tensorflow(一)regression

最新推荐文章于 2025-02-05 08:30:03 发布

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最新推荐文章于 2025-02-05 08:30:03 发布

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分类专栏： tensorFLow

本文链接：https://blog.youkuaiyun.com/fightingdog/article/details/83858910

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本文通过TensorFlow官网的房价预测示例，详细介绍了如何使用Keras进行数据预处理、模型构建、训练及评估。从加载波士顿房价数据集开始，经过数据标准化处理，构建神经网络模型，并使用均方误差作为损失函数进行训练。最后，展示了模型在测试集上的预测效果，包括预测误差的分布。

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tensoflow官网预测房价的例子(https://tensorflow.google.cn/tutorials/keras/basic_regression#conclusion)

from __future__ import absolute_import,division,print_function
import tensorflow as tf
from tensorflow import keras
import numpy as np
import pandas as pd



#1.load data

boston_housing=keras.datasets.boston_housing
(train_data,train_labels),(test_data,test_labels)=boston_housing.load_data()

#2.shuffle the training data
order = np.argsort(np.random.random(train_labels.shape))
train_data=train_data[order]
train_labels=train_labels[order]

print("Trainning set:{}".format(train_data.shape))
print("Testing set:{}".format(test_data.shape))

#3.
column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',
                'TAX', 'PTRATIO', 'B', 'LSTAT']

df=pd.DataFrame(train_data,columns=column_names)
print(df.head())

mean=train_data.mean(axis=0)
std=train_data.std(axis=0)
train_data=(train_data-mean)/std
test_data=(test_data-mean)/std

def build_model():
  model = keras.Sequential([
    keras.layers.Dense(64, activation=tf.nn.relu,
                       input_shape=(train_data.shape[1],)),
    keras.layers.Dense(64, activation=tf.nn.relu),
    keras.layers.Dense(1)
  ])

  optimizer = tf.train.RMSPropOptimizer(0.001)

  model.compile(loss='mse',
                optimizer=optimizer,
                metrics=['mae'])
  return model

model = build_model()
model.summary()

# Display training progress by printing a single dot for each completed epoch
class PrintDot(keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs):
    if epoch % 100 == 0: print('')
    print('.', end='')

EPOCHS=500

history = model.fit(train_data, train_labels, epochs=EPOCHS,
                    validation_split=0.2, verbose=0,
                    callbacks=[PrintDot()])


import matplotlib.pyplot as plt


def plot_history(history):
  plt.figure()
  plt.xlabel('Epoch')
  plt.ylabel('Mean Abs Error [1000$]')
  plt.plot(history.epoch, np.array(history.history['mean_absolute_error']),
           label='Train Loss')
  plt.plot(history.epoch, np.array(history.history['val_mean_absolute_error']),
           label = 'Val loss')
  plt.legend()
  plt.ylim([0, 5])
  plt.show()

plot_history(history)



#
[loss,mae]=model.evaluate(test_data,test_labels,verbose=0)
print("Testing set mean Abs error:${:7.2f}".format(mae*1000))



test_predictions = model.predict(test_data).flatten()

plt.scatter(test_labels, test_predictions)
plt.xlabel('True Values [1000$]')
plt.ylabel('Predictions [1000$]')
plt.axis('equal')
plt.xlim(plt.xlim())
plt.ylim(plt.ylim())
_ = plt.plot([-100, 100], [-100, 100])

plt.show()


error = test_predictions - test_labels
plt.hist(error, bins = 50)
plt.xlabel("Prediction Error [1000$]")
_ = plt.ylabel("Count")

plt.show()