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詳細: Ubuntu 14.04(LTS)、OpenCV 2.4.13、Spyder 2.3.9(Python 2.7)、Tensorflow r0.10

PythonとTensorflow (オプションのOpenCV )で画像から Number を認識したい 。

さらに、テンソルフローでMNISTデータトレーニングを使用したい

このように（コードはこのページの動画を参考に）、

コード：

import tensorflow as tf
import random

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder("float", [None, 784])
y = tf.placeholder("float", [None, 10])

W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1

### modeling ###

activation = tf.nn.softmax(tf.matmul(x, W) + b)

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(activation), reduction_indices=1))

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)

init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)

### training ###

for epoch in range(training_epochs) :

    avg_cost = 0
    total_batch = int(mnist.train.num_examples/batch_size)

    for i in range(total_batch) :

        batch_xs, batch_ys =mnist.train.next_batch(batch_size)
        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
        avg_cost += sess.run(cross_entropy, feed_dict = {x: batch_xs, y: batch_ys}) / total_batch

    if epoch % display_step == 0 :
        print "Epoch : ", "%04d" % (epoch+1), "cost=", "{:.9f}".format(avg_cost)

print "Optimization Finished"

### predict number ###

r = random.randint(0, mnist.test.num_examples - 1)
print "Prediction: ", sess.run(tf.argmax(activation,1), {x: mnist.test.images[r:r+1]})
print "Correct Answer: ", sess.run(tf.argmax(mnist.test.labels[r:r+1], 1))

しかし、問題は、どうすればnumpy配列を次のように作成できるかです

コードの追加:

mnist.test.images[r:r+1]

[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.50196081 1.1.1.1.1.1.0.50196081 0.25098041 0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0 . 0.50196081 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.25098041 0. 0. 0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.50196081 1. 1. 1. 0.74901962 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.74901962 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0.50196081 0. 0. 0. 0. 0. 0. 0. 0.0.25098041 1. 1. 0.74901962 0.25098041 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.74901962 1. 1. 0.74901962 0. 0. 0. 0. 0. 0. 0. 0. 0 . 0. 0.25098041 1. 1. 0.74901962 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 50196081 1. 1. 0.74901962 0. 0. 0. 0. 0. 0. 0. 0. 0 . 0. 0. 0. 0.25098041 1. 1. 0.50196081 0. 0. 0. 0. 0. 0. 0. 0. 0.50196081 1. 1. 0.25098041 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0.50196081 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.25098041 1. 1. 1. 0. 0. 0. 0. 0. 0. 0 . 0. 1. 1. 0.50196081 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.25098041 1. 1. 1. 1. 0. 0. 0.0. 0. 0. 0. 0. 0.74901962 1. 0.50196081 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.74901962 1. 1. 1. 0.25098041 0. 0. 0. 0. 0. 0. 0. 0. 0.50196081 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.25098041 0.74901962 1. 1. 1. 1. 0.74901962 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.50196081 1. 1. 0.74901962 0. 0. 0. 0. 0. 0.25098041 0.50196081 1. 1.1. 1. 1. 1. 0.50196081 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 74901962 1. 1. 1. 1. 0.50196081 0.50196081 0.74901962 1. 1. 1. 1 . 1. 1. 1. 0.50196081 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.74901962 1. 1. 1. 1. 1. 1. 1. 1 . 1. 1. 1. 0.50196081 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0.25098041 1.1.1.1.1.1.1.0.50196081 0.25098041 0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0 . 0. 0. 0. 0.50196081 0.50196081 0.50196081 0.50196081 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0 . 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.0.0.0.0.0.0.0.0.0.0.0.0.0.]]0.0.0.0.]]0.0.0.0.]]

問題を解決するために OpenCV を使用すると、画像に関する numpy 配列を作成できますが、少し奇妙です。(配列を28x28のベクトルにしたい)

コードの追加:

image = cv2.imread("img_easy.jpg")
resized_image = cv2.resize(image, (28, 28))

[[[255 255 255] [255 255 255] [255 255 255] ..., [255 255 255] [255 255 255] [255 255 255]]

[[255 255 255] [255 255 255] [255 255 255] ..., [255 255 255] [255 255 255] [255 255 255]]

[[255 255 255] [255 255 255] [255 255 255] ..., [255 255 255] [255 255 255] [255 255 255]]

...、

[[255 255 255] [255 255 255] [255 255 255] ..., [255 255 255] [255 255 255] [255 255 255]]

[[255 255 255] [255 255 255] [255 255 255] ..., [255 255 255] [255 255 255] [255 255 255]]

[[255 255 255] [255 255 255] [255 255 255] ..., [255 255 255] [255 255 255] [255 255 255]]]

そして、値('resized_image') を Tensorflow コードに入れます。このような、

コードの変更:

### predict number ###

print "Prediction: ", sess.run(tf.argmax(activation,1), {x: resized_image})
print "Correct Answer: 9"

その結果、この行でエラーが発生します。

ValueError: 形状 '(?, 784)' を持つ Tensor u'Placeholder_2:0' の形状 (28, 28, 3) の値をフィードできません

ついに、

1）テンソルフローコード（おそらくnumpy配列[784]）に入力できるデータを作成する方法を知りたい

2) テンソルフローを使った数字認識の例を知っていますか?

私は機械学習の初心者です。

どうすればいいのか詳しく教えてください。

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