get accuracy

get_accuracy.py

import tensorflow as tf

samples = 1000
up = [i for i in range(10)]
down = [9-i for i in range(10)]
data = [up if i%2 == 0 else down for i in range(samples)]
label = [[1] if i%2 == 0 else [0] for i in range(samples)]

tf.reset_default_graph()
x = tf.placeholder(tf.float32, shape=[None, 10])
y_ = tf.placeholder(tf.int32)

layer1 = tf.layers.dense(x, 20)
layer2 = tf.layers.dense(layer1, 15)
layer3 = tf.layers.dense(layer2, 30)
layer4 = tf.layers.dense(layer3, 10)
layer5 = tf.layers.dense(layer4, 5)
out = tf.layers.dense(layer5, 1)

pred = tf.cast(tf.round(tf.nn.sigmoid(out)), tf.int32)
y_ = tf.cast(y_, tf.int32)
comp = tf.cast(tf.equal(pred, y_), tf.float32)
accuracy = tf.reduce_mean(comp)

saver = tf.train.Saver()

with tf.Session() as sess:
    saver.restore(sess, "./checkpoint/simple_neuralnet")
    _pred, _acc = sess.run([pred, accuracy], {x: data, y_: label})
    print("prediction: {}".format(_pred[:10]))
    print("accuracy: ", _acc)