tensorflow cnn

#input_data

import os
import numpy as np
import tensorflow as tf

def get_files(filename):
    class_train = []
    label_train = []
    for train_class in os.listdir(filename):
        for pic in os.listdir(filename+train_class):
            class_train.append(filename+train_class+'/'+pic)
            label_train.append(train_class)
    temp = np.array([class_train,label_train])
    temp = temp.transpose()
    #shuffle the samples
    np.random.shuffle(temp)
    #after transpose, images is in dimension 0 and label in dimension 1
    image_list = list(temp[:,0])
    label_list = list(temp[:,1])
    label_list = [int(i) for i in label_list]
    #print(label_list)
    return image_list,label_list


def get_batches(image, label, resize_w, resize_h, batch_size, capacity):
    # convert the list of images and labels to tensor
    image = tf.cast(image, tf.string)
    label = tf.cast(label, tf.int64)
    queue = tf.train.slice_input_producer([image, label])
    label = queue[1]
    image_c = tf.read_file(queue[0])
    image = tf.image.decode_jpeg(image_c, channels=3)
    # resize
    image = tf.image.resize_image_with_crop_or_pad(image, resize_w, resize_h)
    # (x - mean) / adjusted_stddev
    image = tf.image.per_image_standardization(image)

    image_batch, label_batch = tf.train.batch([image, label],
                                              batch_size=batch_size,
                                              num_threads=64,
                                              capacity=capacity)
    images_batch = tf.cast(image_batch, tf.float32)
    labels_batch = tf.reshape(label_batch, [batch_size])
    return images_batch, labels_batch

#model

import os
import numpy as np
import tensorflow as tf

def mmodel(images,batch_size):
    with tf.variable_scope('conv1') as scope:
        weights = tf.get_variable('weights',
                                  shape = [3,3,3, 16],
                                  dtype = tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[16],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        conv = tf.nn.conv2d(images, weights, strides=[1,1,1,1], padding='SAME')
        pre_activation = tf.nn.bias_add(conv, biases)
        conv1 = tf.nn.relu(pre_activation, name= scope.name)
    with tf.variable_scope('pooling1_lrn') as scope:
        pool1 = tf.nn.max_pool(conv1, ksize=[1,2,2,1],strides=[1,2,2,1],
                               padding='SAME', name='pooling1')
        norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001/9.0,
                          beta=0.75,name='norm1')
    with tf.variable_scope('conv2') as scope:
        weights = tf.get_variable('weights',
                                  shape=[3,3,16,128],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[128],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        conv = tf.nn.conv2d(norm1, weights, strides=[1,1,1,1],padding='SAME')
        pre_activation = tf.nn.bias_add(conv, biases)
        conv2 = tf.nn.relu(pre_activation, name='conv2')
    with tf.variable_scope('pooling2_lrn') as scope:
        norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001/9.0,
                          beta=0.75,name='norm2')
        pool2 = tf.nn.max_pool(norm2, ksize=[1,2,2,1], strides=[1,1,1,1],
                               padding='SAME',name='pooling2')
    with tf.variable_scope('local3') as scope:
        reshape = tf.reshape(pool2, shape=[batch_size, -1])
        dim = reshape.get_shape()[1].value
        weights = tf.get_variable('weights',
                                  shape=[dim,4096],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[4096],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
    with tf.variable_scope('softmax_linear') as scope:
        weights = tf.get_variable('softmax_linear',
                                  shape=[4096, 2],
                                  dtype=tf.float32,
                                  initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32))
        biases = tf.get_variable('biases',
                                 shape=[2],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(0.1))
        softmax_linear = tf.add(tf.matmul(local3, weights), biases, name='softmax_linear')
    return softmax_linear


def loss(logits,label_batches):
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,labels=label_batches)
    cost = tf.reduce_mean(cross_entropy)
    return cost

def get_accuracy(logits,labels):
    acc = tf.nn.in_top_k(logits,labels,1)
    acc = tf.cast(acc,tf.float32)
    acc = tf.reduce_mean(acc)
    return acc

def training(loss,lr):
    train_op = tf.train.AdamOptimizer(lr).minimize(loss)
    return train_op

#train

import os
import tensorflow as tf
import numpy as np
import input_data
import model


def run_training():
    data_dir = 'E:\\data\\dataset\\man_woman_flower\\'
    log_dir = 'E:\\tensorflow\\CNN_classifier_1\\logs\\man_woman\\'
    image, label = input_data.get_files(data_dir)
    image_batches, label_batches = input_data.get_batches(image, label, 32, 32, 32, 20)
    print(image_batches.shape)
    p = model.mmodel(image_batches, 32)
    cost = model.loss(p, label_batches)
    train_op = model.training(cost, 0.001)
    acc = model.get_accuracy(p, label_batches)

    sess = tf.Session()
    init = tf.global_variables_initializer()
    sess.run(init)
    saver = tf.train.Saver()
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)

    try:
        max_acc = 0
        for epoch in range(4000):
            for i in range(300):
                if coord.should_stop():
                    break
                tra_acc = 0
                _, train_acc, train_loss = sess.run([train_op, acc, cost])
                print("Iter:%d,loss:%.3f,accuracy:%.2f%%" %(epoch,train_loss, train_acc))
                tra_acc += train_acc
            if max_acc < tra_acc:
                max_acc = tra_acc
                check = os.path.join(log_dir, "model.ckpt")
                saver.save(sess, check)
    except tf.errors.OutOfRangeError:
        print("Done!!!")
    finally:
        coord.request_stop()
    coord.join(threads)
    sess.close()

run_training()

# evaluation

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import tensorflow as tf
import model

def get_one_image(img_dir):
    image = Image.open(img_dir)
    plt.imshow(image)
    image = image.resize([32, 32])
    image_arr = np.array(image)
    return image_arr


def test(test_file):
    log_dir = 'E:\\tensorflow\\CNN_classifier_1\\logs\\man_woman\\'
    image_arr = get_one_image(test_file)

    with tf.Graph().as_default():
        image = tf.cast(image_arr, tf.float32)
        image = tf.image.per_image_standardization(image)
        image = tf.reshape(image, [1, 32, 32, 3])
        print(image.shape)
        p = model.mmodel(image, 1)
        logits = tf.nn.softmax(p)
        x = tf.placeholder(tf.float32, shape=[32, 32, 3])
        saver = tf.train.Saver()
        with tf.Session() as sess:
            ckpt = tf.train.get_checkpoint_state(log_dir)
            if ckpt and ckpt.model_checkpoint_path:
                global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                saver.restore(sess, ckpt.model_checkpoint_path)
                print('Loading success')
            else:
                print('No checkpoint')
            prediction = sess.run(logits, feed_dict={x: image_arr})
            max_index = np.argmax(prediction)
            print(max_index)

test("111.jpg")