NN_basic_DNN.py

import tensorflow as tf
import numpy as np

x_data = np.array([[0,0],[1,0],[1,1],[0,0],[0,0],[0,1]])

y_data = np.array([
    [1,0,0],
    [0,1,0],
    [0,0,1],
    [1,0,0],
    [1,0,0],
    [0,0,1]])

X = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)

W1 = tf.Variable(tf.random_uniform([2,10],-1.,1.))
W2 = tf.Variable(tf.random_uniform([10,3],-1.,1.))

b1 = tf.Variable(tf.zeros([10]))
b2 = tf.Variable(tf.zeros([3]))

L1 = tf.add(tf.matmul(X,W1),b1)
L1 = tf.nn.relu(L1)


model = tf.add(tf.matmul(L1,W2),b2)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=Y,logits=model))

opt = tf.train.AdamOptimizer(learning_rate=0.01)
train_op = opt.minimize(cost)


init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for step in range(100):
    sess.run(train_op, feed_dict={X:x_data,Y:y_data})

    if (step+1)%10 == 0:
        print(step+1,sess.run(cost, feed_dict={X:x_data,Y:y_data}))


prediction = tf.argmax(model,1)
target = tf.argmax(Y,1)

print('예측값 : ', sess.run(prediction, feed_dict={X : x_data}))
print('실제값 : ', sess.run(target, feed_dict={Y : y_data}))

is_correct = tf.equal(prediction, target)
accurary = tf.reduce_mean(tf.cast(is_correct,tf.float32))
print('정확도 : %.2f' % sess.run(accurary*100, feed_dict={X : x_data,Y : y_data}))