获取array和tensor的shape
list、array、tensor之间的转换
list转array：np.array(list)
numpy数组转tensor：t = tf.convert_to_tensor(array, tf.float32, name='t')或者t = tf.cast(array, tf.float32)
tensor转numpy数组：array = sess.run(tensor) 或者 array = tensor.eval()
如何获取shape
1、获取numpy数组形状使用：a_array.shape[0]
2、获取tensor形状使用：b_tensor.shape[0]或b_tensor.get_shape().as_list()
示例
import numpy as np
import tensorflow as tf
# numpy数组类型
print("==== numpy.ndarray ====")
a_array = np.array([[1,2,3],[4,5,6]])
print(type(a_array)) # <class 'numpy.ndarray'>
row,column = a_array.shape[0],a_array.shape[1]
print("The rows and columns of a_array is %d, %d" %(row,column))
# tensor 类型
print("==== tensorflow.python.framework.ops.Tensor ====")
b_tensor = tf.constant([[1,2,3],[4,5,6]])
print(type(b_tensor)) # <class 'tensorflow.python.framework.ops.Tensor'>
row,column = b_tensor.shape[0],b_tensor.shape[1]
print("The rows and columns of b_tensor is %d, %d" %(row,column))
row,column = b_tensor.get_shape().as_list()
print("The rows and columns of b_tensor is %d, %d" %(row,column))
