Tensorflow mnist 資料集測試程式碼 + 自己下載資料
阿新 • • 發佈:2019-01-27
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # 自己下載 MNIST_data 資料集, csdn 上下載很快 mnist_data_folder="/home/zhangjun/miniconda3/envs/tensorflow/MNIST_data" mnist=input_data.read_data_sets(mnist_data_folder,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.matmul(x, W) + b # Define loss and optimizer y_ = tf.placeholder(tf.float32, [None, 10]) # The raw formulation of cross-entropy, # # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)), # reduction_indices=[1])) # # can be numerically unstable. # # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw # outputs of 'y', and then average across the batch. cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) sess = tf.InteractiveSession() tf.global_variables_initializer().run() # Train 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}) # Test trained model 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}))