TF:利用是Softmax回归+GD算法实现MNIST手写数字图片识别(10000张图片测试得到的准确率为92%)
目录
- TF:利用是Softmax回归+GD算法实现手写数字识别(10000张图片测试得到的准确率为92%)
- 思路:对输入的图像,计算它属于每个类别的概率,找出最大概率即为预测值
- import tensorflow as tf
- from tensorflow.examples.tutorials.mnist import input_data
-
-
- mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)读入MNIST数据
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-
- 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)))
- train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
-
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- sess = tf.InteractiveSession()
- tf.global_variables_initializer().run()
- print('start training...')
-
-
- for _ 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})) 0.9185
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TF:利用是Softmax回归+GD算法实现MNIST手写数字识别(10000张图片测试得到的准确率为92%)
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