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 section
def 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.images
train_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 = 0.001
train_batches = batches(batch_size, train_features, train_labels)
with tf.Session() as sess:
sess.run(init)
# Training cycle
for epoch_i in range(epochs):
# Loop over all batches
for 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 epoch
print_epoch_stats(epoch_i, sess, batch_features, batch_labels)
# Calculate accuracy for test dataset
test_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。