Example#1-10.py

import tensorflow as tf
import numpy as np

xy=np.loadtxt('data-04-zoo.csv', delimiter=',', dtype=np.float32)
x_data = xy[:, 0:-1]
y_data = xy[:, [-1]]

nb_classes = 7 # 0~6

X= tf.placeholder(tf.float32, [None, 16])
Y = tf.placeholder(tf.int32, [None, 1]) # 0~6, shape(?, 1)

Y_one_hot = tf.one_hot(Y, nb_classes) # one hot shape=(?, 1, 7)
Y_one_hot = tf.reshape(Y_one_hot, [-1, nb_classes]) # shape=(?, 7)

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(Logists) / reduce_sum(exp(Logists), dim)
logits = tf.matmul(X, W) + b
hypothesis = tf.nn.softmax(logits)

# Cross entropy cost/loss
# cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))

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.arg_max(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)))