tensorflow18《TensorFlow实战Google深度学习框架》笔记-08-03 SKlearn封装例子 code

# 《TensorFlow实战Google深度学习框架》08 循环神经网络
# win10 Tensorflow1.0.1 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:ts08.04.py # SKlearn封装例子

from sklearn import model_selection
from sklearn import datasets
from sklearn import metrics
import tensorflow as tf
import numpy as np
from tensorflow.contrib.learn.python.learn.estimators.estimator import SKCompat
learn = tf.contrib.learn

# 1. 自定义softmax回归模型
def my_model(features, target):
    target = tf.one_hot(target, 3, 1, 0)

# 计算预测值及损失函数。
    logits = tf.contrib.layers.fully_connected(features, 3, tf.nn.softmax)
    loss = tf.losses.softmax_cross_entropy(target, logits)

# 创建优化步骤。
    train_op = tf.contrib.layers.optimize_loss(
        loss,
        tf.contrib.framework.get_global_step(),
        optimizer='Adam',
        learning_rate=0.01)
return tf.arg_max(logits, 1), loss, train_op

# 2. 读取数据并将数据转化成TensorFlow要求的float32格式
iris = datasets.load_iris()
x_train, x_test, y_train, y_test = model_selection.train_test_split(
    iris.data, iris.target, test_size=0.2, random_state=0)

x_train, x_test = map(np.float32, [x_train, x_test])

# 3. 封装和训练模型，输出准确率
classifier = SKCompat(learn.Estimator(model_fn=my_model, model_dir="Models/model_1"))
classifier.fit(x_train, y_train, steps=800)

y_predicted = [i for i in classifier.predict(x_test)]
score = metrics.accuracy_score(y_test, y_predicted)
print('Accuracy: %.2f%%' % (score * 100))
'''
Accuracy: 100.00%
'''