TensorFlow2 維度變換

目錄tensorflow2教程完整教程目錄（更有python、go、pytorch、tensorflow、爬蟲、人工智慧教學等著你）：

import tensorflow as tf

a = tf.random.normal([4, 28, 28, 3])

a.shape, a.ndim

(tensorshape([4, 28, 28, 3]), 4)

tf.reshape(a, [4, 784, 3]).shape  # 給出一張某個通道的資料，丟失行、寬的資訊

tensorshape([4, 784, 3])

tf.reshape(a, [4, -1, 3]).shape  # 4*(-1)*3 = 4*28*28*3

tensorshape([4, 784, 3])

tf.reshape(a, [4, 784*3]).shape  # 給出一張的所有資料，丟失行、寬和通道的資訊

tensorshape([4, 2352])

tf.reshape(a, [4, -1]).shape

tensorshape([4, 2352])

tf.reshape(tf.reshape(a, [4, -1]), [4, 28, 28, 3]).shape

tensorshape([4, 28, 28, 3])

tf.reshape(tf.reshape(a, [4, -1]), [4, 14, 56, 3]).shape

tensorshape([4, 14, 56, 3])

tf.reshape(tf.reshape(a, [4, -1]), [4, 1, 784, 3]).shape

tensorshape([4, 1, 784, 3])

second reshape:

a = tf.random.normal((4, 3, 2, 1))

a.shape

tensorshape([4, 3, 2, 1])

tf.transpose(a).shape

tensorshape([1, 2, 3, 4])

tf.transpose(a, perm=[0, 1, 3, 2]).shape  # 按照索引替換維度

tensorshape([4, 3, 1, 2])

a = tf.random.normal([4, 28, 28, 3])  # b,h,w,c

a.shape

tensorshape([4, 28, 28, 3])

tf.transpose(a, [0, 2, 1, 3]).shape  # b,2,h,c

tensorshape([4, 28, 28, 3])

tf.transpose(a, [0, 3, 2, 1]).shape  # b,c,w,h

tensorshape([4, 3, 28, 28])

tf.transpose(a, [0, 3, 1, 2]).shape  # b,c,h,w

tensorshape([4, 3, 28, 28])

a = tf.random.normal([4, 25, 8])

a.shape

tensorshape([4, 25, 8])

tf.expand_dims(a, axis=0).shape  # 索引0前

tensorshape([1, 4, 25, 8])

tf.expand_dims(a, axis=3).shape  # 索引3前

tensorshape([4, 25, 8, 1])

tf.expand_dims(a,axis=-1).shape  # 索引-1後

tensorshape([4, 25, 8, 1])

tf.expand_dims(a,axis=-4).shape  # 索引-4後，即左邊空白處

tensorshape([1, 4, 25, 8])

tf.squeeze(tf.zeros([1,2,1,1,3])).shape

tensorshape([2, 3])

a = tf.zeros([1,2,1,3])

a.shape

tensorshape([1, 2, 1, 3])

tf.squeeze(a,axis=0).shape

tensorshape([2, 1, 3])

tf.squeeze(a,axis=2).shape

tensorshape([1, 2, 3])

tf.squeeze(a,axis=-2).shape

tensorshape([1, 2, 3])

tf.squeeze(a,axis=-4).shape

tensorshape([2, 1, 3])

tensorflow2 0 維度變換

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