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Cross-validation in Deep Learning In deep learning, you would normally tempt to avoid CV because of the cost associated with training k different models. Instead of doing k-Fold or other CV techniques, you might use a random subset of your training data as a hold-out for validation purposes.
Cross validation can be used to select the best hyperparameters for training a neural network. If the folds have good performance on their validation sets when a set of hyperparameters is applied, then that set of hyperparameters is believed will help generalize the model to unseen data.
Cross-validation is a technique for evaluating ML models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. Use cross-validation to detect overfitting, ie, failing to generalize a pattern.
As already discussed, tensorflow doesn't provide its own way to cross-validate the model. The recommended way is to use KFold. It's a bit tedious, but doable. Here's a complete example of cross-validating MNIST model with tensorflow and KFold:
from sklearn.model_selection import KFold
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# Parameters
learning_rate = 0.01
batch_size = 500
# TF graph
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
pred = tf.nn.softmax(tf.matmul(x, W) + b)
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.global_variables_initializer()
mnist = input_data.read_data_sets("data/mnist-tf", one_hot=True)
train_x_all = mnist.train.images
train_y_all = mnist.train.labels
test_x = mnist.test.images
test_y = mnist.test.labels
def run_train(session, train_x, train_y):
print "\nStart training"
session.run(init)
for epoch in range(10):
total_batch = int(train_x.shape[0] / batch_size)
for i in range(total_batch):
batch_x = train_x[i*batch_size:(i+1)*batch_size]
batch_y = train_y[i*batch_size:(i+1)*batch_size]
_, c = session.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
if i % 50 == 0:
print "Epoch #%d step=%d cost=%f" % (epoch, i, c)
def cross_validate(session, split_size=5):
results = []
kf = KFold(n_splits=split_size)
for train_idx, val_idx in kf.split(train_x_all, train_y_all):
train_x = train_x_all[train_idx]
train_y = train_y_all[train_idx]
val_x = train_x_all[val_idx]
val_y = train_y_all[val_idx]
run_train(session, train_x, train_y)
results.append(session.run(accuracy, feed_dict={x: val_x, y: val_y}))
return results
with tf.Session() as session:
result = cross_validate(session)
print "Cross-validation result: %s" % result
print "Test accuracy: %f" % session.run(accuracy, feed_dict={x: test_x, y: test_y})
As the dataset gets larger cross validation gets more expensive.In deep learning we usually use large data sets.You should be fine with simple training. Tensorflow doesnt have a built in mechanism for cv as it is not usually used in neural networks.In neural networks, the efficiency of the network relies mainly on the data set, number of epochs and the learning rate.
I have used cv in sklearn You can check the link: https://github.com/hackmaster0110/Udacity-Data-Analyst-Nano-Degree-Projects/
In that,go to poi_id.py in Identify fraud from enron data(In Project folder)
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