from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from absl import app as absl_app
from absl import flags
import tensorflow as tf  # pylint: disable=g-bad-import-order

from mnist import dataset
from mnist.utils.flags import core as flags_core
from mnist.utils.logs import hooks_helper
from mnist.utils.misc import distribution_utils
from mnist.utils.misc import model_helpers

LEARNING_RATE = 1e-4

def create_model(data_format):
  """Model to recognize digits in the MNIST dataset.
  Network structure is equivalent to:
  https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/examples/tutorials/mnist/mnist_deep.py
  and
  https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py

  But uses the tf.keras API.

  Args:
    data_format: Either 'channels_first' or 'channels_last'. 'channels_first' is
      typically faster on GPUs while 'channels_last' is typically faster on
      CPUs. See
      https://www.tensorflow.org/performance/performance_guide#data_formats

  Returns:
    A tf.keras.Model.
  """
  #定义数据格式
  if data_format == 'channels_first':
    input_shape = [1, 28, 28]
  else:
    assert data_format == 'channels_last'
    input_shape = [28, 28, 1]

  # 定义模块开始
  l = tf.keras.layers
  #定义pool层
  max_pool = l.MaxPooling2D((2, 2), (2, 2), padding='same', data_format=data_format)
  # The model consists of a sequential chain of layers, so tf.keras.Sequential
  # (a subclass of tf.keras.Model) makes for a compact description.

  #模块结构
  return tf.keras.Sequential(
      [#训练数据是一维的，重新reshape为二维（28*28）
          l.Reshape(
              target_shape=input_shape,
              input_shape=(28 * 28,)),
          l.Conv2D(#第一层卷积
              32,#深度
              5,#卷积核大小（5*5）
              padding='same',
              data_format=data_format,
              activation=tf.nn.relu),
          max_pool,#第一层pool
          l.Conv2D(#第二次卷积
              64,
              5,
              padding='same',
              data_format=data_format,
              activation=tf.nn.relu),
          max_pool,#第二层pool
          l.Flatten(),#摊平成一维神经网络
          l.Dense(1024, activation=tf.nn.relu),#第一层神经网络
          l.Dropout(0.4),#drop out（作用类似正则化，避免过度拟合）
          l.Dense(10)#输出
      ])

# 训练参数（文件地址等）
def define_mnist_flags():
  flags_core.define_base()
  flags_core.define_image()
  flags.adopt_module_key_flags(flags_core)
  flags_core.set_defaults(data_dir='/tmp/mnist_data',
                          model_dir='/tmp/mnist_model',
                          batch_size=100,
                          train_epochs=40)


def model_fn(features, labels, mode, params):
  """The model_fn argument for creating an Estimator."""
  model = create_model(params['data_format'])
  image = features
  if isinstance(image, dict):
    image = features['image']

# 预测模式
  if mode == tf.estimator.ModeKeys.PREDICT:
    logits = model(image, training=False)
    predictions = {
        'classes': tf.argmax(logits, axis=1),  # 测试结果矩阵转数字
        'probabilities': tf.nn.softmax(logits),  # 输出使用函数
    }
    return tf.estimator.EstimatorSpec(
        mode=tf.estimator.ModeKeys.PREDICT,
        predictions=predictions,#必填
        export_outputs={
            'classify': tf.estimator.export.PredictOutput(predictions)
        })

# 训练模式
  if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE)  # 此函数是Adam优化算法：是一个寻找全局最优点的优化算法，引入了二次方梯度校正。相比于基础SGD算法，1.不容易陷于局部优点。2.速度更快

    # If we are running multi-GPU, we need to wrap the optimizer.
    if params.get('multi_gpu'):  # 如果有多个GPU
      optimizer = tf.contrib.estimator.TowerOptimizer(optimizer)

    logits = model(image, training=True)
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)  # 定义损失函数

    accuracy = tf.metrics.accuracy(labels=labels, predictions=tf.argmax(logits, axis=1))  # 计算准确率（评估效果，与训练过程无关）

    # 用于在tensorboard中显示
    # Name tensors to be logged with LoggingTensorHook.
    tf.identity(LEARNING_RATE, 'learning_rate')
    tf.identity(loss, 'cross_entropy')
    tf.identity(accuracy[1], name='train_accuracy')
    # Save accuracy scalar to Tensorboard output.
    tf.summary.scalar('train_accuracy', accuracy[1])

    return tf.estimator.EstimatorSpec(
        mode=tf.estimator.ModeKeys.TRAIN,
        loss=loss,
        train_op=optimizer.minimize(loss, tf.train.get_or_create_global_step()))

# 评估模式
  if mode == tf.estimator.ModeKeys.EVAL:
    logits = model(image, training=False)
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
    return tf.estimator.EstimatorSpec(
        mode=tf.estimator.ModeKeys.EVAL,
        loss=loss,#必填
        eval_metric_ops={
            'accuracy':
                tf.metrics.accuracy(labels=labels, predictions=tf.argmax(logits, axis=1)),
        })


def run_mnist(flags_obj):
  """Run MNIST training and eval loop.
  Args:
    flags_obj: An object containing parsed flag values.
  """
  model_helpers.apply_clean(flags_obj)
  model_function = model_fn

#多GUP处理
  # Get number of GPUs as defined by the --num_gpus flags and the number of
  # GPUs available on the machine.
  num_gpus = flags_core.get_num_gpus(flags_obj)
  multi_gpu = num_gpus > 1

  if multi_gpu:
    # Validate that the batch size can be split into devices.
    distribution_utils.per_device_batch_size(flags_obj.batch_size, num_gpus)

    # There are two steps required if using multi-GPU: (1) wrap the model_fn,
    # and (2) wrap the optimizer. The first happens here, and (2) happens
    # in the model_fn itself when the optimizer is defined.
    model_function = tf.contrib.estimator.replicate_model_fn(
        model_fn, loss_reduction=tf.losses.Reduction.MEAN,
        devices=["/device:GPU:%d" % d for d in range(num_gpus)])

#选择用CPU还是GPU处理
  data_format = flags_obj.data_format
  if data_format is None:#格式影响速度
    data_format = ('channels_first'
                   if tf.test.is_built_with_cuda() else 'channels_last')
####################################################
 #正式运行
  mnist_classifier = tf.estimator.Estimator(
      model_fn=model_function,
      model_dir=flags_obj.model_dir,
      params={
          'data_format': data_format,
          'multi_gpu': multi_gpu
      })

#设置输入数据
  # Set up training and evaluation input functions.
  def train_input_fn():
    """Prepare data for training."""
    # When choosing shuffle buffer sizes, larger sizes result in better
    # randomness, while smaller sizes use less memory. MNIST is a small
    # enough dataset that we can easily shuffle the full epoch.
    ds = dataset.train(flags_obj.data_dir)    
    ds = ds.cache().shuffle(buffer_size=50000).batch(flags_obj.batch_size)
    # Iterate through the dataset a set number (`epochs_between_evals`) of times
    # during each training session.
    ds = ds.repeat(flags_obj.epochs_between_evals)
    return ds

#设置评估数据
  def eval_input_fn():
    return dataset.test(flags_obj.data_dir).batch(
        flags_obj.batch_size).make_one_shot_iterator().get_next()

#每100步输出一个log（用于tensorboard）
  # Set up hook that outputs training logs every 100 steps.
  train_hooks = hooks_helper.get_train_hooks(
      flags_obj.hooks, model_dir=flags_obj.model_dir,
      batch_size=flags_obj.batch_size)

  # Train and evaluate model.
  for _ in range(flags_obj.train_epochs // flags_obj.epochs_between_evals):
    mnist_classifier.train(input_fn=train_input_fn, hooks=train_hooks)
    eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
    print('\nEvaluation results:\n\t%s\n' % eval_results)

    if model_helpers.past_stop_threshold(flags_obj.stop_threshold,
                                         eval_results['accuracy']):
      break

  # Export the model
  if flags_obj.export_dir is not None:
    image = tf.placeholder(tf.float32, [None, 28, 28])
    input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
        'image': image,
    })
    mnist_classifier.export_savedmodel(flags_obj.export_dir, input_fn)


def main(_):    

    run_mnist(flags.FLAGS)


if __name__ == '__main__':
  tf.logging.set_verbosity(tf.logging.INFO)
  define_mnist_flags()
  absl_app.run(main)