구봄과 함께하는 softmax classfiy.sagews

import tensorflow as tf
tf.set_random_seed(777)  # for reproducibility
    

##xdata 점수   Y_-data : 등급

x_data = [[	9.6	,	10	,	4	,	4.2	,	9	,	7.36	],
[	2.4	,	2	,	0	,	2.4	,	0	,	1.36	],
[	6	,	2	,	7	,	5.4	,	6	,	5.28	],
[	7.8	,	10	,	8.5	,	1.8	,	2	,	6.02	],
[	4	,	10	,	8.5	,	4.8	,	9.5	,	7.36	],
[	9.6	,	10	,	7	,	6	,	10.5	,	8.62	],
[	6	,	8	,	4.5	,	1.8	,	6.5	,	5.36	]
]
y_data = [[0,1,0,0,0],
[0,0,0,1,0],
[0,0,1,0,0],
[0,0,1,0,0],
[0,1,0,0,0],
[1,0,0,0,0],
[0,0,1,0,0],
]

X = tf.placeholder("float", [None, 6])
Y = tf.placeholder("float", [None, 5])
nb_classes = 5

W = tf.Variable(tf.random_normal([6, 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=0.01).minimize(cost)

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

    for step in range(2401):
        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('--------------')
    
    
    L=[]
    for i in range(0,len(x_data )):
        L+=[sess.run(hypothesis, feed_dict={X: [x_data[i]]})]
        print(L[i], sess.run(tf.arg_max(L[i], 1)))
        
    a=sess.run(hypothesis, feed_dict={X: [[	6.8	,	10	,	3.5	,	4.8	,	10	,	7.02	]]})
    print(a, sess.run(tf.arg_max(a, 1)))        
        

(0, nan) (200, nan) (400, nan) (600, nan) (800, nan) (1000, nan) (1200, nan) (1400, nan) (1600, nan) (1800, nan) (2000, nan) (2200, nan) (2400, nan)

WARNING:tensorflow:From :11: arg_max (from tensorflow.python.ops.gen_math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `argmax` instead

-------------- (array([[nan, nan, nan, nan, nan]], dtype=float32), array([0])) (array([[nan, nan, nan, nan, nan]], dtype=float32), array([0])) (array([[nan, nan, nan, nan, nan]], dtype=float32), array([0])) (array([[nan, nan, nan, nan, nan]], dtype=float32), array([0])) (array([[nan, nan, nan, nan, nan]], dtype=float32), array([0])) (array([[nan, nan, nan, nan, nan]], dtype=float32), array([0])) (array([[nan, nan, nan, nan, nan]], dtype=float32), array([0])) (array([[nan, nan, nan, nan, nan]], dtype=float32), array([0]))