深度学习之epoch

一个 epoch（代）是指整个数据集正向反向训练一次。它被用来提示模型的准确率并且不需要额外数据。本节我们将讲解 TensorFlow 里的 epochs，以及如何选择正确的 epochs。

下面是训练一个模型 10 代的 TensorFlow 代码：

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
from helper import batches  # Helper function created in Mini-batching sectiondef print_epoch_stats(epoch_i, sess, last_features, last_labels):"""Print cost and validation accuracy of an epoch"""current_cost = sess.run(cost,feed_dict={features: last_features, labels: last_labels})valid_accuracy = sess.run(accuracy,feed_dict={features: valid_features, labels: valid_labels})print('Epoch: {:<4} - Cost: {:<8.3} Valid Accuracy: {:<5.3}'.format(epoch_i,current_cost,valid_accuracy))n_input = 784  # MNIST data input (img shape: 28*28)
n_classes = 10  # MNIST total classes (0-9 digits)# Import MNIST data
mnist = input_data.read_data_sets('/datasets/ud730/mnist', one_hot=True)# The features are already scaled and the data is shuffled
train_features = mnist.train.images
valid_features = mnist.validation.images
test_features = mnist.test.imagestrain_labels = mnist.train.labels.astype(np.float32)
valid_labels = mnist.validation.labels.astype(np.float32)
test_labels = mnist.test.labels.astype(np.float32)# Features and Labels
features = tf.placeholder(tf.float32, [None, n_input])
labels = tf.placeholder(tf.float32, [None, n_classes])# Weights & bias
weights = tf.Variable(tf.random_normal([n_input, n_classes]))
bias = tf.Variable(tf.random_normal([n_classes]))# Logits - xW + b
logits = tf.add(tf.matmul(features, weights), bias)# Define loss and optimizer
learning_rate = tf.placeholder(tf.float32)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)# Calculate accuracy
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))init = tf.global_variables_initializer()batch_size = 128
epochs = 10
learn_rate = 0.001train_batches = batches(batch_size, train_features, train_labels)with tf.Session() as sess:sess.run(init)# Training cyclefor epoch_i in range(epochs):# Loop over all batchesfor batch_features, batch_labels in train_batches:train_feed_dict = {features: batch_features,labels: batch_labels,learning_rate: learn_rate}sess.run(optimizer, feed_dict=train_feed_dict)# Print cost and validation accuracy of an epochprint_epoch_stats(epoch_i, sess, batch_features, batch_labels)# Calculate accuracy for test datasettest_accuracy = sess.run(accuracy,feed_dict={features: test_features, labels: test_labels})print('Test Accuracy: {}'.format(test_accuracy))

Running the code will output the following:

Epoch: 0    - Cost: 11.0     Valid Accuracy: 0.204
Epoch: 1    - Cost: 9.95     Valid Accuracy: 0.229
Epoch: 2    - Cost: 9.18     Valid Accuracy: 0.246
Epoch: 3    - Cost: 8.59     Valid Accuracy: 0.264
Epoch: 4    - Cost: 8.13     Valid Accuracy: 0.283
Epoch: 5    - Cost: 7.77     Valid Accuracy: 0.301
Epoch: 6    - Cost: 7.47     Valid Accuracy: 0.316
Epoch: 7    - Cost: 7.2      Valid Accuracy: 0.328
Epoch: 8    - Cost: 6.96     Valid Accuracy: 0.342
Epoch: 9    - Cost: 6.73     Valid Accuracy: 0.36
Test Accuracy: 0.3801000118255615

每个 epoch 都试图走向一个低 cost，得到一个更好的准确率。

模型直到 Epoch 9 准确率都一直有提升，让我们把 epochs 的数字提高到 100。

...
Epoch: 79   - Cost: 0.111    Valid Accuracy: 0.86
Epoch: 80   - Cost: 0.11     Valid Accuracy: 0.869
Epoch: 81   - Cost: 0.109    Valid Accuracy: 0.869
....
Epoch: 85   - Cost: 0.107    Valid Accuracy: 0.869
Epoch: 86   - Cost: 0.107    Valid Accuracy: 0.869
Epoch: 87   - Cost: 0.106    Valid Accuracy: 0.869
Epoch: 88   - Cost: 0.106    Valid Accuracy: 0.869
Epoch: 89   - Cost: 0.105    Valid Accuracy: 0.869
Epoch: 90   - Cost: 0.105    Valid Accuracy: 0.869
Epoch: 91   - Cost: 0.104    Valid Accuracy: 0.869
Epoch: 92   - Cost: 0.103    Valid Accuracy: 0.869
Epoch: 93   - Cost: 0.103    Valid Accuracy: 0.869
Epoch: 94   - Cost: 0.102    Valid Accuracy: 0.869
Epoch: 95   - Cost: 0.102    Valid Accuracy: 0.869
Epoch: 96   - Cost: 0.101    Valid Accuracy: 0.869
Epoch: 97   - Cost: 0.101    Valid Accuracy: 0.869
Epoch: 98   - Cost: 0.1      Valid Accuracy: 0.869
Epoch: 99   - Cost: 0.1      Valid Accuracy: 0.869
Test Accuracy: 0.8696000006198883

从上述输出来看，在 epoch 80 的时候，模型的验证准确率就不提升了。让我们看看提升学习率会怎样。

learn_rate = 0.1

Epoch: 76   - Cost: 0.214    Valid Accuracy: 0.752
Epoch: 77   - Cost: 0.21     Valid Accuracy: 0.756
Epoch: 78   - Cost: 0.21     Valid Accuracy: 0.756
...
Epoch: 85   - Cost: 0.207    Valid Accuracy: 0.756
Epoch: 86   - Cost: 0.209    Valid Accuracy: 0.756
Epoch: 87   - Cost: 0.205    Valid Accuracy: 0.756
Epoch: 88   - Cost: 0.208    Valid Accuracy: 0.756
Epoch: 89   - Cost: 0.205    Valid Accuracy: 0.756
Epoch: 90   - Cost: 0.202    Valid Accuracy: 0.756
Epoch: 91   - Cost: 0.207    Valid Accuracy: 0.756
Epoch: 92   - Cost: 0.204    Valid Accuracy: 0.756
Epoch: 93   - Cost: 0.206    Valid Accuracy: 0.756
Epoch: 94   - Cost: 0.202    Valid Accuracy: 0.756
Epoch: 95   - Cost: 0.2974   Valid Accuracy: 0.756
Epoch: 96   - Cost: 0.202    Valid Accuracy: 0.756
Epoch: 97   - Cost: 0.2996   Valid Accuracy: 0.756
Epoch: 98   - Cost: 0.203    Valid Accuracy: 0.756
Epoch: 99   - Cost: 0.2987   Valid Accuracy: 0.756
Test Accuracy: 0.7556000053882599

看来学习率提升的太多了，最终准确率更低了。准确率也更早的停止了改进。我们还是用之前的学习率，把 epochs 改成 80

Epoch: 65   - Cost: 0.122    Valid Accuracy: 0.868
Epoch: 66   - Cost: 0.121    Valid Accuracy: 0.868
Epoch: 67   - Cost: 0.12     Valid Accuracy: 0.868
Epoch: 68   - Cost: 0.119    Valid Accuracy: 0.868
Epoch: 69   - Cost: 0.118    Valid Accuracy: 0.868
Epoch: 70   - Cost: 0.118    Valid Accuracy: 0.868
Epoch: 71   - Cost: 0.117    Valid Accuracy: 0.868
Epoch: 72   - Cost: 0.116    Valid Accuracy: 0.868
Epoch: 73   - Cost: 0.115    Valid Accuracy: 0.868
Epoch: 74   - Cost: 0.115    Valid Accuracy: 0.868
Epoch: 75   - Cost: 0.114    Valid Accuracy: 0.868
Epoch: 76   - Cost: 0.113    Valid Accuracy: 0.868
Epoch: 77   - Cost: 0.113    Valid Accuracy: 0.868
Epoch: 78   - Cost: 0.112    Valid Accuracy: 0.868
Epoch: 79   - Cost: 0.111    Valid Accuracy: 0.868
Epoch: 80   - Cost: 0.111    Valid Accuracy: 0.869
Test Accuracy: 0.86909999418258667

准确率只到 0.86。这有可能是学习率太高造成的。降低学习率需要更多的 epoch，但是可以最终得到更好的准确率。

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