Question

我正在尝试使用RStudio的Keras接口使神经网络训练可重现。在R脚本（set.seed(42)）中设置种子似乎不起作用。是否可以将种子作为参数传递给layer_dense()？我可以选择RandomUniform作为初始化程序，但是我很难通过种子论证。以下行引发错误：

model %>% layer_dense(units = 12, activation = 'relu', input_shape = c(8), kernel_initializer = "RandomUniform(seed=1)")

但是可以在不尝试传递种子参数的情况下添加图层：

model %>% layer_dense(units = 12, activation = 'relu', input_shape = c(8), kernel_initializer = "RandomUniform")

RandomUniform假设根据Keras initializer documents接受种子参数。

Answer 1

kernel initializer参数语法应该是这样的。 kernel_initializer=initializer_random_uniform(minval = -0.05, maxval = 0.05, seed = 104)

尝试以下步骤。

1）在导入keras / tensorflow之前为R环境设置种子

2）设置tensorflow会话配置以使用单线程

3）设置张量流随机种子

4）使用此种子创建tensorflow会话并将其分配给keras后端。

5）最后在你的模型层中，如果你使用随机初始化器，如random_uniform（这是默认值）或random_normal，那么你必须将seed参数更改为某个整数以下是一个例子

# Set R random seed
set.seed(104)
library(keras)
library(tensorflow)

# TensorFlow session configuration that uses only a single thread. Multiple threads are a 
# potential source of non-reproducible results, see: https://stackoverflow.com/questions/42022950/which-seeds-have-to-be-set-where-to-realize-100-reproducibility-of-training-res
#session_conf <- tf$ConfigProto(intra_op_parallelism_threads = 1L, 
#                               inter_op_parallelism_threads = 1L)

# Set TF random seed (see: https://www.tensorflow.org/api_docs/python/tf/set_random_seed)
tf$set_random_seed(104)

# Create the session using the custom configuration
sess <- tf$Session(graph = tf$get_default_graph(), config = session_conf)

# Instruct Keras to use this session
K <- backend()
K$set_session(sess)


#Then in your model architecture, set seed to all random initializers.

model %>% 
    layer_dense(units = n_neurons, activation = 'relu', input_shape = c(100),kernel_initializer=initializer_random_uniform(minval = -0.05, maxval = 0.05, seed = 104)) %>% 
    layer_dense(units = n_neurons, activation = 'relu',kernel_initializer=initializer_random_uniform(minval = -0.05, maxval = 0.05, seed = 104)) %>%
    layer_dense(units =c(100) ,kernel_initializer=initializer_random_uniform(minval = -0.05, maxval = 0.05, seed = 104))

参考文献： https://rstudio.github.io/keras/articles/faq.html#how-can-i-obtain-reproducible-results-using-keras-during-development https://rstudio.github.io/keras/reference/initializer_random_normal.html#arguments

Answer 2

library(keras)
use_session_with_seed(42)

use_session_with_seed（）函数为R，Python，Numpy和Tensorflow建立公共随机种子。有关更多详细信息，请参见https://keras.rstudio.com/articles/faq.html

使用RStudio的Keras接口使神经网络训练可重现

2 个答案: