TensorFlow實作最小化損失函數：交叉熵

softmax_data = [0.7, 0.2, 0.1]
one_hot_data = [1.0, 0.0, 0.0]

softmax_data = tf.placeholder(tf.float32)
one_hot = tf.placeholder(tf.float32)
cross_entropy = -tf.reduce_sum(one_hot*tf.log(softmax_data))
with tf.Session() as sess:
	result = sess.run(cross_entropy,feed_dict={one_hot:one_hot_data,softmax_data:softmax_data})
	print(result)