Question

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

# Training and Test datasets

x_data = [[1,2,1], [1,3,2], [1,3,4], [1,5,5], [1,7,5], [1,2,5], [1,6,6], [1,7,7]]
y_data = [[0,0,1], [0,0,1], [0,0,1], [0,1,0], [0,1,0], [0,1,0], [1,0,0], [1,0,0]]

x_test = [[2,1,1], [3,1,2], [3,3,4]]
y_test = [[0,0,1], [0,0,1], [0,0,1]]

X = tf.placeholder("float", [None, 3])
Y = tf.placeholder("float", [None, 3])
W = tf.Variable(tf.random_normal([3,3]))
b = tf.Variable(tf.random_normal([3]))

hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)
cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)

prediction = tf.arg_max(hypothesis, 1)
is_correct = tf.equal(prediction, tf.arg_max(Y, 1))
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))

with tf.Session() as sess :
    sess.run(tf.global_variables_initializer())
    for step in range(201) :
        cost_val, W_val, _ = sess.run([cost, W, optimizer], feed_dict={X: x_data, Y: y_data})

        print(step, cost_val, W_val)
        print("Prediction:", sess.run(prediction, feed_dict={X: x_test}))
        print("Accuracy:", sess.run(accuracy, feed_dict={X: x_test, Y: y_test}))

如何根据上述成本计算Y * tf.log（假设）？它们的形状均为8和3，所以我想知道它们是如何计算的。

张量流中softmax的成本函数

0 个答案: