Source: https://github.com/rstudio/tensorflow/blob/master/inst/examples/mnist/mnist_softmax.R
# mnist_softmax.R library(tensorflow) # Create the model x <- tf$placeholder(tf$float32, shape(NULL, 784L)) W <- tf$Variable(tf$zeros(shape(784L, 10L))) b <- tf$Variable(tf$zeros(shape(10L))) y <- tf$nn$softmax(tf$matmul(x, W) + b) # Define loss and optimizer y_ <- tf$placeholder(tf$float32, shape(NULL, 10L)) cross_entropy <- tf$reduce_mean(-tf$reduce_sum(y_ * log(y), reduction_indices=1L)) train_step <- tf$train$GradientDescentOptimizer(0.5)$minimize(cross_entropy) # Create session and initialize variables sess <- tf$Session() sess$run(tf$global_variables_initializer()) # Load mnist data ) datasets <- tf$contrib$learn$datasets mnist <- datasets$mnist$read_data_sets("MNIST-data", one_hot = TRUE) # Train for (i in 1:1000) { batches <- mnist$train$next_batch(100L) batch_xs <- batches[[1]] batch_ys <- batches[[2]] sess$run(train_step, feed_dict = dict(x = batch_xs, y_ = batch_ys)) } # Test trained model correct_prediction <- tf$equal(tf$argmax(y, 1L), tf$argmax(y_, 1L)) accuracy <- tf$reduce_mean(tf$cast(correct_prediction, tf$float32)) sess$run(accuracy, feed_dict = dict(x = mnist$test$images, y_ = mnist$test$labels))
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