DeepLearning_Lab06 - 8BitsCoding/RobotMentor GitHub Wiki

# Lab 6 Softmax Classifier
import tensorflow as tf
tf.set_random_seed(777)  # for reproducibility

impoty tensorflow and make seed

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])

input data

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)

물론 softmax를 직접 구현해도 되지만... 그럴꺼 까지야??

# 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)

cost를 구하고 GradientDescentOptimizer로 minimizse

# Launch graph
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    for step in range(2001):
            _, cost_val = sess.run([optimizer, cost], feed_dict={X: x_data, Y: y_data})

            if step % 200 == 0:
                print(step, cost_val)

    print('--------------')
    # Testing & One-hot encoding
    a = sess.run(hypothesis, feed_dict={X: [1, 11, 7, 9](/8BitsCoding/RobotMentor/wiki/1,-11,-7,-9)})
    print(a, sess.run(tf.argmax(a, 1)))

    print('--------------')
    b = sess.run(hypothesis, feed_dict={X: [1, 3, 4, 3](/8BitsCoding/RobotMentor/wiki/1,-3,-4,-3)})
    print(b, sess.run(tf.argmax(b, 1)))

    print('--------------')
    c = sess.run(hypothesis, feed_dict={X: [1, 1, 0, 1](/8BitsCoding/RobotMentor/wiki/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](/8BitsCoding/RobotMentor/wiki/1,-11,-7,-9],-[1,-3,-4,-3],-[1,-1,-0,-1)})
    print(all, sess.run(tf.argmax(all, 1)))

DeepLearning_Lab06 - 8BitsCoding/RobotMentor GitHub Wiki

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