tensorflowのバージョンは1.1.0を使用しています。

複数のモノクロ画像を学習して予測モデルを作りたいのですが、以下のようなエラーがでており詰まっています。

 発生している問題・エラーメッセージ

Traceback (most recent call last):
  File "<stdin>", line 48, in <module>
  File "C:\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 778, in run
    run_metadata_ptr)
  File "C:\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 961, in _run
    % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (256, 1875) for Tensor 'Placeholder:0', which has shape '(?, 625)'

 該当のソースコード

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import cv2
import time
import random
import numpy as np
import tensorflow as tf
import tensorflow.python.platform

NUM_CLASSES = 2
IMAGE = 25
CHANNEL = 1
IMAGE_PIXELS = IMAGE*IMAGE*CHANNEL
zenketu = 7

flags = tf.app.flags
FLAGS = flags.FLAGS

flags.DEFINE_string('train', './train.txt', 'File name of train data')

flags.DEFINE_string('test', './test.txt', 'File name of train data')

flags.DEFINE_string('train_dir', './Learnimage', 'Directory to put the training data.')

flags.DEFINE_integer('max_steps', 100, 'Number of steps to run trainer.')

flags.DEFINE_integer('batch_size',256, 'Batch size Must divide evenly into the dataset sizes.')

flags.DEFINE_float('learning_rate', 1e-5, 'Initial learning rate.')

sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True))

def inference(images_placeholder, keep_prob):
    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, 1, 1, 1],strides=[1, 2, 2, 1], padding='SAME')

    x_image = tf.reshape(images_placeholder, [-1, IMAGE, IMAGE, CHANNEL])

    with tf.name_scope('conv1') as scope:
        W_conv1 = weight_variable([5, 5, CHANNEL, IMAGE])
        b_conv1 = bias_variable([IMAGE])
        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

    with tf.name_scope('pool1') as scope:
        h_pool1 = max_pool_2x2(h_conv1)

    with tf.name_scope('conv2') as scope:
        W_conv2 = weight_variable([5, 5, IMAGE, (IMAGE*2)])
        b_conv2 = bias_variable([IMAGE*2])
        h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

    with tf.name_scope('pool2') as scope:
        h_pool2 = max_pool_2x2(h_conv2)


    with tf.name_scope('fc1') as scope:
        W_fc1 = weight_variable([zenketu*zenketu*(IMAGE*2), 1024])
        b_fc1 = bias_variable([1024])
        h_pool2_flat = tf.reshape(h_pool2, [-1, zenketu*zenketu*(IMAGE*2)])   
        h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    with tf.name_scope('fc2') as scope:
        W_fc2 = weight_variable([1024, NUM_CLASSES])
        b_fc2 = bias_variable([NUM_CLASSES])

    with tf.name_scope('softmax') as scope:
        y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
        return y_conv

def loss(logits, labels):
    cross_entropy = -tf.reduce_sum(labels*tf.log(logits))
    tf.summary.scalar("cross_entropy", cross_entropy)
    return cross_entropy

def training(loss, learning_rate):
    train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
    return train_step

def accuracy(logits, labels):
    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    tf.summary.scalar("accuracy", accuracy)
    return accuracy


if __name__ == '__main__':

    f = open(FLAGS.train, 'r')
    train_image = []
    train_label = []
    for line in f:
        line = line.rstrip()
        l = line.split()
        img = cv2.imread(l[0])
        img = cv2.resize(img, (IMAGE, IMAGE))
        train_image.append(img.flatten().astype(np.float32)/255.0)
        tmp = np.zeros(NUM_CLASSES)
        tmp[int(l[1])] = 1
        train_label.append(tmp)

    train_image = np.asarray(train_image)
    train_label = np.asarray(train_label)
    f.close()

    f = open(FLAGS.test, 'r')
    test_image = []
    test_label = []
    for line in f:
        line = line.rstrip()
        l = line.split()
        img = cv2.imread(l[0])
        img = cv2.resize(img, (IMAGE, IMAGE))
        test_image.append(img.flatten().astype(np.float32)/255.0)
        tmp = np.zeros(NUM_CLASSES)
        tmp[int(l[1])] = 1
        test_label.append(tmp)
    test_image = np.asarray(test_image)
    test_label = np.asarray(test_label)
    f.close()

    with tf.Graph().as_default():
        images_placeholder = tf.placeholder("float", shape=(None,IMAGE_PIXELS))
        labels_placeholder = tf.placeholder("float", shape=(None, NUM_CLASSES))
        keep_prob = tf.placeholder("float")
        logits = inference(images_placeholder, keep_prob)
        loss_value = loss(logits, labels_placeholder)
        train_op = training(loss_value, FLAGS.learning_rate)
        acc = accuracy(logits, labels_placeholder)
        saver = tf.train.Saver()
        sess = tf.Session() 
        sess.run(tf.global_variables_initializer())
        summary_op = tf.summary.merge_all()
        summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)

        for step in range(FLAGS.max_steps):
            for i in range(len(train_image)//FLAGS.batch_size):
                batch = FLAGS.batch_size*i
                sess.run(train_op, feed_dict={images_placeholder: train_image[batch:batch+FLAGS.batch_size],
                                              labels_placeholder: train_label[batch:batch+FLAGS.batch_size],
                                              keep_prob: 0.5})
            train_accuracy = sess.run(acc,
                                      feed_dict={images_placeholder: train_image,
                                                 labels_placeholder: train_label,
                                                 keep_prob: 1.0})
            print( "step %d, training accuracy %g"%(step, train_accuracy))
            summary_str = sess.run(summary_op,
                                   feed_dict={images_placeholder: train_image,
                                              labels_placeholder: train_label,
                                              keep_prob: 1.0})
            summary_writer.add_summary(summary_str, step)
    print ("test accuracy %g"%sess.run(acc,
                                       feed_dict={images_placeholder: test_image,
                                                  labels_placeholder: test_label,
                                                  keep_prob: 1.0}))
    save_path = saver.save(sess,"./model.ckpt")

試したこと

CHANNEL = 3(カラー画像)の場合だと問題なくプログラムが動きます。
しかし、
この値を1(モノクロ画像)にするとエラーがでて、プログラムが動きません。