機器學習準確率accuracy的幾種程式碼結構參考
阿新 • • 發佈:2018-12-05
首先,牢記幾個函式,tf.equal(),tf.argmax(array, 1),tf.reduce_mean()
import tensorflow as tf
import numpy as np
A = [1,3,4,5,6]
B = [1,3,4,3,2]
with tf.Session() as sess:
print(sess.run(tf.equal(A, B)))
A == B
[ True True True False False]
False
A 和 B是 list,tensorflow支援list。
import numpy as np A = [1,3,4,5,6] B = [1,3,4,3,2] A = np.array(A) B = np.array(B) A == B
array([ True, True, True, False, False], dtype=bool)
tf.argmax(array, 1) 指的是 axis=1
tf.argmax(array)預設axis=0
程式碼核心結構如下
images = tf.placeholder(tf.float32, [None, 256, 256, 3], name='input_images') labels = tf.placeholder(tf.int64, [None], name='labels') logits, _ = MyNet(images, num_classes=N_CLASSES) correct_prediction = tf.equal(tf.argmax(logits, 1), labels) evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) with tf.Session() as sess: init=tf.global_variabels_initializer() sess.run(init) validation_accuracy = sess.run(evaluation_step, feed_dict={ images: validation_images, labels: validation_labels})
numpy 也能計算
X = tf.placeholder(tf.float32,[None,n_dim]) Y = tf.placeholder(tf.float32,[None,n_classes]) out = one_layer_net(w,b,X) evaluation_step = np.mean(np.argmax(out,axis=1) == np.argmax(train_Y,axis=1)) with tf.Session() as sess: init = tf.global_variables_intializer() sess.run(init) validation_accuracy = sess.run(evaluation_step, feed_dict={ X:validation_images,Y:validation_labels})
這裡值得一提的是輸入的資料,我們熟悉的輸入資料通常是訓練集的一個batch,而計算accuracy則是把全部資料輸入計算,因此,人們弄出一個驗證集validation set,通常比batch大而比訓練集小。驗證集可以是訓練集中挑選和重複運用,而測試集test set一般不能和訓練集重複。常用的比喻是:驗證集是平時的模擬考試,而測試集是高考。