import tensorflow as tf
learning_rate = 0.01
training_epochs = 1000
num_labels = 3
batch_size = 100
X = tf.placeholder(tf.float32, shape=[None, num_features])
Y = tf.placeholder(tf.float32, shape=[None, num_labels])
w=tf.Variable(tf.zeros[num_features, num_labels])
b=tf.Variable(tf.zeros[num_labels])
y_model = tf.nn.softmax(tf.matmul(X, W) + b)
cost = -tf.reduce_sum(Y * tf.log(y_model))
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
correnct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
with tf.Session() as sess:
tf.grobal_variables_initializer().run()
fpr step in range(training_epochs * train_size // batch_size):
offset = (step * batch_size) % train_size
batch_xs = xs[offset: ( offset + batch_size), :]
batch_labels = labels[offset: (offset + batch_size)]
err, _ = sess.run([cost, train_op], feed_dict={X: batch_xs, Y: batch_labels})
if step % 100 == 0:
print(step, err)
W_val = sess.run(W)
print('w', W_val)
b_val = sess.run(b)
print('b', b_val)
print("accuracy", accuracy.eval(feed_dict={X: test_xs, Y: test_labels}))
