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captcha.py

import tensorflow as tf
import numpy as np
import math
from scipy import misc
import glob

from ops import *

def label(path):
    result = [0 for _ in range(100)]
    index = 0
    result[int(path.split('/')[-1][:2])] = 1
    return np.array(result)

png = [(misc.imread(image_path), label(image_path)) for image_path in glob.glob('./captcha/*.png')]

index = 0
def next_batch(batch_size):
    global index
    xs, ys = [], []
    for i in range(batch_size):
        xs.append(png[index % len(png)][0])
        ys.append(png[index % len(png)][1])
        index += 1
    index %= len(png)

    return xs, ys
  batch_size = 32

x = tf.placeholder(tf.float32, [batch_size, 26, 52, 3]) # input
y_ = tf.placeholder(tf.float32, [batch_size, 100]) # target

h1 = tf.nn.relu(conv2d(x, 48, name='conv1')) # stride is 2 (default)
h2 = tf.nn.relu(conv2d(h1, 64, name='conv2'))
h3 = tf.nn.relu(conv2d(h2, 128, name='conv3'))

h4 = tf.reshape(h3, [batch_size, 7 * 4 * 128])
h4 = linear(h4, 100)
h4 = tf.nn.relu(h4)

y = tf.reshape(tf.nn.softmax(h4), [batch_size, 100])

# loss
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(0.01, beta1=0.5).minimize(cross_entropy)

# train
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9) # remove gpu_options if you are using cpu.

def test_data(batch_size):
    with open('./test_data/label.txt', 'rt') as f:
        xs, ys = [], []
        for i in range(batch_size):
            x = misc.imread('./test_data/' + str(i) + '.png')
            ans = int(f.readline())
            y = [0 for _ in range(100)]
            y[ans] = 1
            xs.append(x)
            ys.append(y)
    return xs, ys

test_images, test_labels = test_data(batch_size)

with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
    tf.initialize_all_variables().run()    
    for i in range(3000):
        # From input
        batch_xs, batch_ys = next_batch(batch_size)
        train_step.run({x: batch_xs, y_:batch_ys})
        if i % 2 == 0:
            print(cross_entropy.eval({x: test_images, y_: test_labels}))
            correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
            print(accuracy.eval({x: test_images, y_: test_labels}))