Getting Started With TensorFlow

Tensors

TensorFlow里的数据形式称为tensor, tensor的rank即为数据的维度。

3 # a rank 0 tensor; this is a scalar with shape []
[1. ,2., 3.] # a rank 1 tensor; this is a vector with shape [3]
[[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3]
[[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3]

TensorFlow Core tutorial

Importing TensorFlow

import tensorflow as tf

The Computational Graph

TensorFlow的过程可以分为两个部分：

构建Computational Graph;
运行Computational Graph。

一个Computational Graph由许多节点(node)组成。例如：

node1 = tf.constant(3.0, tf.float32)
node2 = tf.constant(4.0) # also tf.float32 implicitly
print(node1, node2)
# Output: Tensor("Const:0", shape=(), dtype=float32) Tensor("Const_1:0", shape=(), dtype=float32)

上面就是构建的过程，下面是运行的过程, 运行前需要创建一个Session对象，tensorflow所有的运行都是通过其中的run函数来实现:

sess = tf.Session()
print(sess.run([node1, node2]))
# Output: [3.0, 4.0]
# 这里看出直接print node1输出的是node1的类型，如果print他的值，就需要经过sess.run函数

通过sess.run来实现一个加法的操作:

node3 = tf.add(node1, node2)
print("node3: ", node3)
print("sess.run(node3): ",sess.run(node3))
# Output
# node3:  Tensor("Add_2:0", shape=(), dtype=float32)
# sess.run(node3):  7.0

Computational Graph如下图所示，可以通过TensorBoard来可视化Graph: ~getting_started_add

上面的例子我们

用tf.constant来创建一个常量, 创建后不可被修改;
那如果我们想创建一个变量，先不赋值，再run的过程在赋值传参，可以使用tf.placeholder,
那如果想创建一个变量，先赋值，然后在run的过程中可以重新赋值，可以使用tf.Variable,重新赋值使用tf.assign.

注意: 上面所说tf.Variable可以赋值，但是需要显示调用tf.global_variables_initializer()才会将值赋给变量，否则直接print会报错，具体怎么操作，见下面代码。

示例:

# ------ tf.placeholder ------
a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
adder_node = a + b  # + provides a shortcut for tf.add(a, b)

print(sess.run(adder_node, {a: 3, b:4.5}))
print(sess.run(adder_node, {a: [1,3], b: [2, 4]}))
add_and_triple = adder_node * 3.
print(sess.run(add_and_triple, {a: 3, b:4.5}))

# Output:
# 7.5
# [ 3. 7.]
# 22.5


# ------ tf.Variable ------
W = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
x = tf.placeholder(tf.float32)
linear_model = W * x + b

# print sess.run([W, b])  # error will occur if you uncomment this line
init = tf.global_variables_initializer()
sess.run(init)
print sess.run([W, b])
print(sess.run(linear_model, {x:[1,2,3,4]}))

# Output
# [array([ 0.30000001], dtype=float32), array([-0.30000001], dtype=float32)]
# [ 0.          0.30000001  0.60000002  0.90000004]


# ------ tf.assign ------
tf.assign(W, [-1.])
tf.assign(b, [1.])
print sess.run([W, b])

# Output
# [array([-1.], dtype=float32), array([ 1.], dtype=float32)]

小结：上面讲了几种赋值方式:tf.constant, tf.Variable, tf.placeholder, tf.assign。然后讲了tensorflow的运行机制，先构建好computational graph,然后放进sess.run即可出结果。

tf.train API

一个训练的过程，示例如下:

import numpy as np
import tensorflow as tf

# Model parameters
W = tf.Variable([.3], tf.float32)
b = tf.Variable([-.3], tf.float32)
# Model input and output
x = tf.placeholder(tf.float32)
linear_model = W * x + b
y = tf.placeholder(tf.float32)
# loss
loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
# training data
x_train = [1,2,3,4]
y_train = [0,-1,-2,-3]
# training loop
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(1000):
  sess.run(train, {x:x_train, y:y_train})

# evaluate training accuracy
curr_W, curr_b, curr_loss  = sess.run([W, b, loss], {x:x_train, y:y_train})
print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))

tf.contrib.learn API

tf.contrib.learn是high-level的tensorflow库，包含很多机器学习算法，可以定义很多模型。

例如调用自带的现行回归模型:

import tensorflow as tf
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)

# result: {'global_step': 1000, 'loss': 1.9650059e-11}

调用自己定义的模型

import numpy as np
import tensorflow as tf
# Declare list of features, we only have one real-valued feature
def 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 model
print(estimator.evaluate(input_fn=input_fn, steps=10))

# result: {'loss': 5.9819476e-11, 'global_step': 1000}

其中一些函数还没弄明白怎么调用，先看完整个官方文档再回来纠缠细节。

Getting started with Tensorflow