tensorflow怎么搭建bp神经网络

?TensorFlow???BP?????????:

1. ??????:

import tensorflow as tf

2. ?????????????:

X = tf.placeholder(tf.float32, [None, input_size]) # ????????,None?????????????
Y = tf.placeholder(tf.float32, [None, output_size]) # ????????

3. ?????????:

W = tf.Variable(tf.random_normal([input_size, hidden_size])) # ????????????
b = tf.Variable(tf.random_normal([hidden_size])) # ????????
V = tf.Variable(tf.random_normal([hidden_size, output_size])) # ????????????
c = tf.Variable(tf.random_normal([output_size])) # ????????

4. ?????????:

hidden_layer = tf.nn.sigmoid(tf.matmul(X, W) + b) # ??????
output_layer = tf.matmul(hidden_layer, V) + c # ??????

5. ??????????:

loss = tf.reduce_mean(tf.square(Y - output_layer)) # ????????????
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) # ????????????????

6. ????:

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(num_epochs):
        _, cost = sess.run([optimizer, loss], feed_dict={X: train_X, Y: train_Y})
        if epoch % display_step == 0:
            print("Epoch:", 'd' % (epoch+1), "cost=", "{:.9f}".format(cost))
    print("Optimization Finished!")

    # ????
    correct_prediction = tf.equal(tf.argmax(output_layer, 1), tf.argmax(Y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print("Accuracy:", accuracy.eval({X: test_X, Y: test_Y}))

??????,?????TensorFlow???????BP?????????????????????,???????????????????????????