Tensor
3 # a rank 0 tensor; a scalar with shape []
[1., 2., 3.] # a rank 1 tensor; 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]
Rank: 类似矩阵中的秩，一般来说，看开头的中括号的数目决定其个数
Shape:对张量进行描述，形式为[x,y,z,...]。其中括号内的个数与rank个数相同。eg:以上述最后一个为例，[2,1,3]中2代表逗号“ , ”前后一共有2个rank=2，每个都为1×3的矩阵。
Importing TensorFlow
import tensorflow as tf
Constant
One type of node is a constant. it takes no inputs, and it outputs a value it stores internally. We can create two floating point Tensors node1 and node2 as follows:
node1 = tf.constant(3.0, dtype=tf.float32)
node2 = tf.constant(4.0) # also tf.float32 implicitly
print(node1)
print(node2)
print statement produces:
Tensor("Const:0", shape=(), dtype=float32)
Tensor("Const_1:0", shape=(), dtype=float32)
they are nodes that, until when evaluated, would produce 3.0 and 4.0, respectively. The following code creates a Session object and then invokes its run method to run enough of the computational graph to evaluate node1 and node2. By running the computational graph in a session as follows:
sess = tf.Session() # session is important!
print(sess.run([node1, node2]))
we see the expected values of 3.0 and 4.0:
[3.0, 4.0]
Add
Add our two constant nodes and produce a new graph as follows:
node3 = tf.add(node1, node2) #also node3=node1+node2
print("node3:", node3)
print("sess.run(node3):", sess.run(node3))
print statements produce:
node3: Tensor("Add:0", shape=(), dtype=float32)
sess.run(node3): 7.0
TensorBoard displays a picture of the computation graph like below:
[站外图片上传中...(image-d30106-1514471593029)]
Placeholder
parameterized to accept external inputs, known as placeholders. A placeholder is a promise to provide a value later.
a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
adder_node = a + b # + likes 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]}))
sess的run方法中第二个参数为字典
7.5
[ 3. 7.]
In TensorBoard, the graph looks like this:
[站外图片上传中...(image-d53209-1514471593029)]
more complex by adding another operation:
add_and_triple = adder_node * 3.
print(sess.run(add_and_triple, {a: 3, b: 4.5}))
resulting:
22.5
[站外图片上传中...(image-e26c5a-1514471593029)]
Variable
In machine learning we can take arbitrary inputs, such as the one above. Variables allow us to add trainable parameters to a graph. They are constructed with a type and initial value:
W = tf.Variable([.3], dtype=tf.float32)
b = tf.Variable([-.3], dtype=tf.float32)
x = tf.placeholder(tf.float32)
linear_model = W*x + b
注意:Variable 需要初始化，方法：
init = tf.global_variables_initializer() # 全局变量？
sess.run(init)
Since x is a placeholder, we can evaluate linear_model for several values of x simultaneously as follows:
print(sess.run(linear_model, {x: [1, 2, 3, 4]}))
to pruduce:
[ 0. 0.30000001 0.60000002 0.90000004]
Loss function
To evaluate the model on training data, we need a y placeholder to provide the desired values, and we need to write a loss function.
y = tf.placeholder(tf.float32)
squared_deltas = tf.square(linear_model - y) # (y`-y)^2
loss = tf.reduce_sum(squared_deltas) # 求和
print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}))
得到：
23.66 # 23.66>>0 显然W,b不符合要求
假设我们找到了W=-1.0，b=1.0：
fixW = tf.assign(W, [-1.])
fixb = tf.assign(b, [1.])
sess.run([fixW, fixb])
print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}))
结果 loss=0，得到需要的W，b
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