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]  

# a.get_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

x_shape=x.get_shape()

x_shape=x.get_shape().as_list()  # 可以使用 as_list()得到具体的尺寸，x_shape=[2 3]

y_shape=y.get_shape()  # AttributeError: 'list' object has no attribute 'get_shape'

z_shape=z.get_shape()  # AttributeError: 'numpy.ndarray' object has no attribute 'get_shape'

或者a.shape.as_list()

def conv2d_transpose(x, input_filters, output_filters, kernel, strides):

    with tf.variable_scope('conv_transpose'):

        shape = [kernel, kernel, output_filters, input_filters]

        weight = tf.Variable(tf.truncated_normal(shape, stddev=0.1), name='weight')

        batch_size = tf.shape(x)[0]

        height = tf.shape(x)[1] * strides

        width = tf.shape(x)[2] * strides

        output_shape = tf.stack([batch_size, height, width, output_filters])

return tf.nn.conv2d_transpose(x, weight, output_shape, strides=[1, strides, strides, 1], name='conv_transpose')