mnist_hierarchical_rnn.R example

vignettes/examples/mnist_hierarchical_rnn.R

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+#' This is an example of using Hierarchical RNN (HRNN) to classify MNIST digits.
+#' 
+#' HRNNs can learn across multiple levels of temporal hiearchy over a complex sequence.
+#' Usually, the first recurrent layer of an HRNN encodes a sentence (e.g. of word vectors)
+#' into a  sentence vector. The second recurrent layer then encodes a sequence of
+#' such vectors (encoded by the first layer) into a document vector. This
+#' document vector is considered to preserve both the word-level and
+#' sentence-level structure of the context.
+#' 
+#' References:
+#' 
+#' - [A Hierarchical Neural Autoencoder for Paragraphs and Documents](https://arxiv.org/abs/1506.01057)
+#'   Encodes paragraphs and documents with HRNN.
+#'   Results have shown that HRNN outperforms standard
+#'   RNNs and may play some role in more sophisticated generation tasks like
+#'   summarization or question answering.
+#' - [Hierarchical recurrent neural network for skeleton based action recognition](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298714)
+#'   Achieved state-of-the-art results on skeleton based action recognition with 3 levels
+#'   of bidirectional HRNN combined with fully connected layers.
+#' 
+#' In the below MNIST example the first LSTM layer first encodes every
+#' column of pixels of shape (28, 1) to a column vector of shape (128,). The second LSTM
+#' layer encodes then these 28 column vectors of shape (28, 128) to a image vector
+#' representing the whole image. A final Dense layer is added for prediction.
+#' 
+#' After 5 epochs: train acc: 0.9858, val acc: 0.9864
+#'
+
+#' 
+#' IMPORTANT NOTE: This example does net work correctly with the version of 
+#' Keras integrated with TensorFlow (the Python variation doesn't work 
+#' either). Therefore, we shouldn't yet add this to the list of published
+#' examples.
+#' 
+
 library(keras)
+
+# Training parameters.
+batch_size <- 32
+num_classes <- 10
+epochs <- 5
+
+# Embedding dimensions.
+row_hidden <- 128
+col_hidden <- 128
+
+# the data, shuffled and split between train and test sets
+mnist <- dataset_mnist()
+x_train <- mnist$train$x
+y_train <- mnist$train$y
+x_test <- mnist$test$x
+y_test <- mnist$test$y
+
+# Reshapes data to 4D for Hierarchical RNN.
+x_train <- array(as.numeric(x_train), dim = c(dim(x_train)[[1]], 28, 28, 1))
+x_test <- array(as.numeric(x_test), dim = c(dim(x_test)[[1]], 28, 28, 1))
+x_train <- x_train / 255
+x_test <- x_test / 255
+
+dim_x_train <- dim(x_train)
+cat('x_train_shape:', dim_x_train)
+cat(dim_x_train[[1]], 'train samples')
+cat(dim(x_test)[[1]], 'test samples')
+
+# Converts class vectors to binary class matrices
+y_train <- to_categorical(y_train, num_classes)
+y_test <- to_categorical(y_test, num_classes)
+
+row <- dim_x_train[[2]]
+col <- dim_x_train[[3]]
+pixel <- dim_x_train[[4]]
+
+# Model input (4D)
+input <- layer_input(shape = c(row, col, pixel))
+
+# Encodes a row of pixels using TimeDistributed Wrapper
+encoded_rows <- input %>% time_distributed(layer_lstm(units = row_hidden))
+
+# Encodes columns of encoded rows.
+encoded_columns <- encoded_rows %>% layer_lstm(units = col_hidden)
+
+# Model output
+prediction <- encoded_columns %>%
+  layer_dense(units = num_classes, activation = 'softmax')
+
+# Define and compile model
+model <- keras_model(input, prediction)
+model %>% compile(
+  loss = 'categorical_crossentropy',
+  optimizer = 'rmsprop',
+  metrics = c('accuracy')
+)
+
+# Training
+model %>% fit(
+  x_train, y_train,
+  batch_size = batch_size,
+  epochs = epochs,
+  verbose = 1,
+  validation_data = list(x_test, y_test)
+)
+
+# Evaluation
+scores <- model %>% evaluate(x_test, y_test, verbose = 0)
+cat('Test loss:', scores[[1]], '\n')
+cat('Test accuracy:', scores[2], '\n')
+
+
+
+