四、先来入门五个例子

• 例一：Hello World。

import tensorflow as tf
hw = tf.constant("Hello World")
with tf.Session() as sess:
 print(sess.run(hw))

• 例二：两个矩阵相乘。

# Build a dataflow graph.
c = tf.constant([[1.0, 2.0], [3.0, 4.0]])
d = tf.constant([[1.0, 1.0], [0.0, 1.0]])
e = tf.matmul(c, d)

# Construct a `Session` to execute the graph.
with tf.Session() as sess:
  # Execute the graph and store the value that `e` represents in `result`.
  result = sess.run(e)

print(result)

• 例三：使用Feeding在执行时传入参数

import tensorflow as tf

# Build a dataflow graph.
c = tf.constant([[1.0, 2.0], [3.0, 4.0]])
d = tf.constant([[1.0, 1.0], [0.0, 1.0]])
e = tf.matmul(c, d)

# Construct a `Session` to execute the graph.
sess = tf.Session()

# Execute the graph and store the value that `e` represents in `result`.
result = sess.run(e,feed_dict={c:[[0.0, 0.0], [3.0, 4.0]]})
print(result)
sess.close()

TensorFlow的一大特色时其图中的节点可以是带状态的。

• 例四：带状态的图

import tensorflow as tf

# Build a dataflow graph.
count = tf.Variable([0],trainable=False);
init_op = tf.global_variables_initializer()
update_count = count.assign_add(tf.constant([2]))

# Construct a `Session` to execute the graph.
sess = tf.Session()
sess.run(init_op)

for step in range(10):
    result = sess.run(update_count)
    print("step %d: count = %g" % (step,result))

sess.close()

• 例五：梯度计算

import tensorflow as tf

# Build a dataflow graph.
filename_queue = tf.train.string_input_producer(['1.txt'],num_epochs=1)
reader = tf.TextLineReader()
key,value = reader.read(filename_queue)
num = tf.decode_csv(value,record_defaults=[[0]])
x = tf.Variable([0])
loss = x * num
grads = tf.gradients([loss],x)
grad_x = grads[0]

def train_fn(sess):
  train_fn.counter += 1
  result = sess.run(grad_x)
  print("step %d: grad = %g" % (train_fn.counter,result))

train_fn.counter = 0

sv = tf.train.Supervisor()
tf.train.basic_train_loop(sv,train_fn)