tensorflow_practice2.py

import numpy as np
from sklearn.utils import shuffle
import tensorflow as tf

M = 2
K = 3
n = 100
N = n * K

X1 = np.random.randn(n, M) + np.array([0, 10])
X2 = np.random.randn(n, M) + np.array([5, 5])
X3 = np.random.randn(n, M) + np.array([10, 0])

Y1 = np.array([[1, 0, 0] for i in range(n)])
Y2 = np.array([[0, 1, 0] for i in range(n)])
Y3 = np.array([[0, 0, 1] for i in range(n)])

X = np.concatenate((X1, X2, X3), axis=0)
Y = np.concatenate((Y1, Y2, Y3), axis=0)

W = tf.Variable(tf.zeros([M, K]))
b = tf.Variable(tf.zeros([K]))

x = tf.placeholder(tf.float32, shape=[None, M])
t = tf.placeholder(tf.float32, shape=[None, K])
y = tf.nn.softmax(tf.matmul(x, W) + b)

cross_entropy = tf.reduce_mean(-tf.reduce_sum(t * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(t, 1))
batch_size = 50
n_batches = N // batch_size

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

for epoch in range(20):
    X_, Y_ = shuffle(X, Y)

    for i in range(n_batches):
        start = i * batch_size
        end = start * batch_size

        sess.run(train_step, feed_dict={x: X_[start:end], t: Y_[start:end]})

X_, Y_ = shuffle(X, Y)

classifed = correct_prediction.eval(session=sess, feed_dict={x: X_[0:10], t: Y_[0:10]})
prob = y.eval(session=sess, feed_dict={x: X_[0:10]})

print('classifed:')
print(classifed)
print()
print('output probability:')
print(prob)