Revert "Use myReshape instead of reshape or lapply to create the batc…

…hes. (faster than original, but slower than lapply)" This reverts commit 19c5e58.
caewok · Jan 30, 2013 · 21d886d · 21d886d
1 parent 19c5e58
commit 21d886d
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Showing 2 changed files with 18 additions and 10 deletions.
diff --git a/Neural Net Language Model/LoadData.R b/Neural Net Language Model/LoadData.R
@@ -33,8 +33,16 @@ load_data <- function(N) {
 
      # shift to an list of M minibatches, each with D*N
      # looks like we threw out the remainder training data
-     train_input <- myReshape(data$trainData[1:D, 1:(N*M), drop=F], D, N, M)
-     train_target <- myReshape(data$trainData[D + 1, 1:(N*M), drop=F], 1, N, M)
+     start <- seq.int(1, N*M, by=1000)
+     end <- seq.int(1000, N*M, by=1000)
+     train_input <- mapply(function(x, start, end) x[,start:end], 
+                                           start=seq.int(1, N*M, by=1000), 
+                                           end=seq.int(1000, N*M, by=1000), 
+                                           MoreArgs=list(x=data$trainData[1:D, 1:(N*M)]), SIMPLIFY=F)
+     train_target <- mapply(function(x, start, end) x[,start:end], 
+                            start=seq.int(1, N*M, by=1000), 
+                            end=seq.int(1000, N*M, by=1000), 
+                            MoreArgs=list(x=data$trainData[D + 1, 1:(N*M), drop=F]), SIMPLIFY=F)
      valid_input <- data$validData[1:D,, drop=F]
      valid_target <- data$validData[D + 1, , drop=F]
      test_input <- data$validData[1:D, , drop=F]

diff --git a/Neural Net Language Model/Train.R b/Neural Net Language Model/Train.R
@@ -38,11 +38,11 @@ train <- function(epochs) {
 
      #      % LOAD DATA.
      data <- load_data(batchsize)
-     tmp <- size(data$train_input)
+     tmp <- size(data$train_input[[1]])
      numwords <- tmp[1]
      #batchsize <- tmp[2]
-     numbatches <- tmp[3]
-     #numbatches <- length(data$train_input)
+     #numbatches <- tmp[3]
+     numbatches <- length(data$train_input)
      vocab_size <- size(data$vocab, 2)  
 
      word_embedding_weights = init_wt * randn(vocab_size, numhid1);
@@ -75,14 +75,14 @@ train <- function(epochs) {
            #while(hasNext(inputIT) & hasNext(targetIT)) {    
 
 
-               input_batch <- data$train_input[,,m]
-               target_batch <- data$train_target[,,m]
+               #input_batch <- data$train_input[,,m]
+               #target_batch <- data$train_target[,,m]
                #input_batch <- nextElem(inputIT)
                #target_batch <- nextElem(targetIT)
-               #input_batch <- data$train_input[[m]]
-               #target_batch <- data$train_target[[m]]
+               input_batch <- data$train_input[[m]]
+               target_batch <- data$train_target[[m]]
 
-               #dim(target_batch) <- NULL
+               dim(target_batch) <- NULL
 
                #                % FORWARD PROPAGATE.
                #                % Compute the state of each layer in the network given the input batch and all weights and biases