import sysimport codecsimport tensorflow as tf# 1.参数设置。# 读取checkpoint的路径。9000表示是训练程序在第9000步保存的checkpoint。CHECKPOINT_PATH = "F:\\temp\\attention_ckpt-9000"# 模型参数。必须与训练时的模型参数保持一致。HIDDEN_SIZE = 1024
import sysimport codecsimport tensorflow as tf# 1.参数设置。# 读取checkpoint的路径。9000表示是训练程序在第9000步保存的checkpoint。CHECKPOINT_PATH = "F:\\temp\\seq2seq_ckpt-9000"# 模型参数。必须与训练时的模型参数保持一致。HIDDEN_SIZE = 1024
import numpy as npimport tensorflow as tfimport matplotlib.pyplot as plt# 定义RNN的参数。HIDDEN_SIZE = 30 # LSTM中隐藏节点的个数。NUM_LAYERS = 2 # LSTM的层数。TIMESTEPS = 10
import numpy as npimport tensorflow as tfimport matplotlib.pyplot as plt#随机调整图片的色彩,定义两种顺序。def distort_color(image, color_ordering=0): if color_ordering == 0: image = tf.image.random_brightness(image, max_delta=32./255.) image