glhfgg1024

The following two pieces of code are not the same. The second one is correct.

image_node = tf.placeholder(tf.float32, shape=[None, DEPTH, HEIGHT, WIDTH, 2])
image_node = tf.map_fn(lambda frame: frame - tf.reduce_mean(frame, axis=[0,1,2], keep_dims=True), image_node)


image_node = tf.placeholder(tf.float32, shape=[None, DEPTH, HEIGHT, WIDTH, 2])
image_node_new = tf.map_fn(lambda frame: frame - tf.reduce_mean(frame, axis=[0,1,2], keep_dims=True), image_node)

glhfgg1024

import numpy as np
import tensorflow as flow
from tensorflow.python.saved_model import loader

# first, read the pretrained weights into a dictionary
variables = {}
g1 = tf.Graph()
with g1.as_default():
    restore_from = 'pretrained_model/1513006564'
    with tf.Session() as sess:

glhfgg1024

from inspect import getmembers, isfunction
mylib = tf.load_op_library('mylib.so')
functions_list = [o for o in getmembers(mylib) if isfunction(o[1])]

glhfgg1024

def vgg16_fc_convolution(in_put, out_channel, layer_name, use_relu=True):
    with tf.variable_scope(layer_name):
        input_shape = in_put.get_shape()
        assert len(input_shape) == 4
        height, width, in_channel = input_shape[1:]
        print(height, width, in_channel)
        weights = tf.get_variable(name="weights", shape=[height*width*in_channel, out_channel])
        biases = tf.get_variable(name="biases", shape=[out_channel])
        reshape_weights = tf.reshape(weights,
                                     shape=[tf.to_int32(height), tf.to_int32(width),

glhfgg1024

import tensorflow as tf
import numpy as np

sess = tf.Session()
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init_op)

def compute_mean_op():
    count = 0
    image_sum = tf.zeros([32,32,32,3], dtype=tf.float64)