Testing Keras for R

library(keras)
mnist <- dataset_mnist()

x_train <- mnist$train$x
y_train <- mnist$train$y
x_test <- mnist$test$x
y_test <- mnist$test$y

The x data is a 3-d array (images,width,height) of grayscale values . To prepare the data for training we convert the 3-d arrays into matrices by reshaping width and height into a single dimension (28x28 images are flattened into length 784 vectors). Then, we convert the grayscale values from integers ranging between 0 to 255 into floating point values ranging between 0 and 1:

# reshape
dim(x_train) <- c(nrow(x_train), 784)
dim(x_test) <- c(nrow(x_test), 784)
# rescale
x_train <- x_train / 255
x_test <- x_test / 255

The y data is an integer vector with values ranging from 0 to 9. To prepare this data for training we one-hot encode the vectors into binary class matrices using the Keras to_categorical() function:

y_train <- to_categorical(y_train, 10)
y_test <- to_categorical(y_test, 10)

Defining the Model

The core data structure of Keras is a model, a way to organize layers. The simplest type of model is the Sequential model, a linear stack of layers.

We begin by creating a sequential model and then adding layers using the pipe (%>%) operator:

model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 256, activation = 'relu', input_shape = c(784)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dropout(rate = 0.3) %>%
  layer_dense(units = 10, activation = 'softmax')

The input_shape argument to the first layer specifies the shape of the input data (a length 784 numeric vector representing a grayscale image). The final layer outputs a length 10 numeric vector (probabilities for each digit) using a softmax activation function.

Use the summary() function to print the details of the model:

summary(model)

________________________________________________________________________________________________________________________________________________________________________________________________________
Layer (type)                                                                              Output Shape                                                                    Param #                       
========================================================================================================================================================================================================
dense_7 (Dense)                                                                           (None, 256)                                                                     200960                        
________________________________________________________________________________________________________________________________________________________________________________________________________
dropout_5 (Dropout)                                                                       (None, 256)                                                                     0                             
________________________________________________________________________________________________________________________________________________________________________________________________________
dense_8 (Dense)                                                                           (None, 128)                                                                     32896                         
________________________________________________________________________________________________________________________________________________________________________________________________________
dropout_6 (Dropout)                                                                       (None, 128)                                                                     0                             
________________________________________________________________________________________________________________________________________________________________________________________________________
dense_9 (Dense)                                                                           (None, 10)                                                                      1290                          
========================================================================================================================================================================================================
Total params: 235,146
Trainable params: 235,146
Non-trainable params: 0
________________________________________________________________________________________________________________________________________________________________________________________________________

Next, compile the model with appropriate loss function, optimizer, and metrics:

model %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = optimizer_rmsprop(),
  metrics = c('accuracy')
)

Training and Evaluation

Use the fit() function to train the model for 30 epochs using batches of 128 images:

history <- model %>% fit(
  x_train, y_train, 
  epochs = 30, batch_size = 128, 
  validation_split = 0.2
)

The history object returned by fit() includes loss and accuracy metrics which we can plot:

plot(history)

Evaluate the model’s performance on the test data:

model %>% evaluate(x_test, y_test)

   32/10000 [..............................] - ETA: 3s
  736/10000 [=>............................] - ETA: 0s
 1440/10000 [===>..........................] - ETA: 0s
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 3648/10000 [=========>....................] - ETA: 0s
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 5056/10000 [==============>...............] - ETA: 0s
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 9344/10000 [===========================>..] - ETA: 0s
10000/10000 [==============================] - 0s     
$loss
[1] 0.1094651

$acc
[1] 0.9785

Generate predictions on new data

model %>% predict_classes(x_test)

   [1] 7 2 1 0 4 1 4 9 6 9 0 6 9 0 1 5 9 7 3 4 9 6 6 5 4 0 7 4 0 1 3 1 3 4 7 2 7 1 2 1 1 7 4 2 3 5 1 2 4 4 6 3 5 5 6 0 4 1 9 5 7 8 9 3 7 4 6 4 3 0 7 0 2 9 1 7 3 2 9 7 7 6 2 7 8 4 7 3 6 1 3 6 9 3 1 4 1
  [98] 7 6 9 6 0 5 4 9 9 2 1 9 4 8 7 3 9 7 4 4 4 9 2 5 4 7 6 7 9 0 5 8 5 6 6 5 7 8 1 0 1 6 4 6 7 3 1 7 1 8 2 0 3 9 9 5 5 1 5 6 0 3 4 4 6 5 4 6 5 4 5 1 4 4 7 2 3 2 7 1 8 1 8 1 8 5 0 8 9 2 5 0 1 1 1 0 9
 [195] 0 3 1 6 4 2 3 6 1 1 1 3 9 5 2 9 4 5 9 3 9 0 3 6 5 5 7 2 2 7 1 2 8 4 1 7 3 3 8 8 7 9 2 2 4 1 5 9 8 7 2 3 0 6 4 2 4 1 9 5 7 7 2 8 2 0 8 5 7 7 9 1 8 1 8 0 3 0 1 9 9 4 1 8 2 1 2 9 7 5 9 2 6 4 1 5 8
 [292] 2 9 2 0 4 0 0 2 8 4 7 1 2 4 0 2 7 4 3 3 0 0 3 1 9 6 5 2 5 9 7 9 3 0 4 2 0 7 1 1 2 1 5 3 3 9 7 8 6 3 6 1 3 8 1 0 5 1 3 1 5 5 6 1 8 5 1 7 9 4 6 2 2 5 0 6 5 6 3 7 2 0 8 8 5 4 1 1 4 0 3 3 7 6 1 6 2
 [389] 1 9 2 8 6 1 9 5 2 5 4 4 2 8 3 8 2 4 5 0 3 1 7 7 5 7 9 7 1 9 2 1 4 2 9 2 0 4 9 1 4 8 1 8 4 5 9 8 8 3 7 6 0 0 3 0 2 0 6 9 9 3 3 3 2 3 9 1 2 6 8 0 5 6 6 6 3 8 8 2 7 5 8 9 6 1 8 4 1 2 5 9 1 9 7 5 4
 [486] 0 8 9 9 1 0 5 2 3 7 0 9 4 0 6 3 9 5 2 1 3 1 3 6 5 7 4 2 2 6 3 2 6 5 4 8 9 7 1 3 0 3 8 3 1 9 3 4 4 6 4 2 1 8 2 5 4 8 8 4 0 0 2 3 2 7 3 0 8 7 4 4 7 9 6 9 0 9 8 0 4 6 0 6 3 5 4 8 3 3 9 3 3 3 7 8 0
 [583] 2 2 1 7 0 6 5 4 3 8 0 9 6 3 8 0 9 9 6 8 6 8 5 7 8 6 0 2 4 0 2 2 3 1 9 7 5 2 0 8 4 6 2 6 7 9 3 2 9 8 2 2 9 2 7 3 5 9 1 8 0 2 0 5 2 1 3 7 6 7 1 2 5 8 0 3 7 2 4 0 9 1 8 6 7 7 4 3 4 9 1 9 5 1 7 3 9
 [680] 7 6 9 1 3 3 8 3 3 6 7 2 4 5 8 5 1 1 4 4 3 1 0 7 7 0 7 9 9 4 8 5 5 4 0 8 2 1 0 8 4 8 0 4 0 6 1 9 3 2 6 7 2 6 9 3 1 4 6 2 5 9 2 0 6 2 1 7 3 4 1 0 5 4 3 1 1 7 4 9 9 4 8 4 0 2 4 5 1 1 6 4 7 1 9 4 2
 [777] 4 1 5 5 3 8 3 1 4 5 6 8 9 4 1 5 3 8 0 3 2 5 1 2 8 3 4 4 0 8 8 3 3 1 2 3 5 9 6 3 2 6 1 3 6 0 7 2 1 7 1 4 2 4 2 1 7 9 6 1 1 2 4 3 1 7 7 4 8 0 7 3 1 3 1 0 7 7 0 3 5 5 2 7 6 6 9 2 8 3 5 2 2 5 6 0 8
 [874] 2 9 2 8 8 8 8 7 4 9 3 0 6 6 3 2 1 3 2 2 9 3 0 0 5 7 8 3 4 4 6 0 2 9 1 4 7 4 7 3 9 8 8 4 7 1 2 1 2 2 3 2 3 2 3 9 1 7 4 0 3 5 5 8 6 3 2 6 7 6 6 3 2 7 8 1 1 7 4 6 4 9 5 1 3 3 4 7 8 9 1 1 0 9 1 4 4
 [971] 5 4 0 6 2 2 3 1 5 1 2 0 3 8 1 2 6 7 1 6 2 3 9 0 1 2 2 0 8 9
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