_state:決定是否返回最後乙個時刻的cell狀態，由示例2結果可見，最後乙個時刻的state = [h,c]

注意：在keras 中文文件中，在介紹convlstm2d時，沒有介紹 return_state 引數，該引數在lstm的介紹中介紹，但是在convlstm2d中通用。。。自己差點以為convlstm2d中沒有這個功能。。。

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