如何在此Tensorflow多類SVM代碼中實現交叉驗證

Question

在以下鏈接提供的代碼中，我需要在訓練循環中添加10倍交叉驗證，但我對Tensorflow很陌生，而且我真的很難找到一種方法來做到這一點，但仍然不知道。

https://github.com/nfmcclure/tensorflow_cookbook/blob/master/04_Support_Vector_Machines/06_Implementing_Multiclass_SVMs/06_multiclass_svm.py

我適應所提供的代碼，以我的數據集和它運作良好，但我需要用相同的重採樣技術，以便利用評價Tensorflow的性能比較我的舊R代碼裏面表現Tensorflow性能GPU。

另外，我需要知道最終模型的參數以及驗證數據的預測。任何幫助，將不勝感激

感謝

編輯： 我試圖使用Kfold但問題是與代碼是爲多類SVM書寫方式。 y_vals的大小與類的數量（3）相同，但實際上並不是這樣。如果你可以看看上面的代碼或重現它，請明白我的意思。由於這個原因，我現在有這個錯誤：IndexError：index（樣本數量除以分割數）超出軸0的大小（分類數） 這裏是我修改的代碼kFold：

import matplotlib.pyplot as plt 
import numpy as np 
import tensorflow as tf 
from sklearn import datasets 
from tensorflow.python.framework import ops 
from sklearn.model_selection import KFold 
ops.reset_default_graph() 

# Create graph 
sess = tf.Session() 

# Load the data 
# iris.data = [(Sepal Length, Sepal Width, Petal Length, Petal Width)] 
iris = datasets.load_iris() 
x_vals = np.array([[x[0], x[3]] for x in iris.data]) 
y_vals1 = np.array([1 if y==0 else -1 for y in iris.target]) 
y_vals2 = np.array([1 if y==1 else -1 for y in iris.target]) 
y_vals3 = np.array([1 if y==2 else -1 for y in iris.target]) 
y_vals = np.array([y_vals1, y_vals2, y_vals3]) 

# Declare batch size 
batch_size = 50 

# Initialize placeholders 
x_data = tf.placeholder(shape=[None, 2], dtype=tf.float32) 
y_target = tf.placeholder(shape=[3, None], dtype=tf.float32) 
prediction_grid = tf.placeholder(shape=[None, 2], dtype=tf.float32) 


# Create variables for svm 
b = tf.Variable(tf.random_normal(shape=[3,batch_size])) 

# Gaussian (RBF) kernel 
gamma = tf.constant(-10.0) 
dist = tf.reduce_sum(tf.square(x_data), 1) 
dist = tf.reshape(dist, [-1,1]) 
sq_dists = tf.multiply(2., tf.matmul(x_data, tf.transpose(x_data))) 
my_kernel = tf.exp(tf.multiply(gamma, tf.abs(sq_dists))) 

# Declare function to do reshape/batch multiplication 
def reshape_matmul(mat): 
v1 = tf.expand_dims(mat, 1) 
v2 = tf.reshape(v1, [3, batch_size, 1]) 
return(tf.matmul(v2, v1)) 

# Compute SVM Model 
first_term = tf.reduce_sum(b) 
b_vec_cross = tf.matmul(tf.transpose(b), b) 
y_target_cross = reshape_matmul(y_target) 

second_term = tf.reduce_sum(tf.multiply(my_kernel, tf.multiply(b_vec_cross, 
y_target_cross)),[1,2]) 
loss = tf.reduce_sum(tf.negative(tf.subtract(first_term, second_term))) 

# Gaussian (RBF) prediction kernel 
rA = tf.reshape(tf.reduce_sum(tf.square(x_data), 1),[-1,1]) 
rB = tf.reshape(tf.reduce_sum(tf.square(prediction_grid), 1),[-1,1]) 
pred_sq_dist = tf.add(tf.subtract(rA, tf.multiply(2., tf.matmul(x_data, 
tf.transpose(prediction_grid)))), tf.transpose(rB)) 
pred_kernel = tf.exp(tf.multiply(gamma, tf.abs(pred_sq_dist))) 

prediction_output = tf.matmul(tf.multiply(y_target,b), pred_kernel) 
prediction = tf.arg_max(prediction_output- 
tf.expand_dims(tf.reduce_mean(prediction_output,1), 1), 0) 
accuracy = tf.reduce_mean(tf.cast(tf.equal(prediction, 
tf.argmax(y_target,0)), tf.float32)) 

# Declare optimizer 
my_opt = tf.train.GradientDescentOptimizer(0.01) 
train_step = my_opt.minimize(loss) 

# Initialize variables 
init = tf.global_variables_initializer() 
sess.run(init) 

# Training loop 
kf = KFold(n_splits=3) 

loss_vec = [] 
train_accuracy = [] 
valid_accuracy = [] 
x_trains = [] 
y_trains = [] 
x_tests = [] 
y_tests = [] 
for train_index, test_index in kf.split(x_vals): 
X_train, X_test = x_vals[train_index], x_vals[test_index] 
y_train, y_test = y_vals[train_index], y_vals[test_index] 
x_trains.append(X_train) 
y_trains.append(y_train) 
x_tests.append(X_test) 
y_tests.append(y_test) 
x_trains = np.asarray(x_trains) 
y_trains = np.asarray(y_trains) 
x_tests = np.asarray(x_tests) 
y_tests = np.asarray(y_tests) 
for i in range(100): 
rand_index = np.random.choice(len(x_trains), size=batch_size) 
rand_x = x_trains[rand_index] 
rand_y = y_trains[:,rand_index] 
sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y}) 

temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y}) 
loss_vec.append(temp_loss) 

train_acc_temp = sess.run(accuracy, feed_dict={x_data: x_trains, 
               y_target: y_trains, 
               prediction_grid:x_trains}) 
train_accuracy.append(train_acc_temp) 

valid_acc_temp = sess.run(accuracy, feed_dict={x_data: x_tests, 
               y_target: y_tests, 
               prediction_grid: x_tests}) 
valid_accuracy.append(valid_acc_temp) 

if (i+1)%25==0: 
    print('Step #' + str(i+1)) 
    print('Loss = ' + str(temp_loss)) 


# Plot train/test accuracies 
plt.plot(train_accuracy, 'k-', label='Training Accuracy') 
plt.plot(valid_accuracy, 'r--', label='Validation Accuracy') 
plt.title('Train and Validation Set Accuracies') 
plt.xlabel('Generation') 
plt.ylabel('Accuracy') 
plt.legend(loc='lower right') 
plt.show() 

# Plot loss over time 
plt.plot(loss_vec, 'k-') 
plt.title('Loss per Generation') 
plt.xlabel('Generation') 
plt.ylabel('Loss') 
plt.show() 

Answer 1

在您的代碼中，每次創建訓練和測試數據集時都會覆蓋X_train和y_train。

for train_index, test_index in kf.split(x_vals): 
    X_train, X_test = x_vals[train_index], x_vals[test_index] 
    y_train, y_test = y_vals[train_index], y_vals[test_index] 

我建議你創建數組並追加像訓練數據：

x_trains = [] 
y_trains = [] 
for train_index, test_index in kf.split(x_vals): 
    X_train, X_test = x_vals[train_index], x_vals[test_index] 
    y_train, y_test = y_vals[train_index], y_vals[test_index] 
x_trains.append(X_train) 
y_trains.append(y_train) 

如果這還不夠，請爲x_vals提供實施，y_vals因爲它們不是在你的代碼。