具有所有行排列的Tensorflow1 concat 2D张量

import tensorflow as tf

# 创建两个示例2D张量
tensor1 = tf.constant([[1, 2], [3, 4]])
tensor2 = tf.constant([[5, 6], [7, 8]])

# 打印原始张量
print("Tensor 1:")
print(tensor1)
print("Tensor 2:")
print(tensor2)

# 沿着行（axis=0）连接张量
concat_tensor_axis0 = tf.concat([tensor1, tensor2], axis=0)

# 打印连接后的张量
print("Concatenated Tensor along axis 0:")
print(concat_tensor_axis0)

# 沿着列（axis=1）连接张量
concat_tensor_axis1 = tf.concat([tensor1, tensor2], axis=1)

# 打印连接后的张量
print("Concatenated Tensor along axis 1:")
print(concat_tensor_axis1)

import tensorflow as tf

# 创建两个示例2D张量
tensor1 = tf.constant([[1, 2], [3, 4]])
tensor2 = tf.constant([[5, 6], [7, 8]])

# 打印原始张量
print("Tensor 1:")
print(tensor1)
print("Tensor 2:")
print(tensor2)

# 沿着行（axis=0）连接张量
concat_tensor_axis0 = tf.concat([tensor1, tensor2], axis=0)

# 沿着列（axis=1）连接张量
concat_tensor_axis1 = tf.concat([tensor1, tensor2], axis=1)

# 创建会话并运行
with tf.Session() as sess:
    result_axis0 = sess.run(concat_tensor_axis0)
    result_axis1 = sess.run(concat_tensor_axis1)
    print("Concatenated Tensor along axis 0:")
    print(result_axis0)
    print("Concatenated Tensor along axis 1:")
    print(result_axis1)