CNNにてaccuracyの値が変化しない。

Question

前提・実現したいこと

現在、CNNとカスケードを活用して自分の顔のみ認識し、その顔に対してカスケードを使い矩形を描画するプログラムを作成しています。そこで、「自分の顔」というものを学習させるプログラムを以下の通り作成したのですが、 training accuracyの値が一切変化しません。
そこで、どのような改善をすれば学習が開始されるのかアドバイスをお願いいたします。
また過不足な情報があればお申し付けください。

発生している問題・エラーメッセージ

![イメージ説明](49c0662e790182f8fed721754593e1df.png)

該当のソースコード

import os
import random
import cv2
import numpy as np
import tensorflow as tf

import matplotlib.pyplot as plt

path = "./faces/train"
dirs = os.listdir(path)
dirs = [f for f in dirs if os.path.isdir(os.path.join(path, f))]

label_dict = {}
i = 0

for dirname in dirs:
    label_dict[dirname] = i
    i += 1

def load_data(data_type):

    filenames, images, labels = [], [], []

    walk = filter(lambda _: not len(_[1]) and data_type in _[0], os.walk('faces'))

    for root, dirs, files in walk:
        filenames += ['{}/{}'.format(root, _) for _ in files if not _.startswith('.')]

    # Shuffle files
    random.shuffle(filenames)

    # Read, resize, and reshape images
    images = []
    for file in filenames:
        img = cv2.imread(file)
        img = cv2.resize(img, (32,32))
        images.append(img.flatten().astype(np.float32) / 255.0)
    images = np.asarray(images)

    for filename in filenames:
        label = np.zeros(len(label_dict))
        for k, v in label_dict.items():
            if k in filename:
                label[v] = 1.
        labels.append(label)

    return images, labels

def get_batch_list(l, batch_size):
    # [1, 2, 3, 4, 5,...] -> [[1, 2, 3], [4, 5,..]]
    return [np.asarray(l[_:_+batch_size]) for _ in range(0, len(l), batch_size)]

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

def inference(images_placeholder, keep_prob):

    x_image = tf.reshape(images_placeholder, [-1, 32, 32, 3])

    # Convolution layer
    W_conv1 = weight_variable([5, 5, 3, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

    # Pooling layer
    h_pool1 = max_pool_2x2(h_conv1)

    # Convolution layer
    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

    # Pooling layer
    h_pool2 = max_pool_2x2(h_conv2)

    # Full connected layer
    W_fc1 = weight_variable([8 * 8 * 64, 1024])
    b_fc1 = bias_variable([1024])
    h_pool2_flat = tf.reshape(h_pool2, [-1, 8 * 8 * 64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    # Dropout
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    # Full connected layer
    W_fc2 = weight_variable([1024, len(label_dict)])
    b_fc2 = bias_variable([len(label_dict)])

    return tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# データ読込
train_images, train_labels = load_data('train')
test_images, test_labels = load_data('test')

print("train_images", len(train_images))
print("test_images", len(test_images))

# 
x = tf.placeholder('float', shape=[None, 32 * 32 * 3])  # 32 * 32, 3 channels
y_ = tf.placeholder('float', shape=[None, len(label_dict)]) # label_dict size
keep_prob = tf.placeholder('float')
y_conv = inference(x, keep_prob)

# Loss function
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
tf.summary.scalar('cross_entropy', cross_entropy)

# Minimize cross entropy by using SGD
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

# Accuracy
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))

saver = tf.train.Saver()
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())

# Batch
batch_size = 20
batched_train_images = get_batch_list(train_images, batch_size)
batched_train_labels = get_batch_list(train_labels, batch_size)
print(len(batched_train_labels))

train_labels, test_labels = np.asarray(train_labels), np.asarray(test_labels)

cnt = 0

accuracys = []

# Train
for i in range(15):

    for step, (images, labels) in enumerate(zip(batched_train_images, batched_train_labels)):
        sess.run(train_step, feed_dict={ x: images, y_: labels, keep_prob: 0.5 })
        train_accuracy = accuracy.eval(feed_dict = {
            x: train_images, y_: train_labels, keep_prob: 1.0 })
        accuracys.append(train_accuracy)
        cnt += 1

        print('step {}, training accuracy {}'.format(cnt, train_accuracy))

# Test trained model
test_accuracy = accuracy.eval(feed_dict = {
    x: test_images, y_: test_labels, keep_prob: 1.0 })
print('test accuracy {}'.format(test_accuracy))

# Save model
save_path = saver.save(sess, "model_face/model.ckpt")

sess.close()

plt.plot(accuracys)
plt.show()

試したこと

何通りかtrain_stepの値を変えて実行してみましたが、変化はなかったです。

補足情報（FW/ツールのバージョンなど）

ここにより詳細な情報を記載してください。