simple nn

simple_neuralnet.py

%writefile ./package/simple_neuralnet.py
import tensorflow as tf

up = [i for i in range(10)]
down = [9-i for i in range(10)]

x = tf.constant([up if i%2 == 0 else down for i in range(1000)], tf.float32)
y_ = tf.constant([[1] if i%2 == 0 else [0] for i in range(1000)], tf.float32)

layer1 = tf.layers.dense(x, 10)
layer2 = tf.layers.dense(layer1, 10)
layer3 = tf.layers.dense(layer2, 10)
layer4 = tf.layers.dense(layer3, 10)
out = tf.layers.dense(layer4, 1)

loss = tf.losses.sigmoid_cross_entropy(y_, out)
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    
    for i in range(1000):
        _, _loss = sess.run([train_op, loss])
        if i%100 == 0:
            print("step: {}, loss: {}".format(i, _loss))