if FLAGS.embed_status is False:
init_embedding = None
else:
print('get initialized embedding...')
init_embedding = embed.embedding
#print("\nParameters:")
#for attr, value in sorted(FLAGS.__flags.items(),reverse=True):
# print("{}={}".format(attr.upper(), value))
#print("")
print('load student data...')
#load train data
print('loading train data...')
train_char_s, train_label_s = load_data.load_datas(config.TRAIN_DATA_UNI)
#train_pred = get_pred_p.load_datas(config.TRAIN_DATA_UNI_PRED)
# Build vocabulary
print("Build vocabulary...")
max_sentene_length = config.MAX_LEN
#train_x_s, train_label_s, train_pred = build_bi_vocab.build_bi_vocabulary(train_char_s, train_label_s, train_pred, max_sentene_length, BI_GRAM)
#load dev data
print('loading dev data...')
dev_char_s, dev_label_s = load_data.load_datas(config.DEV_DATA_UNI)
dev_pred = get_pred_p.load_datas(config.DEV_DATA_UNI_PRED)
#max_dev_sentene_length_s = max([len(x) for x in dev_char_s])
dev_x_s, dev_label_s, dev_pred = build_vocabulary.build_vocabulary(
dev_char_s, dev_label_s, dev_pred, max_sentene_length)
'''
fashion-mnist 数据集 进行基本分类
'''
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
import load_data
fashion_mnist = keras.datasets.fashion_mnist
# (train_data, train_labels), (test_data, test_labels) = fashion_mnist.load_data()
(train_images, train_labels), (test_images,
test_labels) = load_data.load_datas()
train_images = train_images / 255.0
test_images = test_images / 255.0
class_names = [
'T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt',
'Sneaker', 'Bag', 'Ankle boot'
]
# print(train_images.shape)
# plt.figure()
# plt.imshow(train_images[0])
# plt.colorbar()
# plt.grid(False)
# plt.figure(figsize=(10,10))
# for i in range(25):
# plt.subplot(5,5,i+1)
# plt.xticks([])
# plt.yticks([])