MNIST - MLP
Biostat 203B
Dr. Hua Zhou @ UCLA
3/10/2020
sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS High Sierra 10.13.6
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## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
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## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## loaded via a namespace (and not attached):
## [1] compiler_4.0.2 magrittr_1.5 tools_4.0.2 htmltools_0.5.0
## [5] yaml_2.2.1 stringi_1.5.3 rmarkdown_2.3 knitr_1.30
## [9] stringr_1.4.0 xfun_0.17 digest_0.6.25 rlang_0.4.7
## [13] evaluate_0.14Source: https://tensorflow.rstudio.com/guide/keras/examples/mnist_mlp/
In this example, we train an MLP (multi-layer perceptron) on the MNIST data set. Achieve testing accuracy 98.11% after 30 epochs.
The MNIST database (Modified National Institute of Standards and Technology database) is a large database of handwritten digits (\(28 \times 28\)) that is commonly used for training and testing machine learning algorithms.
60,000 training images, 10,000 testing images.
Prepare data
Aquire data:
library(keras)
mnist <- dataset_mnist()
x_train <- mnist$train$x
y_train <- mnist$train$y
x_test <- mnist$test$x
y_test <- mnist$test$yTraining set:
dim(x_train)## [1] 60000 28 28dim(y_train)## [1] 60000x_train[1, , ]## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## [1,] 0 0 0 0 0 0 0 0 0 0 0 0 0
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## [28,] 0 0 0image(t(x_train[1, 28:1,]), useRaster=TRUE, axes=FALSE, col=grey(seq(0, 1, length = 256)))
y_train[1]## [1] 5Testing set:
dim(x_test)## [1] 10000 28 28dim(y_test)## [1] 10000Vectorize \(28 \times 28\) images into \(784\)-vectors and scale entries to [0, 1]:
# reshape
x_train <- array_reshape(x_train, c(nrow(x_train), 784))
x_test <- array_reshape(x_test, c(nrow(x_test), 784))
# rescale
x_train <- x_train / 255
x_test <- x_test / 255
dim(x_train)## [1] 60000 784dim(x_test)## [1] 10000 784Encode \(y\) as binary class matrix:
y_train <- to_categorical(y_train, 10)
y_test <- to_categorical(y_test, 10)
dim(y_train)## [1] 60000 10dim(y_test)## [1] 10000 10head(y_train)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 0 0 0 0 0 1 0 0 0 0
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## [4,] 0 1 0 0 0 0 0 0 0 0
## [5,] 0 0 0 0 0 0 0 0 0 1
## [6,] 0 0 1 0 0 0 0 0 0 0Define the model
Define a sequential model (a linear stack of layers) with 2 fully-connected hidden layers (256 and 128 neurons):
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')
summary(model)## Model: "sequential"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## dense (Dense) (None, 256) 200960
## ________________________________________________________________________________
## dropout (Dropout) (None, 256) 0
## ________________________________________________________________________________
## dense_1 (Dense) (None, 128) 32896
## ________________________________________________________________________________
## dropout_1 (Dropout) (None, 128) 0
## ________________________________________________________________________________
## dense_2 (Dense) (None, 10) 1290
## ================================================================================
## Total params: 235,146
## Trainable params: 235,146
## Non-trainable params: 0
## ________________________________________________________________________________Compile the model with appropriate loss function, optimizer, and metrics:
model %>% compile(
loss = 'categorical_crossentropy',
optimizer = optimizer_rmsprop(),
metrics = c('accuracy')
)Training and validation
system.time({
history <- model %>% fit(
x_train, y_train,
epochs = 30, batch_size = 128,
validation_split = 0.2
)
})## user system elapsed
## 226.844 37.107 78.676plot(history)## `geom_smooth()` using formula 'y ~ x'
Testing
Evaluate model performance on the test data:
model %>% evaluate(x_test, y_test)## loss accuracy
## 0.1180085 0.9804000Generate predictions on new data:
model %>% predict_classes(x_test) %>% head()## [1] 7 2 1 0 4 1Exercise
Suppose we want to fit a multinomial-logit model and use it as a baseline method to neural networks. How to do that? Of course we can use mlogit or other packages. Instead we can fit the same model using keras, since multinomial-logit is just an MLP with (1) one input layer with linear activation and (2) one output layer with softmax link function.
# set up model
library(keras)
mlogit <- keras_model_sequential()
mlogit %>%
# layer_dense(units = 256, activation = 'linear', input_shape = c(784)) %>%
# layer_dropout(rate = 0.4) %>%
layer_dense(units = 10, activation = 'softmax', input_shape = c(784))
summary(mlogit)## Model: "sequential_1"
## ________________________________________________________________________________
## Layer (type) Output Shape Param #
## ================================================================================
## dense_3 (Dense) (None, 10) 7850
## ================================================================================
## Total params: 7,850
## Trainable params: 7,850
## Non-trainable params: 0
## ________________________________________________________________________________# compile model
mlogit %>% compile(
loss = 'categorical_crossentropy',
optimizer = optimizer_rmsprop(),
metrics = c('accuracy')
)# fit model
mlogit_history <- mlogit %>% fit(
x_train, y_train,
epochs = 20, batch_size = 128,
validation_split = 0.2
)# Evaluate model performance on the test data:
mlogit %>% evaluate(x_test, y_test)## loss accuracy
## 0.2715999 0.9268000Generate predictions on new data:
mlogit %>% predict_classes(x_test) %>% head()## [1] 7 2 1 0 4 1Experiment: Change the linear activation to relu in the multinomial-logit model and see the change in classification accuracy.