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June 25, 2017 21:31
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Keras in RStudio
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## Simple example to create a VGG-like convolutional NN in R | |
## This requires the Keras library and a Tensorflow installation: | |
## https://rstudio.github.io/keras/index.html | |
## | |
## This tutorial was taken from: https://rstudio.github.io/keras/articles/sequential_model.html | |
library(keras) | |
# generate dummy data | |
x_train <- array(runif(100 * 100 * 100 * 3), dim = c(100, 100, 100, 3)) | |
y_train <- runif(100, min = 0, max = 9) %>% | |
round() %>% | |
matrix(nrow = 100, ncol = 1) %>% | |
to_categorical(num_classes = 10) | |
x_test <- array(runif(20 * 100 * 100 * 3), dim = c(20, 100, 100, 3)) | |
y_test <- runif(20, min = 0, max = 9) %>% | |
round() %>% | |
matrix(nrow = 20, ncol = 1) %>% | |
to_categorical(num_classes = 10) | |
# create model | |
model <- keras_model_sequential() | |
# define and compile model | |
# input: 100x100 images with 3 channels -> (100, 100, 3) tensors. | |
# this applies 32 convolution filters of size 3x3 each. | |
model %>% | |
layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu', | |
input_shape = c(100,100,3)) %>% | |
layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu') %>% | |
layer_max_pooling_2d(pool_size = c(2,2)) %>% | |
layer_dropout(rate = 0.25) %>% | |
layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>% | |
layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = 'relu') %>% | |
layer_max_pooling_2d(pool_size = c(2,2)) %>% | |
layer_dropout(rate = 0.25) %>% | |
layer_flatten() %>% | |
layer_dense(units = 256, activation = 'relu') %>% | |
layer_dropout(rate = 0.25) %>% | |
layer_dense(units = 10, activation = 'softmax') %>% | |
compile( | |
loss = 'categorical_crossentropy', | |
optimizer = optimizer_sgd(lr = 0.01, decay = 1e-6, momentum = 0.9, nesterov = TRUE) | |
) | |
# train | |
model %>% fit(x_train, y_train, batch_size = 32, epochs = 10) | |
# evaluate | |
score <- model %>% evaluate(x_test, y_test, batch_size = 32) |
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