如何测试tensorflow cifar10 cnn教程模型
How to test tensorflow cifar10 cnn tutorial model
我对机器学习比较陌生，目前几乎没有开发它的经验。
所以我的问题是：在训练和评估来自tensorflow的cifar10数据集后tutorial我想知道如何用样本图像测试它？
我可以训练和评估 Imagenet tutorial from the caffe machine-learning framework 并且使用 python API.
在自定义应用程序上使用经过训练的模型相对容易
任何帮助将不胜感激！
我建议查看 TensorFlow 网站上的 basic MNIST tutorial。看起来你定义了一些函数来生成你想要的输出类型，然后 运行 你的会话，传递给它这个评估函数（下面的 correct_prediction），以及一个包含你需要的任何参数的字典（ x 和 y_ 下面）。
如果您定义并训练了一些接受输入 x 的网络，会根据您的输入生成响应 y，并且您知道测试集的预期响应 y_，您可以打印出对测试集的每个响应，例如：
correct_prediction = tf.equal(y, y_) % Check whether your prediction is correct
print(sess.run(correct_prediction, feed_dict={x: test_images, y_: test_labels}))
这只是对教程中所做内容的修改，他们不是尝试打印每个响应，而是确定正确响应的百分比。另请注意，本教程使用 one-hot vectors 进行预测 y 和实际值 y_，因此为了 return 相关数字，他们必须找到这些向量的哪个索引等于 tf.argmax(y, 1).
编辑
一般来说，如果你在你的图表中定义了一些东西，你可以稍后在你 运行 你的图表中输出它。假设您将输出 logits 上的 softmax 函数的结果定义为：
graph = tf.Graph()
with graph.as_default():
...
prediction = tf.nn.softmax(logits)
...
然后你可以在 运行 时间输出：
with tf.Session(graph=graph) as sess:
...
feed_dict = { ... } # define your feed dictionary
pred = sess.run([prediction], feed_dict=feed_dict)
# do stuff with your prediction vector
这不是问题的 100% 答案，但它是一种类似的解决方法，基于问题评论中建议的 MNIST NN 训练示例。
基于 TensorFlow begginer MNIST 教程，感谢 this tutorial，这是一种使用自定义数据训练和使用神经网络的方法。
请注意，对于像 CIFAR10 这样的教程，也应该做类似的事情，正如评论中提到的@Yaroslav Bulatov。
import input_data
import datetime
import numpy as np
import tensorflow as tf
import cv2
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
from random import randint
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder("float", [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder("float", [None,10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
#Train our model
iter = 1000
for i in range(iter):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
#Evaluationg our model:
correct_prediction=tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))
print "Accuracy: ", sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
#1: Using our model to classify a random MNIST image from the original test set:
num = randint(0, mnist.test.images.shape[0])
img = mnist.test.images[num]
classification = sess.run(tf.argmax(y, 1), feed_dict={x: [img]})
'''
#Uncomment this part if you want to plot the classified image.
plt.imshow(img.reshape(28, 28), cmap=plt.cm.binary)
plt.show()
'''
print 'Neural Network predicted', classification[0]
print 'Real label is:', np.argmax(mnist.test.labels[num])
#2: Using our model to classify MNIST digit from a custom image:
# create an an array where we can store 1 picture
images = np.zeros((1,784))
# and the correct values
correct_vals = np.zeros((1,10))
# read the image
gray = cv2.imread("my_digit.png", 0 ) #0=cv2.CV_LOAD_IMAGE_GRAYSCALE #must be .png!
# rescale it
gray = cv2.resize(255-gray, (28, 28))
# save the processed images
cv2.imwrite("my_grayscale_digit.png", gray)
"""
all images in the training set have an range from 0-1
and not from 0-255 so we divide our flatten images
(a one dimensional vector with our 784 pixels)
to use the same 0-1 based range
"""
flatten = gray.flatten() / 255.0
"""
we need to store the flatten image and generate
the correct_vals array
correct_val for a digit (9) would be
[0,0,0,0,0,0,0,0,0,1]
"""
images[0] = flatten
my_classification = sess.run(tf.argmax(y, 1), feed_dict={x: [images[0]]})
"""
we want to run the prediction and the accuracy function
using our generated arrays (images and correct_vals)
"""
print 'Neural Network predicted', my_classification[0], "for your digit"
如需进一步的图像调节（白色背景中的数字应完全变暗）和更好的神经网络训练（准确度>91%），请查看 TensorFlow 的高级 MNIST 教程或我提到的第二个教程。
下面的例子不是mnist教程，而是一个简单的XOR例子。注意 train() 和 test() 方法。我们在全球范围内声明和保留的所有内容都是权重、偏差和会话。在测试方法中，我们重新定义了输入的形状，并重用了我们在训练中改进的相同权重和偏差（以及会话）。
import tensorflow as tf
#parameters for the net
w1 = tf.Variable(tf.random_uniform(shape=[2,2], minval=-1, maxval=1, name='weights1'))
w2 = tf.Variable(tf.random_uniform(shape=[2,1], minval=-1, maxval=1, name='weights2'))
#biases
b1 = tf.Variable(tf.zeros([2]), name='bias1')
b2 = tf.Variable(tf.zeros([1]), name='bias2')
#tensorflow session
sess = tf.Session()
def train():
#placeholders for the traning inputs (4 inputs with 2 features each) and outputs (4 outputs which have a value of 0 or 1)
x = tf.placeholder(tf.float32, [4, 2], name='x-inputs')
y = tf.placeholder(tf.float32, [4, 1], name='y-inputs')
#set up the model calculations
temp = tf.sigmoid(tf.matmul(x, w1) + b1)
output = tf.sigmoid(tf.matmul(temp, w2) + b2)
#cost function is avg error over training samples
cost = tf.reduce_mean(((y * tf.log(output)) + ((1 - y) * tf.log(1.0 - output))) * -1)
#training step is gradient descent
train_step = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
#declare training data
training_x = [[0,1], [0,0], [1,0], [1,1]]
training_y = [[1], [0], [1], [0]]
#init session
init = tf.initialize_all_variables()
sess.run(init)
#training
for i in range(100000):
sess.run(train_step, feed_dict={x:training_x, y:training_y})
if i % 1000 == 0:
print (i, sess.run(cost, feed_dict={x:training_x, y:training_y}))
print '\ntraining done\n'
def test(inputs):
#redefine the shape of the input to a single unit with 2 features
xtest = tf.placeholder(tf.float32, [1, 2], name='x-inputs')
#redefine the model in terms of that new input shape
temp = tf.sigmoid(tf.matmul(xtest, w1) + b1)
output = tf.sigmoid(tf.matmul(temp, w2) + b2)
print (inputs, sess.run(output, feed_dict={xtest:[inputs]})[0, 0] >= 0.5)
train()
test([0,1])
test([0,0])
test([1,1])
test([1,0])
我对机器学习比较陌生，目前几乎没有开发它的经验。
所以我的问题是：在训练和评估来自tensorflow的cifar10数据集后tutorial我想知道如何用样本图像测试它？
我可以训练和评估 Imagenet tutorial from the caffe machine-learning framework 并且使用 python API.
在自定义应用程序上使用经过训练的模型相对容易任何帮助将不胜感激！
我建议查看 TensorFlow 网站上的 basic MNIST tutorial。看起来你定义了一些函数来生成你想要的输出类型，然后 运行 你的会话，传递给它这个评估函数（下面的 correct_prediction），以及一个包含你需要的任何参数的字典（ x 和 y_ 下面）。
如果您定义并训练了一些接受输入 x 的网络，会根据您的输入生成响应 y，并且您知道测试集的预期响应 y_，您可以打印出对测试集的每个响应，例如：
correct_prediction = tf.equal(y, y_) % Check whether your prediction is correct
print(sess.run(correct_prediction, feed_dict={x: test_images, y_: test_labels}))
这只是对教程中所做内容的修改，他们不是尝试打印每个响应，而是确定正确响应的百分比。另请注意，本教程使用 one-hot vectors 进行预测 y 和实际值 y_，因此为了 return 相关数字，他们必须找到这些向量的哪个索引等于 tf.argmax(y, 1).
编辑
一般来说，如果你在你的图表中定义了一些东西，你可以稍后在你 运行 你的图表中输出它。假设您将输出 logits 上的 softmax 函数的结果定义为：
graph = tf.Graph()
with graph.as_default():
...
prediction = tf.nn.softmax(logits)
...
然后你可以在 运行 时间输出：
with tf.Session(graph=graph) as sess:
...
feed_dict = { ... } # define your feed dictionary
pred = sess.run([prediction], feed_dict=feed_dict)
# do stuff with your prediction vector
这不是问题的 100% 答案，但它是一种类似的解决方法，基于问题评论中建议的 MNIST NN 训练示例。
基于 TensorFlow begginer MNIST 教程，感谢 this tutorial，这是一种使用自定义数据训练和使用神经网络的方法。
请注意，对于像 CIFAR10 这样的教程，也应该做类似的事情，正如评论中提到的@Yaroslav Bulatov。
import input_data
import datetime
import numpy as np
import tensorflow as tf
import cv2
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
from random import randint
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder("float", [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder("float", [None,10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
#Train our model
iter = 1000
for i in range(iter):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
#Evaluationg our model:
correct_prediction=tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,"float"))
print "Accuracy: ", sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
#1: Using our model to classify a random MNIST image from the original test set:
num = randint(0, mnist.test.images.shape[0])
img = mnist.test.images[num]
classification = sess.run(tf.argmax(y, 1), feed_dict={x: [img]})
'''
#Uncomment this part if you want to plot the classified image.
plt.imshow(img.reshape(28, 28), cmap=plt.cm.binary)
plt.show()
'''
print 'Neural Network predicted', classification[0]
print 'Real label is:', np.argmax(mnist.test.labels[num])
#2: Using our model to classify MNIST digit from a custom image:
# create an an array where we can store 1 picture
images = np.zeros((1,784))
# and the correct values
correct_vals = np.zeros((1,10))
# read the image
gray = cv2.imread("my_digit.png", 0 ) #0=cv2.CV_LOAD_IMAGE_GRAYSCALE #must be .png!
# rescale it
gray = cv2.resize(255-gray, (28, 28))
# save the processed images
cv2.imwrite("my_grayscale_digit.png", gray)
"""
all images in the training set have an range from 0-1
and not from 0-255 so we divide our flatten images
(a one dimensional vector with our 784 pixels)
to use the same 0-1 based range
"""
flatten = gray.flatten() / 255.0
"""
we need to store the flatten image and generate
the correct_vals array
correct_val for a digit (9) would be
[0,0,0,0,0,0,0,0,0,1]
"""
images[0] = flatten
my_classification = sess.run(tf.argmax(y, 1), feed_dict={x: [images[0]]})
"""
we want to run the prediction and the accuracy function
using our generated arrays (images and correct_vals)
"""
print 'Neural Network predicted', my_classification[0], "for your digit"
如需进一步的图像调节（白色背景中的数字应完全变暗）和更好的神经网络训练（准确度>91%），请查看 TensorFlow 的高级 MNIST 教程或我提到的第二个教程。
下面的例子不是mnist教程，而是一个简单的XOR例子。注意 train() 和 test() 方法。我们在全球范围内声明和保留的所有内容都是权重、偏差和会话。在测试方法中，我们重新定义了输入的形状，并重用了我们在训练中改进的相同权重和偏差（以及会话）。
import tensorflow as tf
#parameters for the net
w1 = tf.Variable(tf.random_uniform(shape=[2,2], minval=-1, maxval=1, name='weights1'))
w2 = tf.Variable(tf.random_uniform(shape=[2,1], minval=-1, maxval=1, name='weights2'))
#biases
b1 = tf.Variable(tf.zeros([2]), name='bias1')
b2 = tf.Variable(tf.zeros([1]), name='bias2')
#tensorflow session
sess = tf.Session()
def train():
#placeholders for the traning inputs (4 inputs with 2 features each) and outputs (4 outputs which have a value of 0 or 1)
x = tf.placeholder(tf.float32, [4, 2], name='x-inputs')
y = tf.placeholder(tf.float32, [4, 1], name='y-inputs')
#set up the model calculations
temp = tf.sigmoid(tf.matmul(x, w1) + b1)
output = tf.sigmoid(tf.matmul(temp, w2) + b2)
#cost function is avg error over training samples
cost = tf.reduce_mean(((y * tf.log(output)) + ((1 - y) * tf.log(1.0 - output))) * -1)
#training step is gradient descent
train_step = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
#declare training data
training_x = [[0,1], [0,0], [1,0], [1,1]]
training_y = [[1], [0], [1], [0]]
#init session
init = tf.initialize_all_variables()
sess.run(init)
#training
for i in range(100000):
sess.run(train_step, feed_dict={x:training_x, y:training_y})
if i % 1000 == 0:
print (i, sess.run(cost, feed_dict={x:training_x, y:training_y}))
print '\ntraining done\n'
def test(inputs):
#redefine the shape of the input to a single unit with 2 features
xtest = tf.placeholder(tf.float32, [1, 2], name='x-inputs')
#redefine the model in terms of that new input shape
temp = tf.sigmoid(tf.matmul(xtest, w1) + b1)
output = tf.sigmoid(tf.matmul(temp, w2) + b2)
print (inputs, sess.run(output, feed_dict={xtest:[inputs]})[0, 0] >= 0.5)
train()
test([0,1])
test([0,0])
test([1,1])
test([1,0])