在keras 中获取张量 tensor 的维度大小实例

>>> from keras import backend as K
>>> tf_session = K.get_session()
>>> val = np.array([[1, 2], [3, 4]])
>>> kvar = K.variable(value=val)
>>> input = keras.backend.placeholder(shape=(2, 4, 5))
>>> K.shape(kvar)
<tf.Tensor 'Shape_8:0' shape=(2,) dtype=int32>
>>> K.shape(input)
<tf.Tensor 'Shape_9:0' shape=(3,) dtype=int32>
__To get integer shape (Instead, you can use K.int_shape(x))__
 
>>> K.shape(kvar).eval(session=tf_session)
array([2, 2], dtype=int32)
>>> K.shape(input).eval(session=tf_session)
array([2, 4, 5], dtype=int32)

>>> from keras import backend as K
>>> input = K.placeholder(shape=(2, 4, 5))
>>> K.int_shape(input)
(2, 4, 5)
>>> val = np.array([[1, 2], [3, 4]])
>>> kvar = K.variable(value=val)
>>> K.int_shape(kvar)
(2, 2)

import tensorflow as tf 
import numpy as np 

x=tf.constant([[1,2,3],[4,5,6]])
y=[[1,2,3],[4,5,6]] 
z=np.arange(24).reshape([2,3,4])

sess=tf.Session() 
# tf.shape() 
x_shape=tf.shape(x)          # x_shape 是一个tensor 
y_shape=tf.shape(y)          # <tf.Tensor 'Shape_2:0' shape=(2,) dtype=int32> 
z_shape=tf.shape(z)          # <tf.Tensor 'Shape_5:0' shape=(3,) dtype=int32> 
print(sess.run(x_shape))       # 结果:[2 3]
print(sess.run(y_shape))       # 结果:[2 3]
print(sess.run(z_shape) )       # 结果:[2 3 4]

x_shape=x.get_shape() 
print(x_shape)    # 返回的是TensorShape([Dimension(2), Dimension(3)]),不能使用 sess.run() 因为返回的不是tensor 或string,而是元组                            (2, 3)
x_shape=x.get_shape().as_list() 
print(x_shape) # 可以使用 as_list()得到具体的尺寸，x_shape=[2 3] 这是重点 返回列表方便参加其他代码的运算
# y_shape=y.get_shape() 
print(x_shape)# AttributeError: 'list' object has no attribute 'get_shape'
# z_shape=z.get_shape() 
print(x_shape)# AttributeError: 'numpy.ndarray' object has no attribute 'get_shape' 或者a.shape.as_list()