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# ## lab 06-1: TensorFlow로 Softmax Classification의 구현하기 (new)
#
# ### Softmax function
# `n`개의 값을 예측할 때 사용<br/>
# 점수 -> Softmax function(점수) -> 확률<br/>
# `y_data`를 **One Hot Encoding**형식으로 사용

# +
import tensorflow as tf

x_data = [
    [1, 2, 1, 1],
    [2, 1, 3, 2],
    [3, 1, 3, 4],
    [4, 1, 5, 5],
    [1, 7, 5, 5],
    [1, 2, 5, 6],
    [1, 6, 6, 6],
    [1, 7, 7, 7],
]
y_data = [
    [0, 0, 1],
    [0, 0, 1],
    [0, 0, 1],
    [0, 1, 0],
    [0, 1, 0],
    [0, 1, 0],
    [1, 0, 0],
    [1, 0, 0],
]

X = tf.placeholder("float", [None, 4])
Y = tf.placeholder("float", [None, 3])
nb_classes = 3

W = tf.Variable(tf.random_normal([4, 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)
hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)

# Cross entropy cost / Loss
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)

# Launch Graph
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    
    for step in range(2001):
        sess.run(optimizer, feed_dict={X: x_data, Y: y_data})
        
        if step % 200 == 0:
            print(step, sess.run(cost, feed_dict={X: x_data, Y: y_data}))
            
    print('--------------')
    # Testing & One-hot encoding
    a = sess.run(hypothesis, feed_dict={X: [[1, 11, 7, 9]]})
    print(a, sess.run(tf.argmax(a, 1)))

    print('--------------')
    b = sess.run(hypothesis, feed_dict={X: [[1, 3, 4, 3]]})
    print(b, sess.run(tf.argmax(b, 1)))

    print('--------------')
    c = sess.run(hypothesis, feed_dict={X: [[1, 1, 0, 1]]})
    print(c, sess.run(tf.argmax(c, 1)))

    print('--------------')
    all = sess.run(hypothesis, feed_dict={X: [[1, 11, 7, 9], [1, 3, 4, 3], [1, 1, 0, 1]]})
    print(all, sess.run(tf.argmax(all, 1)))

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