TensorFlow官方文档入门笔记[一]

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]

node1 = tf.constant(3.0, dtype=tf.float32)
node2 = tf.constant(4.0) # also tf.float32 implicitly
print(node1, node2)

输出：

Tensor("Const:0", shape=(), dtype=float32) Tensor("Const_1:0", shape=(), dtype=float32)

sess = tf.Session()
print(sess.run([node1, node2]))

输出：

[3.0, 4.0]

node3 = tf.add(node1, node2)
print("node3:", node3)
print("sess.run(node3):", sess.run(node3))

输出：

node3: Tensor("Add:0", shape=(), dtype=float32)
sess.run(node3): 7.0

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]}))

输出

7.5
[ 3.  7.]

add_and_triple = adder_node * 3.

print(sess.run(add_and_triple, {a: 3, b: 4.5}))

输出

22.5

W = tf.Variable([.3], dtype=tf.float32)

b = tf.Variable([-.3], dtype=tf.float32)

x = tf.placeholder(tf.float32)

linear_model = W * x + b

init = tf.global_variables_initializer()

sess.run(init)

print(sess.run(linear_model, {x: [1, 2, 3, 4]}))

输出

[ 0.          0.30000001  0.60000002  0.90000004]

y = tf.placeholder(tf.float32)

squared_deltas = tf.square(linear_model - y)

loss = tf.reduce_sum(squared_deltas)

print(sess.run(loss, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}))

输出

23.66

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]}))

   输出

0.0

optimizer = tf.train.GradientDescentOptimizer(0.01)

train = optimizer.minimize(loss)

sess.run(init) # reset values to incorrect defaults.

for i in range(1000):

    sess.run(train, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]})

  

print(sess.run([W, b]))

   输出

[array([-0.9999969], dtype=float32), array([ 0.99999082],

dtype=float32)]

import tensorflow as tf

  

# Model parameters

W = tf.Variable([.3], dtype=tf.float32)

b = tf.Variable([-.3], dtype=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))

输出

W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11