06-2. Softemax_zoo_classifier

■ 파일 처리 Softmax 구현

▶ 사용된 텐서플로우 API

tf.nn.softmax_cross_entropy_with_logits

tf.argmax

cost_i = tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=Y_one_hot)

- logits = tf.matmul(X,W)+ b 에서 바로 cost 함수를 얻는다.

- hypothesis = tf.nn.softmax(logits) 은 사용하지 않고 예측할 때 사용된다.

cost = tf.reduce_mean(cost_i)

- 코스트 함수의 평균을 구한다.

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)

- 학습을 진행한다.

prediction=tf.argmax(hypothesis,1)

- 예측을 진행한다. 1은 차원을 의미한다.(argmax 함수 공부 必)

correct_prediction = tf.equal(prediction,tf.argmax(Y_one_hot,1))

- 정답과 예측을 비교한다.

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

- 정확도를 구한다.

# Lab06-2 Softmax_zoo_classifier
 
import tensorflow as tf
 
import numpy as np
 
tf.set_random_seed(777)  # for reproducibility
 
 
# Predicting animal type based on various features
 
xy = np.loadtxt('data-04-zoo.csv', delimiter=',', dtype=np.float32)
 
x_data = xy[:, 0:-1]
 
y_data = xy[:, [-1]]
 
 
print(x_data.shape, y_data.shape)
 
 
nb_classes = 7  # 0 ~ 6
 
 
X = tf.placeholder(tf.float32, [None, 16])
 
Y = tf.placeholder(tf.int32, [None, 1])  # 0 ~ 6
 
Y_one_hot = tf.one_hot(Y, nb_classes)  # one hot
 
print("one_hot", Y_one_hot)
 
Y_one_hot = tf.reshape(Y_one_hot, [-1, nb_classes])
 
print("reshape", Y_one_hot)
 
 
W = tf.Variable(tf.random_normal([16, nb_classes]), name='weight')
 
b = tf.Variable(tf.random_normal([nb_classes]), name='bias')
 
 
# tf.nn.softmax computes softmax activations
 
# softmax = exp(logits) / reduce_sum(exp(logits), dim)
 
logits = tf.matmul(X, W) + b
 
hypothesis = tf.nn.softmax(logits)
 
 
# Cross entropy cost/loss
 
cost_i = tf.nn.softmax_cross_entropy_with_logits(logits=logits,
 
                                                 labels=Y_one_hot)
 
cost = tf.reduce_mean(cost_i)
 
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
 
 
prediction = tf.argmax(hypothesis, 1)
 
correct_prediction = tf.equal(prediction, tf.argmax(Y_one_hot, 1))
 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 
# Launch graph
 
with tf.Session() as sess:
 
    sess.run(tf.global_variables_initializer())
 
 
    for step in range(2000):
 
        sess.run(optimizer, feed_dict={X: x_data, Y: y_data})
 
        if step % 100 == 0:
 
            loss, acc = sess.run([cost, accuracy], feed_dict={
 
                                 X: x_data, Y: y_data})
 
            print("Step: {:5}\tLoss: {:.3f}\tAcc: {:.2%}".format(
 
                step, loss, acc))
 
 
    # Let's see if we can predict
 
    pred = sess.run(prediction, feed_dict={X: x_data})
 
    # y_data: (N,1) = flatten => (N, ) matches pred.shape
 
    for p, y in zip(pred, y_data.flatten()):
 
        print("[{}] Prediction: {} True Y: {}".format(p == int(y), p, int(y)))