本文介绍了如何在Tensorflow中应用Dropout以提高神经网络的准确性?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
Drop-Out是正则化技术.我想将其应用于notMNIST数据以减少过度拟合以完成我的Udacity深度学习课程作业.我已经阅读了.这是我的代码
Drop-Out is regularization techniques. And I want to apply it to notMNIST data to reduce over-fitting to finish my Udacity Deep Learning Course Assignment.I have read the docs of tensorflow on how to call the tf.nn.dropout. And here is my code
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
pickle_file = 'notMNIST.pickle'
with open(pickle_file, 'rb') as f:
save = pickle.load(f)
train_dataset = save['train_dataset']
train_labels = save['train_labels']
valid_dataset = save['valid_dataset']
valid_labels = save['valid_labels']
test_dataset = save['test_dataset']
test_labels = save['test_labels']
del save # hint to help gc free up memory
print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
image_size = 28
num_labels = 10
def reformat(dataset, labels):
dataset = dataset.reshape((-1, image_size * image_size)).astype(np.float32)
# Map 1 to [0.0, 1.0, 0.0 ...], 2 to [0.0, 0.0, 1.0 ...]
labels = (np.arange(num_labels) == labels[:,None]).astype(np.float32)
return dataset, labels
train_dataset, train_labels = reformat(train_dataset, train_labels)
valid_dataset, valid_labels = reformat(valid_dataset, valid_labels)
test_dataset, test_labels = reformat(test_dataset, test_labels)
print('Training set', train_dataset.shape, train_labels.shape)
print('Validation set', valid_dataset.shape, valid_labels.shape)
print('Test set', test_dataset.shape, test_labels.shape)
def accuracy(predictions, labels):
return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) / predictions.shape[0])
# ReLU neuron
# param
training_epochs = 30
batch_size = 521
display_step = 1
n_input = 784 # img shape: 28*28
n_classes = 10 # MNIST total classes (0-9 digits)
# hyper-parameter
n_hidden_1 = 256
learning_rate = 0.05
lambda_term = 0.01
graph = tf.Graph()
with graph.as_default():
# init weights
weights_hiden = tf.Variable(tf.random_normal([n_input, n_hidden_1], stddev=np.sqrt(n_input)))
weights_out = tf.Variable(tf.random_normal([n_hidden_1, n_classes], stddev=np.sqrt(n_hidden_1)))
biases_hidden = tf.Variable(tf.random_normal([n_hidden_1]))
biases_out = tf.Variable(tf.random_normal([n_classes]))
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
def model(x, weights_hiden, weights_out, biases_hidden, biases_out):
# hidden layer with RELU activation
layer_1 = tf.nn.relu(tf.add(tf.matmul(x, weights_hiden), biases_hidden))
# apply DropOut to hidden layer
keep_prob = tf.placeholder(tf.float32) # DROP-OUT here
drop_out = tf.nn.dropout(layer_1, keep_prob) # DROP-OUT here
# output layer with linear activation
out_layer = tf.matmul(layer_1, weights_out) + biases_out
return out_layer
# Construct model
pred = model(x, weights_hiden, weights_out, biases_hidden, biases_out)
# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y) +
lambda_term * tf.nn.l2_loss(weights_hiden) +
lambda_term * tf.nn.l2_loss(weights_out) +
lambda_term * tf.nn.l2_loss(biases_hidden) +
lambda_term * tf.nn.l2_loss(biases_out))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# run the graph
with tf.Session(graph=graph) as sess:
tf.initialize_all_variables().run()
print('Initialized')
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(train_dataset.shape[0]/batch_size)
# Loop over all batches
for i in range(total_batch):
batch_x = train_dataset[(i*batch_size):((i*batch_size) + batch_size), :]
batch_y = train_labels[(i*batch_size):((i*batch_size) + batch_size), :]
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
# Compute average loss
avg_cost += c / total_batch
# Display logs per epoch step
if epoch % display_step == 0:
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
print("Optimization Finished!")
# Test model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print("Test data accuracy:", accuracy.eval({x: test_dataset, y: test_labels}))
print("Valid data accuracy:", accuracy.eval({x: valid_dataset, y: valid_labels}))
在函数model()中调用了tf.nn.dropout，但是在我将DropOut技术应用于神经网络之后，准确性似乎没有任何变化，这是结果:
The tf.nn.dropout is called in function model(), but after I applied the DropOut technique to the neural network, the accuracy did seem any change, here is the result:
Epoch: 0001 cost= 579980.086977807
Epoch: 0002 cost= 238859.802382506
Epoch: 0003 cost= 90672.733752856
Epoch: 0004 cost= 32649.040985028
Epoch: 0005 cost= 11325.878361874
Epoch: 0006 cost= 3866.805511076
Epoch: 0007 cost= 1357.785540469
Epoch: 0008 cost= 519.381747333
Epoch: 0009 cost= 225.359804119
Epoch: 0010 cost= 110.099476707
Epoch: 0011 cost= 55.212384386
Epoch: 0012 cost= 28.469241683
Epoch: 0013 cost= 14.511494627
Epoch: 0014 cost= 6.567228943
Epoch: 0015 cost= 3.186372240
Epoch: 0016 cost= 1.701917576
Epoch: 0017 cost= 1.041632473
Epoch: 0018 cost= 0.843376874
Epoch: 0019 cost= 0.786183911
Epoch: 0020 cost= 0.775412846
Epoch: 0021 cost= 0.782965020
Epoch: 0022 cost= 0.796788171
Epoch: 0023 cost= 0.814522117
Epoch: 0024 cost= 0.832090579
Epoch: 0025 cost= 0.849197715
Epoch: 0026 cost= 0.867473578
Epoch: 0027 cost= 0.889561496
Epoch: 0028 cost= 0.921837020
Epoch: 0029 cost= 16.655304543
Epoch: 0030 cost= 1.421570476
Optimization Finished!
Test data accuracy: 0.8775
Valid data accuracy: 0.8069
我如何应用Tensorflow的DropOut来提高网络的准确性?谢谢！
How can I apply DropOut by Tensorflow to improve the accuracy of the network? Thank you!
推荐答案
在图中，我建议将keep_prob = tf.placeholder(tf.float32)移动到model函数之外，以使其具有全局性.
In the graph, I'd suggest to move keep_prob = tf.placeholder(tf.float32) outside of the model function to make it global.
with graph.as_default():
...
x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])
keep_prob = tf.placeholder(tf.float32)
def model(x, weights_hiden, weights_out, biases_hidden, biases_out, keep_prob):
# hidden layer with RELU activation
layer_1 = tf.nn.relu(tf.add(tf.matmul(x, weights_hiden), biases_hidden))
# apply DropOut to hidden layer
drop_out = tf.nn.dropout(layer_1, keep_prob) # DROP-OUT here
# output layer with linear activation
out_layer = tf.matmul(drop_out, weights_out) + biases_out
return out_layer
...
运行session时，在训练时间内输入所需的keep_prob值，并在参考(验证和/或测试)时间内将keep_prob馈入keep_prob.
When running session, feed a desired keep_prob value during training time, and feed 1.0 to keep_prob during reference (validation and/or testing) time.
# run the graph
with tf.Session(graph=graph) as sess:
tf.initialize_all_variables().run()
...
for epoch in range(training_epochs):
...
for i in range(total_batch):
batch_x = ...
batch_y = ...
# Run optimization op (backprop) and cost op (to get loss value)
# Feed a value < 1.0 for keep prob during training
_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y, keep_prob : 0.5})
...
# Feed 1.0 for keep prob during testing
print("Test data accuracy:", accuracy.eval({x: test_dataset, y: test_labels, keep_prob : 1.0}))
print("Valid data accuracy:", accuracy.eval({x: valid_dataset, y: valid_labels, keep_prob : 1.0}))
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