TensorFlow(三)——MNIST分類
阿新 • • 發佈:2018-12-09
import input_data import tensorflow as tf mnist = input_data.read_data_sets('data/', one_hot=True) #定義迴歸模型 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 #定義損失函式和優化器 y_ = tf.placeholder(tf.float32, [None, 10]) #用tf.nn.softmax_cross_entropy_with_logits計算預測值y和真值y_的差值,並取均值 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_)) #採用SGD作為優化器 train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) #使用InteractiveSession()建立互動式上下文的TensorFlow會話,可以先定義會話再定義操作 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}) #評估訓練好的模型 #計算預測值和真實值 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) #布林轉化為浮點,取平均值,得到準確率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) #計算在測試集上的準確率 print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
結果:
0.9154