Tensorflow Relu的误解 [英] Tensorflow Relu Misunderstanding

问题描述

我最近正在上一门Udacity深度学习课程，该课程基于 TensorFlow 。我有一个简单的 MNIST 程序，准确度约为92％：

I've recently been doing a Udacity Deep Learning course which is based around TensorFlow. I have a simple MNIST program which is about 92% accurate:

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x, W) + b)

y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) 

我将其分配给，将带有SGD的逻辑回归示例转换为具有隐藏线性单元nn.relu（）和1024个隐藏节点的1-隐藏层神经网络

我对此有一个心理障碍。目前，我有一个784 x 10的权重矩阵和一个10个元素的长偏差矢量。我不明白如何将所得的10个元素矢量从 WX +偏置连接到1024 Relu s。

I am having a mental block about this. Currently I have a 784 x 10 Matrix of weights, and a 10 element long bias vector. I don't understand how I connect the resulting 10 element vector from WX + Bias to 1024 Relus.

如果有人可以向我解释这一点，我将非常感激。

If anyone could explain this to me I'd be very grateful.

推荐答案

现在您有类似的东西

，您需要这样的东西

（此图缺少ReLU层，该层在+ b1）

(this diagram is missing ReLU layer which goes after +b1)

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