1. 雲端計算

2. Tensorflow
python

3. Tensorflow

4. Load the necessary libraries and import data
import math
from IPython import display
from matplotlib import cm
from matplotlib import gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.metrics as metrics
import tensorflow as tf
from tensorflow.python.data import Dataset
tf.logging.set_verbosity(tf.logging.ERROR)
pd.options.display.max_rows = 10
pd.options.display.float_format = '{:.1f}'.format
california_housing_dataframe = pd.read_csv(" sep=",")
california_housing_dataframe = california_housing_dataframe.reindex(
np.random.permutation(california_housing_dataframe.index))
california_housing_dataframe["median_house_value"] /=
print(california_housing_dataframe)

5. Set up the input function
def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):
# Convert pandas data into a dict of np arrays.
features = {key:np.array(value) for key,value in dict(features).items()}
# Construct a dataset, and configure batching/repeating.
ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit
ds = ds.batch(batch_size).repeat(num_epochs)
# Shuffle the data, if specified.
if shuffle:
ds = ds.shuffle(buffer_size=10000)
# Return the next batch of data.
features, labels = ds.make_one_shot_iterator().get_next()
return features, labels
"""Trains a linear regression model of one feature. 參數
Args:
features: pandas DataFrame of features
targets: pandas DataFrame of targets
batch_size: Size of batches to be passed to the model
shuffle: True or False. Whether to shuffle the data.
num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely
Returns:
Tuple of (features, labels) for next data batch
"""
buffer_size :size of the dataset from which shuffle will randomly sample

6. Train the model

7. Synthetic feature: 由多個feature組成
california_housing_dataframe["rooms_per_person"] = (
california_housing_dataframe["total_rooms"] / california_housing_dataframe["population"])
calibration_data = train_model(
learning_rate=0.05,
steps=500,
batch_size=5,
input_feature="rooms_per_person")
用rooms_per_person當作feature

8. Use rooms_per_person as the input_feature to train_model()

9. Identify outliers Scatter plot of predictions vs. targets
plt.figure(figsize=(15, 6))
plt.subplot(1, 2, 1)
plt.scatter(calibration_data["predictions"], calibration_data["targets"])
subplot(m, n, k): 把圖形視窗切成 m x n 格，畫在第k格。
順序由左而右，由上而下。

10. Identify outliers Plot a histogram of rooms_per_person
plt.subplot(1, 2, 2)
_ = california_housing_dataframe["rooms_per_person"].hist()

11. Identify outliers

12. Clip outliers
Setting the outlier values of feature to some reasonable minimum or maximum.

13. 驗收: Clip outliers & histogram
Setting the outlier values of rooms_per_person to some reasonable minimum or maximum.
Plot a histogram of rooms_per_person
Hint: use min()

14. Python3.5 modules

15. modules

16. Packages

17. Packages

18. 驗收 建一個ramdom_List.py從0~100產生10個隨機的數字
可使用: random.sample(range(),)
建一個compare.py使用ramdom_List.py產生的數字找出最大最小值並做 排序
可使用: max()、min()、sorted()、sorted(,reverse=True)