【发布时间】:2017-06-16 07:12:17
【问题描述】:
这个问题是关于 R 和来自 Rstudio 的包 keras。 (https://github.com/rstudio/keras)
我正在尝试学习一个模型来标记序列的某些部分。
我希望模型执行以下操作: [64,34,77,33,88] -> [0,0,1,1,0]
因此,在输入中,我有一个使用pad_sequences 生成的序列矩阵(每行 1 个序列),如下所示:
int [1:21885, 1:30] 21 21 1506 28 102 21 61 224 15 15 ...
输出也是pad_sequences产生的序列矩阵:
int [1:21885, 1:30] 0 0 1 0 0 0 1 1 0 0 ...
这是模拟我使用的输入/输出形状的代码:
input_length = 30
n_sample = 5
vocab_size = 100
quest_train <- matrix(floor(runif(input_length*n_sample, 1,vocab_size)), ncol = input_length)
tag_train <- matrix(sample(c(0,1), size = input_length*n_sample, replace = T), ncol = input_length)
这是我尝试拟合的模型:
input_dim = vocab_size
embed_dim = 50
model <- keras_model_sequential()
model %>%
layer_embedding(input_dim = input_dim,
output_dim = embed_dim) %>%
layer_dropout(rate = 0.2) %>%
layer_lstm(units = 128, return_sequences = T) %>%
layer_dropout(rate = 0.5) %>%
time_distributed(layer_dense(units = 2, activation = 'softmax'))
model %>%
compile(loss = 'categorical_crossentropy',
optimizer = 'adam',
metrics = c('accuracy'))
model %>% fit(quest_train,
tag_train,
batch_size = 16 ,
epochs = 10,
shuffle = TRUE)
但是当我尝试运行它时,我得到了这个错误:
Error in py_call_impl(callable, dots$args, dots$keywords) :
ValueError: Error when checking model target: expected time_distributed_23 to have 3 dimensions, but got array with shape (5, 30)
所以我尝试将输出向量转换为带有to_categorical 的二维矩阵列表
像这样:
tags_train_cat <- lapply(1:nrow(tag_train), function(x) (to_categorical(tag_train[x,])))
那么我的新目标是这样的:
List of 5
$ : num [1:30, 1:2] 1 1 1 1 1 0 1 1 1 1 ...
$ : num [1:30, 1:2] 1 1 1 1 1 0 0 1 1 1 ...
$ : num [1:30, 1:2] 0 1 1 1 1 1 1 1 1 1 ...
$ : num [1:30, 1:2] 1 1 1 1 1 1 0 0 0 0 ...
...
但知道我收到此错误:
ValueError: Error when checking model target: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 arrays but instead got the following list of X arrays
所以,我的问题是:我做错了什么?
【问题讨论】:
-
我找不到名为
keras的包。有一个kerasR,但不是由 Rstudio 提供的。你能澄清一下吗? -
你可以在这里找到它:github.com/rstudio/keras