vignettes/train-with-tensorflow/tf_mnist.R

In azuremlsdk: Interface to the 'Azure Machine Learning' 'SDK'

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
# Copyright 2016 RStudio, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================


library(tensorflow)

library(azuremlsdk)

# 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))
cat("Accuracy: ", sess$run(accuracy,
                           feed_dict = dict(x = mnist$test$images,
                                            y_ = mnist$test$labels)))

log_metric_to_run("accuracy",
                  sess$run(accuracy, feed_dict = dict(x = mnist$test$images,
                                                      y_ = mnist$test$labels)))