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scikit Flow ，tensor flow 做ml模型
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【https://github.com/ilblackdragon/tf_examples/blob/master/titanic.py】
【keras 高层tensorflow】https://keras.io/getting-started/sequential-model-guide/
Scikit Flow封装了很多的TensorFlow的最新的API，并且将它们封装成了很类似于Scikit Learn API的样式。TensorFlow的核心即是基于构建与执行某个图，这是一个非常棒，但也是非常难以直接上手的概念。如果我们看Scikit Flow的底层封装，我们可以看到整个模型被分为了以下几个部分：
TensorFlowTrainer — 用于寻找所有优化器的类(使用梯度进行了部分的图构建，进行了一些梯度裁剪并且添加一些优化器)
logistic_regression —用于构造Logistic回归图的函数
linear_regression — 用于构造线性回归图的函数
DataFeeder — 用于将训练数据填充到模型中 (由于TensorFlow使用了数据集合中的随机的一些部分作为随机梯度下降的数据，因此需要这样的Mini数据批处理)。
TensorFlowLinearClassifier — 用LogisticRegression 模型实现了 Scikit Learn提供的某个接口。它提供了一个模型和一个训练器，并且根据给定的数据集合利用fit()方法进行数据训练，并且通过predict()方法进行预测。
TensorFlowLinearRegressor — 类似于 TensorFlowClassifier, 但是使用LinearRegression 作为模型。
import random
import pandas
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.utils import check_array
from sklearn.cross_validation import train_test_split import tensorflow as tf
from tensorflow.contrib import layers
from tensorflow.contrib import learn train = pandas.read_csv('data/titanic_train.csv')
y, X = train['Survived'], train[['Age', 'SibSp', 'Fare']].fillna(0)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) lr = LogisticRegression()
lr.fit(X_train, y_train)
print(accuracy_score(lr.predict(X_test), y_test)) # Linear classifier. 内部是logistics模型 random.seed(42)
tflr = learn.LinearClassifier(n_classes=2,
feature_columns=learn.infer_real_valued_columns_from_input(X_train),
optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.05))
tflr.fit(X_train, y_train, batch_size=128, steps=500)
print(accuracy_score(tflr.predict(X_test), y_test)) # 3 layer neural network with rectified linear activation. random.seed(42)
classifier = learn.DNNClassifier(hidden_units=[10, 20, 10],
n_classes=2,
feature_columns=learn.infer_real_valued_columns_from_input(X_train),
optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.05))
classifier.fit(X_train, y_train, batch_size=128, steps=500)
print(accuracy_score(classifier.predict(X_test), y_test)) # 3 layer neural network with hyperbolic tangent activation. def dnn_tanh(features, target):
target = tf.one_hot(target, 2, 1.0, 0.0)
logits = layers.stack(features, layers.fully_connected, [10, 20, 10],
activation_fn=tf.tanh)
prediction, loss = learn.models.logistic_regression(logits, target)
train_op = layers.optimize_loss(loss,
tf.contrib.framework.get_global_step(), optimizer='SGD', learning_rate=0.05)
return tf.argmax(prediction, dimension=1), loss, train_op random.seed(42)
classifier = learn.Estimator(model_fn=dnn_tanh)
classifier.fit(X_train, y_train, batch_size=128, steps=100)
print(accuracy_score(classifier.predict(X_test), y_test))
TensorFlow实战之Scikit Flow系列指导：Part 1 http://www.liuhaihua.cn/archives/186209.htmlscikit Flow 是 TensorFlow 的简化接口，模仿 Scikit 学习，让用户可以在预测分析和数据挖掘中使用。
为什么使用 TensorFlow？
TensorFlow 提供构建各种不同类型机器学习应用的核心
会继续在分布式方向和常规管道机器中进行创新
为什么使用 Scikit Flow？可以平滑的从单向机器学习 Scikit Learn 过渡到更开放的，可以构建不同类型的 ML 模型。用户可以通过 fit/predict 和切换到 TensorFlow APIs。
提供一系列的参考模型，方便与现有的代码集成。
Linear Classifierimport skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: %f" % score)
Linear Regressorimport skflow
from sklearn import datasets, metrics, preprocessing
boston = datasets.load_boston()
X = preprocessing.StandardScaler().fit_transform(boston.data)
regressor = skflow.TensorFlowLinearRegressor()
regressor.fit(X, boston.target)
score = metrics.mean_squared_error(regressor.predict(X), boston.target)
print ("MSE: %f" % score)
Deep Neural Networkimport skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
classifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: %f" % score)
Custom modelimport skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
def my_model(X, y):
"""This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability."""
layers = skflow.ops.dnn(X, [10, 20, 10], keep_prob=0.5)
return skflow.models.logistic_regression(layers, y)
classifier = skflow.TensorFlowEstimator(model_fn=my_model, n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: %f" % score)
可以平滑的从单向机器学习 Scikit Learn 过渡到更开放的，可以构建不同类型的 ML 模型。用户可以通过 fit/predict 和切换到 TensorFlow APIs。
提供一系列的参考模型，方便与现有的代码集成。
import skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: %f" % score)
Linear Regressorimport skflow
from sklearn import datasets, metrics, preprocessing
boston = datasets.load_boston()
X = preprocessing.StandardScaler().fit_transform(boston.data)
regressor = skflow.TensorFlowLinearRegressor()
regressor.fit(X, boston.target)
score = metrics.mean_squared_error(regressor.predict(X), boston.target)
print ("MSE: %f" % score)
Deep Neural Networkimport skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
classifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: %f" % score)
Custom modelimport skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
def my_model(X, y):
"""This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability."""
layers = skflow.ops.dnn(X, [10, 20, 10], keep_prob=0.5)
return skflow.models.logistic_regression(layers, y)
classifier = skflow.TensorFlowEstimator(model_fn=my_model, n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: %f" % score)
import skflow
from sklearn import datasets, metrics, preprocessing
boston = datasets.load_boston()
X = preprocessing.StandardScaler().fit_transform(boston.data)
regressor = skflow.TensorFlowLinearRegressor()
regressor.fit(X, boston.target)
score = metrics.mean_squared_error(regressor.predict(X), boston.target)
print ("MSE: %f" % score)
import skflow
from sklearn import datasets, metricsiris = datasets.load_iris()
classifier = skflow.TensorFlowDNNClassifier(hidden_units=[10, 20, 10], n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: %f" % score)
Custom modelimport skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
def my_model(X, y):
"""This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability."""
layers = skflow.ops.dnn(X, [10, 20, 10], keep_prob=0.5)
return skflow.models.logistic_regression(layers, y)
classifier = skflow.TensorFlowEstimator(model_fn=my_model, n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: %f" % score)
import skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
def my_model(X, y):
"""This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability."""
layers = skflow.ops.dnn(X, [10, 20, 10], keep_prob=0.5)
return skflow.models.logistic_regression(layers, y)
classifier = skflow.TensorFlowEstimator(model_fn=my_model, n_classes=3)
classifier.fit(iris.data, iris.target)
score = metrics.accuracy_score(classifier.predict(iris.data), iris.target)
print("Accuracy: %f" % score)
