學習網址：https://www.tensorflow.org/get_started/get_started

This guide gets you started programming in TensorFlow. Before using this guide, install TensorFlow. To get the most out of this guide, you should know the following:

• How to program in Python.
• At least a little bit about arrays.
• Ideally, something about machine learning. However, if you know little or nothing about machine learning, then this is still the first guide you should read.

• Python程式設計.
• 陣列知識（線性代數相關知識）.
• 機器學習相關知識

Tensors

The central unit of data in TensorFlow is the tensor. A tensor consists of a set of primitive values shaped into an array of any number of dimensions. A tensor's rank is its number of dimensions. Here are some examples of tensors:

總體上看，這是一些常量的使用方法，應當沒什麼可以解釋的，繼續下面

這裡面來了一個placeholders，應該是屬於變數輸入的介面了（暫時如此理解），繼續往下看

這裡面就有了機器學習的影子了，裡面包含了輸入變數X，引數w和b，輸出y，損失函式loss，基本元素基本上都包含了。那麼y也是placeholder，所以placeholder應當包含了輸入輸出介面了，具體怎麼翻譯呢？有地方翻譯為佔位符，也挺好的。

這是如何訓練一個模型，這裡是最小化損失函式，總共訓練了1000次，最後得到了我們要的引數w和b。

最終的損失函式數值為e-11.效果很好，主要是該模型就是一個非常完美的線性模型，毫無疑問了。

該訓練網路為上圖，rank之上我就看不懂了，還有就是每一個模組似乎都跟gradient模組相連，有點萌。但是計算梯度的時候，這些數值都是需要的，應該是這個原因了。

contrib屬性學習，是屬於高層的抽象，看一下如何應用

import tensorflow as tf
# NumPy is often used to load, manipulate and preprocess data.
import numpy as np

# Declare list of features. We only have one real-valued feature. There are many
# other types of columns that are more complicated and useful.
features = [tf.contrib.layers.real_valued_column("x", dimension=1)]

# An estimator is the front end to invoke training (fitting) and evaluation
# (inference). There are many predefined types like linear regression,
# logistic regression, linear classification, logistic classification, and
# many neural network classifiers and regressors. The following code
# provides an estimator that does linear regression.
estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)

# TensorFlow provides many helper methods to read and set up data sets.
# Here we use `numpy_input_fn`. We have to tell the function how many batches
# of data (num_epochs) we want and how big each batch should be.
x = np.array([1., 2., 3., 4.])
y = np.array([0., -1., -2., -3.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x":x}, y, batch_size=4,
                                              num_epochs=1000)

# We can invoke 1000 training steps by invoking the `fit` method and passing the
# training data set.
estimator.fit(input_fn=input_fn, steps=1000)

# Here we evaluate how well our model did. In a real example, we would want
# to use a separate validation and testing data set to avoid overfitting.
estimator.evaluate(input_fn=input_fn)

features = [tf.contrib.layers.real_valued_column("x", dimension=1)]

輸入為一維x

estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)

線性模型

x = np.array([1., 2., 3., 4.])
y = np.array([0., -1., -2., -3.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x":x}, y, batch_size=4,
                                              num_epochs=1000)

batch_size，機器學習中非常有用的概念，當樣本數量非常大，有一些冗餘的時候，採用minibatch訓練，效果會非常好。當然這裡就4個樣本，就是full——batch

num_epochs是每個batch的大小，為什麼是1000呢，有點暈

estimator.fit(input_fn=input_fn, steps=1000) 

這裡面有兩個input_fn，當然了，第一個是預設變數，看一下其原始碼：

這樣是不是就一目瞭然了，哈哈，1000次訓練

estimator.evaluate(input_fn=input_fn)

具體各個引數什麼意思，慢慢學習了，先有個總體的印象，學習，就是要一遍一遍不斷的看，不斷的學習。

這個例子到此先暫時放一下了。

import numpy as np
import tensorflow as tf
# Declare list of features, we only have one real-valued featuredef model(features, labels, mode):# Build a linear model and predict values
  W = tf.get_variable("W",[1], dtype=tf.float64)
  b = tf.get_variable("b",[1], dtype=tf.float64)
  y = W*features['x']+ b
  # Loss sub-graph
  loss = tf.reduce_sum(tf.square(y - labels))# Training sub-graph
  global_step = tf.train.get_global_step()
  optimizer = tf.train.GradientDescentOptimizer(0.01)
  train = tf.group(optimizer.minimize(loss),
                   tf.assign_add(global_step,1))# ModelFnOps connects subgraphs we built to the# appropriate functionality.return tf.contrib.learn.ModelFnOps(
      mode=mode, predictions=y,
      loss=loss,
      train_op=train)

estimator = tf.contrib.learn.Estimator(model_fn=model)# define our data set
x = np.array([1.,2.,3.,4.])
y = np.array([0.,-1.,-2.,-3.])
input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x}, y,4, num_epochs=1000)# train
estimator.fit(input_fn=input_fn, steps=1000)# evaluate our modelprint(estimator.evaluate(input_fn=input_fn, steps=10))

到此，第一個感覺已經建立了。