CNNでフォルダ内の画像を学習させたいのですが起動すると学習効率が上がらないのは何故なのか原因を知りたいです。コードは以下の通りです。

import os
import glob
import sys
import cv2
import tensorflow as tf
import numpy as np
import pandas as pd
from numpy.random import permutation

import time

image_size = 28#次元
image_channnel = 1
imgpx = image_size * image_size * image_channnel#画像サイズ
category = 6
np.random.seed(1)

flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('log_path', '/tmp/cnn/', 'Directory to put the training data')
flags.DEFINE_string('save_name', os.getcwd() + '/var.ckpt', 'path to save variables')
flags.DEFINE_integer('max_steps', 200, 'Number of steps to run trainer')
flags.DEFINE_integer('batch_size', 32, '')
flags.DEFINE_integer('training_epoch', 100, '')
flags.DEFINE_integer('validation_interval', 10, 'validation interval')
flags.DEFINE_float('learning_rate', 1e-4, 'Initial learning rate')
flags.DEFINE_float('dropout_keep_prob', 0.5, '')

def next_batch(data, label, batch_size):
    perm = np.arange(data.shape[0])
    np.random.shuffle(perm)
    return data[perm][:batch_size], label[perm][:batch_size]

def input_placeholder(size, channel, label):
    with tf.name_scope('input') as scope:
        inputs = tf.placeholder("float", [None, size, size, channel], 'inputs')
        labels = tf.placeholder("float", [None, label], 'labels')
    dropout_keep_prob = tf.placeholder("float", None, 'keep_prob')
    learning_rate = tf.placeholder("float", None, name='learning_rate')

    return inputs, labels, dropout_keep_prob, learning_rate

def get_im(path): # reading imagefile and resizing

    img = cv2.imread(path, 0)
    resize = cv2.resize(img, (image_size, image_size))
    #修正：cv2.resize(img, (image_size, image_size),1)
    return resize

def read_train_data(ho=0): # read train and validation data

    train_data = []
    train_target = []

    for j in range(3, 6): 

        path = 'c:/Users/mitsutaka/Documents/resize/data2/%i/*.png'%(j)#

        files = sorted(glob.glob(path))

        for fl in files:

            flbase = os.path.basename(fl)

            img = get_im(fl)
            img = np.array(img, dtype=np.float32)
            img -= np.mean(img)
            img /= np.std(img)
            tmp = np.zeros(3)
            tmp[int(j-3)] = 1

            train_data.append(img)
            train_target.append(tmp)

    train_data = np.array(train_data, dtype=np.float32)
    train_target = np.array(train_target, dtype=np.uint8)

    # shuffle
    #perm = permutation(len(train_target))#permutation:シャッフル
    #train_data = train_data[perm]
    #train_target = train_target[perm]

    return train_data, train_target

def read_test_data(test_class):

    path = 'c:/Users/mitsutaka/Documents/%i/*.png'%(test_class)#

    files = sorted(glob.glob(path))
    X_test = []
    X_test_id = []

    for fl in files:

        flbase = os.path.basename(fl)

        img = get_im(fl)#画像データの取得
        img = np.array(img, dtype=np.float32)
        img -= np.mean(img)#mean:平均
        img /= np.std(img)#std：標準偏差

        X_test.append(img)
        X_test_id.append(flbase)

    test_data = np.array(X_test, dtype=np.float32)

    return test_data, X_test_id

def inference(images_placeholder, keep_prob):

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


    def bias(shape):
        initial = tf.zeros(shape)
        return tf.Variable(initial)

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

    def MaxPooling(x):#tensorflow-APIより使用, 最大値を抽出
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides =[1, 2, 2, 1], padding='SAME')#[1, 2, 2, 1]は2*2の意味　（[1, 3, 3, 1]は3*3の意味）

    x_image = tf.reshape(images_placeholder, [-1, image_size, image_size, image_channnel])
    tf.summary.image('input', x_image, 10)


    with tf.name_scope('Convolution1') as scope:#畳み込み1層目
        W_conv1 = weight([3, 3, image_channnel, 32])
        b_conv1 = bias([32])#バイアス加算
        h_conv1 = tf.nn.relu(conv2d(images_placeholder, W_conv1) + b_conv1)#活性化関数reluを適応



    with tf.name_scope('Convolution2') as scope:
        W_conv2 = weight([3, 3, 32, 32])
        b_conv2 = bias([32])
        h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2) + b_conv2)



    with tf.name_scope('Pooling1') as scope:
        h_pool1 = MaxPooling(h_conv2)



    with tf.name_scope('Convolution3') as scope:
        W_conv3 = weight([3, 3, 32, 64])
        b_conv3 = bias([64])
        h_conv3 = tf.nn.relu(conv2d(h_pool1, W_conv3) + b_conv3)

    with tf.name_scope('Convolution4') as scope:
        W_conv4 = weight([3, 3, 64, 64])
        b_conv4 = bias([64])
        h_conv4 = tf.nn.relu(conv2d(h_conv3, W_conv4) + b_conv4)

    with tf.name_scope('Pooling2') as scope:
        h_pool2 = MaxPooling(h_conv4)

    with tf.name_scope('FullyConnected1') as scope:#fc:affineの意味
        flat = tf.contrib.layers.flatten(h_pool1)
        tensor_shape = flat.get_shape().as_list()
        W_fc1 = weight([tensor_shape[1], 1024])
        b_fc1 = bias([1024])
        h_fc1 = tf.nn.relu(tf.matmul(flat, W_fc1) + b_fc1)
        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    with tf.name_scope('FullyConnected2') as scope:
        W_fc2 = weight([1024, 1024])
        b_fc2 = bias([1024])
        h_fc2 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
        h_fc2_drop = tf.nn.dropout(h_fc2, keep_prob)

    with tf.name_scope('SoftMax') as scope:
        W_sf = weight([1024, 3])
        b_sf = bias([3])
        y = tf.nn.softmax(tf.matmul(h_fc2_drop, W_sf) + b_sf)

    return y

def loss(logits, labels):

    with tf.name_scope('loss'):
        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = labels))
        tf.summary.scalar('loss', loss)
    return loss

def training(loss, learning_rate):

    train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
    return train_step

def accuracy(logits, labels):

    with tf.name_scope('accuracy'):
        correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        tf.summary.scalar('accuracy', accuracy)
    return accuracy

def train():

    inputs, labels, dropout_keep_prob, learning_rate =input_placeholder(image_size, image_channnel, 3)
    #inputs=np.expand_dims(inputs,axis=-1)
    #修正箇所
    logits = inference(inputs, dropout_keep_prob)

    acc = accuracy(logits, labels)
    loss_value = loss(logits, labels)
    train = training(loss_value, learning_rate)

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

    merged = tf.summary.merge_all()
    train_writer = tf.summary.FileWriter(FLAGS.log_path + '/train/', sess.graph)
    validation_writer = tf.summary.FileWriter(FLAGS.log_path + '/validation/')

    saver = tf.train.Saver()
    if os.path.isfile(FLAGS.save_name):
        saver.restore(sess, FLAGS.save_name)

    ho = 0
    train_dataset, train_labels = read_train_data(ho)
    train_dataset=np.expand_dims(train_dataset,axis=-1)
    print(train_dataset.shape)
    validation_dataset, validation_labels = read_train_data(ho)
    validation_dataset=np.expand_dims(validation_dataset,axis=-1)


    i=0
    print("Initialized")
    cur_learning_rate = FLAGS.learning_rate

    t1 = time.time()

    for epoch in range(FLAGS.training_epoch):
        step = 0
        if epoch % 10 == 0 and epoch > 0:
            cur_learning_rate /= 10
        for start, end in zip(range(0, len(train_dataset), FLAGS.batch_size), range(FLAGS.batch_size, len(train_dataset), FLAGS.batch_size)):

            step += 1
            batch_data = train_dataset[start:end]
            batch_labels = train_labels[start:end]
            print(step)
            feed_dict_batch ={inputs: batch_data, labels: batch_labels, dropout_keep_prob: FLAGS.dropout_keep_prob, learning_rate: cur_learning_rate}

            if i% FLAGS.validation_interval == 0:
                summary, _, loss_batch, acc_batch = sess.run([merged, train, loss_value, acc], feed_dict=feed_dict_batch)
                train_writer.add_summary(summary, i)

                summary_valid, loss_valid, acc_valid = sess.run([merged, loss_value, acc], feed_dict={inputs: validation_dataset,
                                                                                                     labels: np.squeeze(validation_labels),
                                                                                                     dropout_keep_prob: 1.0})

                validation_writer.add_summary(summary_valid, i)
                print('----------------------------------')
                print('epoch:', epoch + 1, ', step:', step)
                print("Batch loss: {:.2f}".format(loss_batch))
                print("Batch accuracy: {0:.01%}".format(acc_batch))

                print("Validation loss: {:.2f}".format(loss_valid))
                print("Validation acuuracy: {0:.01%}".format(acc_valid))
            else:
                sess.run(train, feed_dict=feed_dict_batch)
            i+=1

    t2 = time.time()

    elapsed_time = t2-t1
    print(f"経過時間：{elapsed_time}")

    train_writer.close()
    validation_writer.close()

if __name__ == '__main__':

    param = sys.argv

    run_type = param[1]
    if run_type == 'train':
        train()