Machine learning 如何在keras中高效地创建自定义循环网络？
我有一个函数，它将一个（图像，隐藏状态）作为输入，并将输出反馈回输入，这涉及序列的长度，最后一个输出是我感兴趣的：- 递归函数如下所示：-Machine learning 如何在keras中高效地创建自定义循环网络？,machine-learning,keras,recurrent-neural-network,Machine Learning,Keras,Recurrent Neural Network,我有一个函数，它将一个（图像，隐藏状态）作为输入，并将输出反馈回输入，这涉及序列的长度，最后一个输出是我感兴趣的：- 递归函数如下所示：- def recurrence(): input_image = Input(shape=[dim1,dim2,dim3]) hidden_state= Input(shape=[4,4,4,128]) conv1a = Conv2D(filters=96, kernel_size=7, padding='same',input_shape=(dim1,
def recurrence():
input_image = Input(shape=[dim1,dim2,dim3])
hidden_state= Input(shape=[4,4,4,128])
conv1a = Conv2D(filters=96, kernel_size=7, padding='same',input_shape=(dim1, dim2,dim3), kernel_initializer=k_init, bias_initializer=b_init)(input_image)
conv1a = LeakyReLU(0.01)(conv1a)
conv1b = Conv2D(filters=96, kernel_size=3, padding='same', kernel_initializer=k_init, bias_initializer=b_init)(conv1a)
conv1b = LeakyReLU(0.01)(conv1b)
conv1b = ZeroPadding2D(padding=(1, 1))(conv1b)
pool1 = MaxPooling2D(2)(conv1b)
flat6 = Flatten()(pool1)
fc7 = Dense(units=1024, kernel_initializer=k_init, bias_initializer=b_init)(flat6)
rect7 = LeakyReLU(0.01)(fc7)
t_x_s_update_conv = Conv3D(128, 3, activation=None, padding='same', kernel_initializer=k_init, use_bias=False)(hidden_state)
t_x_s_update_dense =Reshape((4,4,4,128))(Dense(units=8192)(rect7))
t_x_s_update = layers.add([t_x_s_update_conv, t_x_s_update_dense])
t_x_s_reset_conv = Conv3D(128, 3, activation=None, padding='same', kernel_initializer=k_init, use_bias=False)(hidden_state)
t_x_s_reset_dense =Reshape((4,4,4,128))(Dense(units=8192)(rect7))
t_x_s_reset =layers.add([t_x_s_reset_conv, t_x_s_reset_dense])
update_gate = Activation(K.sigmoid)(t_x_s_update)
comp_update_gate = Lambda(lambda x: 1 - x)(update_gate)
reset_gate = Activation(K.sigmoid)(t_x_s_reset)
rs = layers.multiply([reset_gate, hidden_state])
t_x_rs_conv = Conv3D(128, 3, activation=None, padding='same', kernel_initializer=k_init, use_bias=False)(rs)
t_x_rs_dense = Reshape((4,4,4,128))(Dense(units=8192)(rect7))
t_x_rs = layers.add([t_x_rs_conv, t_x_rs_dense])
tanh_t_x_rs = Activation(K.tanh)(t_x_rs)
gru_out = layers.add([layers.multiply([update_gate, hidden_state]), layers.multiply([comp_update_gate, tanh_t_x_rs])])
return Model(inputs=[input_image,hidden_state],outputs=gru_out)
step=recurrence()
s_update = hidden_init
for i in input_imgs:
s_update = step([i, s_update])`