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"""A very simple MNIST classifier.
See extensive documentation at
Modified with labels to be compatible with inference in go.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
FLAGS = None
def main(_):
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
# GOLANG note that we must label the input-tensor!
x = tf.placeholder(tf.float32, [None, 784], name="imageinput")
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.add(tf.matmul(x, W) , b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.InteractiveSession()
# Train
for _ in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100), feed_dict={x: batch_xs, y_: batch_ys})
# GOLANG note that we must label the infer-operation!!
infer = tf.argmax(y,1, name="infer")
correct_prediction = tf.equal(infer, tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(, feed_dict={x: mnist.test.images,
y_: mnist.test.labels}))
builder = tf.saved_model.builder.SavedModelBuilder("mnistmodel")
# GOLANG note that we must tag our model so that we can retrieve it at inference-time
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',
help='Directory for storing input data')
FLAGS, unparsed = parser.parse_known_args(), argv=[sys.argv[0]] + unparsed)