import warnings
warnings.simplefilter(action='ignore')
import tensorflow as tf
c = tf.constant(2, name='c')
c
<tf.Tensor 'c:0' shape=() dtype=int32>
var = tf.Variable(c + 5, name='var')
var
<tf.Variable 'var:0' shape=() dtype=int32_ref>
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
print('c:', sess.run(c))
print('var:', sess.run(var))
c: 2 var: 7
print('c:', c.eval(session=sess))
print('var:', var.eval(session=sess))
c: 2 var: 7
sess.close()
c = tf.constant(2, name='c')
var = tf.Variable(c + 5, name='var')
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
print('c:', sess.run(c))
print('var:', sess.run(var))
c: 2 var: 7
width = tf.placeholder('int32')
height = tf.placeholder('int32')
area = tf.multiply(width, height)
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
print('area:', sess.run(area, feed_dict={width: 6, height: 8}))
area: 48
var = tf.Variable([0.4, 0.2, 0.4])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
result = sess.run(var)
print(result)
[0.4 0.2 0.4]
print(result.shape)
(3,)
var = tf.Variable([[0.4, 0.2, 0.4]])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
result = sess.run(var)
print(result)
[[0.4 0.2 0.4]]
print(result.shape)
(1, 3)
W = tf.Variable([
[-0.5, -0.2],
[-0.3, 0.4],
[-0.5, 0.2]
])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
result = sess.run(W)
print(result)
[[-0.5 -0.2] [-0.3 0.4] [-0.5 0.2]]
print(result.shape)
(3, 2)
X = tf.Variable([[1.0, 1.0, 1.0]])
W = tf.Variable([
[-0.5, -0.2],
[-0.3, 0.4],
[-0.5, 0.2]
])
product = tf.matmul(X, W)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print("product:", sess.run(product))
product: [[-1.3 0.4]]
b = tf.Variable([[0.1, 0.2]])
product = tf.Variable([[-1.3, 0.4]])
result = product + b
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print('result:', sess.run(result))
result: [[-1.1999999 0.6 ]]
X = tf.Variable([[1.0, 1.0, 1.0]])
W = tf.Variable([
[-0.5, -0.2],
[-0.3, 0.4],
[-0.5, 0.2]
])
b = tf.Variable([[0.1, 0.2]])
result = tf.matmul(X, W) + b
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print('result:', sess.run(result))
result: [[-1.1999999 0.6 ]]