溫馨提示×
您好，登錄后才能下訂單哦！
點擊 登錄注冊 即表示同意《億速云用戶服務條款》
您好，登錄后才能下訂單哦！
說明
本例子利用TensorFlow搭建一個全連接神經網絡，實現對MNIST手寫數字的識別。
先上代碼
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
# prepare data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
xs = tf.placeholder(tf.float32, [None, 784])
ys = tf.placeholder(tf.float32, [None, 10])
# the model of the fully-connected network
weights = tf.Variable(tf.random_normal([784, 10]))
biases = tf.Variable(tf.zeros([1, 10]) + 0.1)
outputs = tf.matmul(xs, weights) + biases
predictions = tf.nn.softmax(outputs)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(predictions),
reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# compute the accuracy
correct_predictions = tf.equal(tf.argmax(predictions, 1), tf.argmax(ys, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={
xs: batch_xs,
ys: batch_ys
})
if i % 50 == 0:
print(sess.run(accuracy, feed_dict={
xs: mnist.test.images,
ys: mnist.test.labels
}))
代碼解析
1. 讀取MNIST數據
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
2. 建立占位符
xs = tf.placeholder(tf.float32, [None, 784]) ys = tf.placeholder(tf.float32, [None, 10])
xs 代表圖片像素數據, 每張圖片(28×28)被展開成(1×784), 有多少圖片還未定, 所以shape為None×784.
ys 代表圖片標簽數據, 0-9十個數字被表示成One-hot形式, 即只有對應bit為1, 其余為0.
3. 建立模型
weights = tf.Variable(tf.random_normal([784, 10]))
biases = tf.Variable(tf.zeros([1, 10]) + 0.1)
outputs = tf.matmul(xs, weights) + biases
predictions = tf.nn.softmax(outputs)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(predictions),
reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
使用Softmax函數作為激活函數：
4. 計算正確率
correct_predictions = tf.equal(tf.argmax(predictions, 1), tf.argmax(ys, 1)) accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
5. 使用模型
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={
xs: batch_xs,
ys: batch_ys
})
if i % 50 == 0:
print(sess.run(accuracy, feed_dict={
xs: mnist.test.images,
ys: mnist.test.labels
}))
運行結果
訓練1000個循環, 準確率在87%左右.
Extracting MNIST_data/train-images-idx3-ubyte.gz Extracting MNIST_data/train-labels-idx1-ubyte.gz Extracting MNIST_data/t10k-images-idx3-ubyte.gz Extracting MNIST_data/t10k-labels-idx1-ubyte.gz 0.1041 0.632 0.7357 0.7837 0.7971 0.8147 0.8283 0.8376 0.8423 0.8501 0.8501 0.8533 0.8567 0.8597 0.8552 0.8647 0.8654 0.8701 0.8712 0.8712
以上這篇使用TensorFlow搭建一個全連接神經網絡教程就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持億速云。
免責聲明：本站發布的內容（圖片、視頻和文字）以原創、轉載和分享為主，文章觀點不代表本網站立場，如果涉及侵權請聯系站長郵箱：is@yisu.com進行舉報，并提供相關證據，一經查實，將立刻刪除涉嫌侵權內容。
