華爲雲AI-深度學習糖尿病預測

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 15 10:54:53 2018
@author: myhaspl
@email:myhaspl@myhaspl.com
糖尿病預測（多層）
csv格式：懷孕次數、葡萄糖、血壓、皮膚厚度，胰島素,bmi,糖尿病血統函數,年齡,結果
"""

import tensorflow as tf
import os

trainCount=10000
inputNodeCount=8
validateCount=50
sampleCount=200
testCount=10
outputNodeCount=1

g=tf.Graph()
with g.as_default():

    def getWeights(shape,wname):
        weights=tf.Variable(tf.truncated_normal(shape,stddev=0.1),name=wname)
        return weights

    def getBias(shape,bname):
        biases=tf.Variable(tf.constant(0.1,shape=shape),name=bname)
        return biases

    def inferenceInput(x):
        layer1=tf.nn.relu(tf.add(tf.matmul(x,w1),b1))    
        result=tf.add(tf.matmul(layer1,w2),b2)
        return result

    def inference(x):
        yp=inferenceInput(x)
        return tf.sigmoid(yp)

    def loss():
        yp=inferenceInput(x)
        return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y,logits=yp))

    def train(learningRate,trainLoss,trainStep):
        trainOp=tf.train.AdamOptimizer(learningRate).minimize(trainLoss,global_step=trainStep)
        return trainOp

    def evaluate(x):
        return tf.cast(inference(x)>0.5,tf.float32)

    def accuracy(x,y,count):
        yp=evaluate(x)
        return tf.reduce_mean(tf.cast(tf.equal(yp,y),tf.float32))

    def inputFromFile(fileName,skipLines=1):
        #生成文件名隊列
        fileNameQueue=tf.train.string_input_producer([fileName])
        #生成記錄鍵值對
        reader=tf.TextLineReader(skip_header_lines=skipLines)
        key,value=reader.read(fileNameQueue)
        return value

    def getTestData(fileName,skipLines=1,n=10):
        #生成文件名隊列
        testFileNameQueue=tf.train.string_input_producer([fileName])
        #生成記錄鍵值對
        testReader=tf.TextLineReader(skip_header_lines=skipLines)
        testKey,testValue=testReader.read(testFileNameQueue)
        testRecordDefaults=[[1.],[1.],[1.],[1.],[1.],[1.],[1.],[1.],[1.]]
        testDecoded=tf.decode_csv(testValue,record_defaults=testRecordDefaults)
        pregnancies,glucose,bloodPressure,skinThickness,insulin,bmi,diabetespedigreefunction,age,outcome=tf.train.shuffle_batch(testDecoded,batch_size=n,capacity=1000,min_after_dequeue=1)    
        testFeatures=tf.transpose(tf.stack([pregnancies,glucose,bloodPressure,skinThickness,insulin,bmi,diabetespedigreefunction,age]))
        testY=tf.transpose([outcome])
        return (testFeatures,testY)

    def getNextBatch(n,values):
        recordDefaults=[[1.],[1.],[1.],[1.],[1.],[1.],[1.],[1.],[1.]]
        decoded=tf.decode_csv(values,record_defaults=recordDefaults)
        pregnancies,glucose,bloodPressure,skinThickness,insulin,bmi,diabetespedigreefunction,age,outcome=tf.train.shuffle_batch(decoded,batch_size=n,capacity=1000,min_after_dequeue=1)    
        features=tf.transpose(tf.stack([pregnancies,glucose,bloodPressure,skinThickness,insulin,bmi,diabetespedigreefunction,age]))
        y=tf.transpose([outcome])
        return (features,y) 

    with tf.name_scope("inputSample"): 
        samples=inputFromFile("s3://myhaspl/tf_learn/diabetes.csv",1)
        inputDs=getNextBatch(sampleCount,samples)  
        with tf.name_scope("validateSamples"):
            validateInputs=getNextBatch(validateCount,samples)

    with tf.name_scope("testSamples"):  
        testInputs=getTestData("s3://myhaspl/tf_learn/diabetes_test.csv")

    with tf.name_scope("inputDatas"):
        x=tf.placeholder(dtype=tf.float32,shape=[None,inputNodeCount],name="input_x")
        y=tf.placeholder(dtype=tf.float32,shape=[None,outputNodeCount],name="input_y")

    with tf.name_scope("Variable"):
        w1=getWeights([inputNodeCount,12],"w1")
        b1=getBias((),"b1")       
        w2=getWeights([12,outputNodeCount],"w2")
        b2=getBias((),"b2")       
        trainStep=tf.Variable(0,dtype=tf.int32,name="tcount",trainable=False)      

    with tf.name_scope("train"):
        trainLoss=loss()    
        trainOp=train(0.005,trainLoss,trainStep)
        init=tf.global_variables_initializer()         

with tf.Session(graph=g) as sess:    
    sess.run(init)

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    while trainStep.eval()<trainCount: 

        sampleX,sampleY=sess.run(inputDs)
        sess.run(trainOp,feed_dict={x:sampleX,y:sampleY})
        nowStep=sess.run(trainStep)
        if nowStep%500==0:
            validate_acc=sess.run(accuracy(sampleX,sampleY,sampleCount))
            print "%d次後=>正確率%g"%(nowStep,validate_acc)
        if nowStep>trainCount:
            break

    testInputX,testInputY=sess.run(testInputs)
    print "測試樣本正確率%g"%sess.run(accuracy(testInputX,testInputY,testCount))
    print testInputX,testInputY
    print sess.run(evaluate(testInputX))

    coord.request_stop()
    coord.join(threads)

500次後=>正確率0.67
1000次後=>正確率0.75
1500次後=>正確率0.81
2000次後=>正確率0.75
2500次後=>正確率0.775
3000次後=>正確率0.765
3500次後=>正確率0.84
4000次後=>正確率0.85
4500次後=>正確率0.77
5000次後=>正確率0.78
5500次後=>正確率0.775
6000次後=>正確率0.835
6500次後=>正確率0.84
7000次後=>正確率0.785
7500次後=>正確率0.805
8000次後=>正確率0.765
8500次後=>正確率0.83
9000次後=>正確率0.835
9500次後=>正確率0.78
10000次後=>正確率0.775
測試樣本正確率0.7
[[1.00e+01 1.01e+02 7.60e+01 4.80e+01 1.80e+02 3.29e+01 1.71e-01 6.30e+01]
 [3.00e+00 7.80e+01 5.00e+01 3.20e+01 8.80e+01 3.10e+01 2.48e-01 2.60e+01]
 [2.00e+00 1.22e+02 7.00e+01 2.70e+01 0.00e+00 3.68e+01 3.40e-01 2.70e+01]
 [2.00e+00 8.80e+01 5.80e+01 2.60e+01 1.60e+01 2.84e+01 7.66e-01 2.20e+01]
 [1.00e+01 1.01e+02 7.60e+01 4.80e+01 1.80e+02 3.29e+01 1.71e-01 6.30e+01]
 [2.00e+00 1.22e+02 7.00e+01 2.70e+01 0.00e+00 3.68e+01 3.40e-01 2.70e+01]
 [1.00e+00 8.90e+01 6.60e+01 2.30e+01 9.40e+01 2.81e+01 1.67e-01 2.10e+01]
 [6.00e+00 1.48e+02 7.20e+01 3.50e+01 0.00e+00 3.36e+01 6.27e-01 5.00e+01]
 [1.00e+00 9.30e+01 7.00e+01 3.10e+01 0.00e+00 3.04e+01 3.15e-01 2.30e+01]
 [2.00e+00 1.22e+02 7.00e+01 2.70e+01 0.00e+00 3.68e+01 3.40e-01 2.70e+01]] [[0.]
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 [1.]
 [0.]
 [0.]]
[[1.]
 [0.]
 [0.]
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 [1.]
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 [1.]
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感受華爲雲中提供的深度學習服務，就是給你提供一個強大的服務器，而後，你本身編寫代碼。可能還提供了一些更多的功能

另外，提供了一個訓練用戶自定義數據的代碼
補充一個概念：
MoXing是華爲雲深度學習服務提供的網絡模型開發API。相對於TensorFlow和MXNet等原生API而言，MoXing API讓模型的代碼編寫更加簡單，並且可以自動獲取高性能的分佈式執行能力。

MoXing容許用戶只須要關心數據輸入(input_fn)和模型構建(model_fn)的代碼，就能夠實現任意模型在多GPU和分佈式下的高性能運行。MoXing-TensorFlow支持原生TensorFlow、Keras、slim等API，幫助構建圖像分類、物體檢測、生成對抗、天然語言處理和OCR等多種模型。

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

import tensorflow as tf
import moxing.tensorflow as mox
slim = tf.contrib.slim

# 用TensorFlow原生的方式定義超參
tf.flags.DEFINE_string('data_url', None, '')
tf.flags.DEFINE_string('train_dir', None, '')

flags = tf.flags.FLAGS

def train_my_model():

  def input_fn(run_mode, **kwargs):
    # 從TFRecord中獲取輸入數據集
    keys_to_features = {
      'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
      'image/format': tf.FixedLenFeature((), tf.string, default_value='raw'),
      'image/class/label': tf.FixedLenFeature(
        [1], tf.int64, default_value=tf.zeros([1], dtype=tf.int64)),
    }

    items_to_handlers = {
      'image': slim.tfexample_decoder.Image(shape=[28, 28, 1], channels=1),
      'label': slim.tfexample_decoder.Tensor('image/class/label', shape=[]),
    }
    # 數據集中包含60000張訓練集圖像（數據文件名爲mnist_train.tfrecord）
    # 以及10000張驗證集圖像（數據文件名爲mnist_test.tfrecord）
    dataset = mox.get_tfrecord(dataset_dir=flags.data_url,
                               file_pattern='mnist_train.tfrecord' if run_mode == mox.ModeKeys.TRAIN else 'mnist_test.tfrecord',
                               num_samples=60000 if run_mode == mox.ModeKeys.TRAIN else 10000,
                               keys_to_features=keys_to_features,
                               items_to_handlers=items_to_handlers,
                               capacity=1000)

    image, label = dataset.get(['image', 'label'])
    # 將圖像像素值轉換爲float並統一大小
    image = tf.to_float(image)
    image = tf.image.resize_image_with_crop_or_pad(image, 28, 28)
    return image, label

  def model_fn(inputs, run_mode, **kwargs):
    # 獲取一批輸入數據
    images, labels = inputs
    # 將輸入圖像進行歸一化
    images = tf.subtract(images, 128.0)
    images = tf.div(images, 128.0)
    # 定義函數參數做用域：
    # 1. 全部的卷積和全連接L2正則項係數爲0
    # 2. 全部的卷積和全連接使用截斷正態分佈初始化待訓練變量
    # 3. 全部的卷積和全連接的激活層採用ReLU
    with slim.arg_scope(
        [slim.conv2d, slim.fully_connected],
        weights_regularizer=slim.l2_regularizer(scale=0.0),
        weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
        activation_fn=tf.nn.relu):
      # 定義網絡
      net = slim.conv2d(images, 32, [5, 5])
      net = slim.max_pool2d(net, [2, 2], 2)
      net = slim.conv2d(net, 64, [5, 5])
      net = slim.max_pool2d(net, [2, 2], 2)
      net = slim.flatten(net)
      net = slim.fully_connected(net, 1024)
      net = slim.dropout(net, 0.5, is_training=True)
      logits = slim.fully_connected(net, 10, activation_fn=None)
    labels_one_hot = slim.one_hot_encoding(labels, 10)
    # 定義交叉熵損失值
    loss = tf.losses.softmax_cross_entropy(
      logits=logits, onehot_labels=labels_one_hot,
      label_smoothing=0.0, weights=1.0)
    # 因爲函數參數做用域定義了全部L2正則項係數爲0，因此這裏將不會獲取到任何L2正則項
    regularization_losses = mox.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)

    if len(regularization_losses) > 0:
      regularization_loss = tf.add_n(regularization_losses)
      loss += regularization_loss
    # 定義評價指標
    accuracy_top_1 = tf.reduce_mean(tf.cast(tf.nn.in_top_k(logits, labels, 1), tf.float32))
    accuracy_top_5 = tf.reduce_mean(tf.cast(tf.nn.in_top_k(logits, labels, 5), tf.float32))
    # 必須返回mox.ModelSpec
    return mox.ModelSpec(loss=loss,
                         log_info={'loss': loss, 'top1': accuracy_top_1, 'top5': accuracy_top_5})

  # 獲取一個內置的Optimizer
  optimizer_fn = mox.get_optimizer_fn('sgd', learning_rate=0.01)

  # 啓動訓練
  mox.run(input_fn=input_fn,
          model_fn=model_fn,
          optimizer_fn=optimizer_fn,
          run_mode=mox.ModeKeys.TRAIN,
          batch_size=50,
          log_dir=flags.train_dir,
          max_number_of_steps=2000,
          log_every_n_steps=10,
          save_summary_steps=50,
          save_model_secs=60)

if __name__ == '__main__':
  train_my_model()