学习维度转换
tf.shape(input,name = None)
a = tf.constant([i for i in range(20)],shape =[2,2,5])
with tf.Session() as sess:
print (sess.run(tf.shape(a)))
结果:[2 2 5]
tf.size(input,name = None)
a = tf.constant([i for i in range(20)],shape =[2,2,5])
with tf.Session() as sess:
print (sess.run(tf.size(a)))
结果:20
tf.rank(input, name=None)
a = tf.constant([i for i in range(20)],shape =[2,2,5])
with tf.Session() as sess:
print (sess.run(tf.rank(a)))
结果 : 3
tf.reshape(tensor, shape, name=None)
a = tf.constant([i for i in range(20)],shape =[2,2,5])
with tf.Session() as sess:
print (sess.run(tf.reshape(a,shape = [5,2,2])))
原始的数据
结果:
没理解,等理解了再来更新。
tf.squeeze(input, squeeze_dims=None, name=None)
没理解,等理解了再来更新。
tf.expand_dims(input, dim, name=None)