Created
December 27, 2017 10:30
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基于 Softmax 的 Mnist 识别训练代码
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| from tensorflow.examples.tutorials.mnist import input_data | |
| mnist = input_data.read_data_sets("MNIST_data",one_hot=True) | |
| print(mnist.train.images.shape,mnist.train.labels.shape) | |
| print(mnist.test.images.shape,mnist.test.labels.shape) | |
| print(mnist.validation.images.shape,mnist.validation.labels.shape) | |
| import tensorflow as tf | |
| sess = tf.InteractiveSession() | |
| 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) | |
| tf.global_variables_initializer().run() | |
| for i in range(1000000): | |
| batch_xs,batch_ys = mnist.train.next_batch(100) | |
| train_step.run({x : batch_xs , y_:batch_ys}) | |
| if i % 1000 == 0: | |
| correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1)) | |
| accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) | |
| print(accuracy.eval({x:mnist.test.images, y_:mnist.test.labels})) |
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