p410_inception_resnet_v2_flowers.py

import os
import tensorflow as tf
import tensorflow.contrib.slim as slim
import inception_resnet_v2 as model

checkpoints_dir = "./data/model"
flowers_data_dir = "./data/flower_photos"

def load_batch(dataset, batch_size=4, height=299, width=299, is_training=True):
    import tensorflow.contrib.slim.python.slim.data.dataset_data_provider as provider
    data_provider = provider.DatasetDataProvider(dataset, common_queue_capacity=8, common_queue_min=1)
    image_raw, label = data_provider.get(['image', 'label'])
    image_raw = tf.image.resize_images(image_raw, [height, width])
    image_raw = tf.image.convert_image_dtype(image_raw, tf.float32)

    images_raw, labels = tf.train.batch([image_raw, label],
                                        batch_size=batch_size,
                                        num_threads=1,
                                        capacity=2 * batch_size)
    return images_raw, labels


fintuning = tf.Graph()
with fintuning.as_default():
    tf.logging.set_verbosity(tf.logging.INFO)

    # 获取数据集
    from datasets import flowers
    dataset = flowers.get_split("train", flowers_data_dir)
    images, labels = load_batch(dataset)

    # 载入模型，此时模型未载入参数
    with slim.arg_scope(model.inception_resnet_v2_arg_scope()):
        pre, _ = model.inception_resnet_v2(images, num_classes=1001)
    probabilities = tf.nn.softmax(pre)

    # 对标签进行格式化处理
    one_hot_labels = slim.one_hot_encoding(labels, 1001)

    # 创建损失函数
    slim.losses.softmax_cross_entropy(probabilities, one_hot_labels)
    total_loss = slim.losses.get_total_loss()

    # 创建训练节点
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
    train_op = slim.learning.create_train_op(total_loss, optimizer)

    # 创建载入模型权重的函数
    model_path = "./data/model/inception_resnet_v2_2016_08_30.ckpt"
    variables = slim.get_model_variables("InceptionResnetV2")
    init_fn = slim.assign_from_checkpoint_fn(model_path, variables)

    with tf.Session() as sess:
        init_fn(sess)
        print("done")
        final_loss = slim.learning.train(train_op, logdir=None, number_of_steps=10)