TensorFlow2.0学习笔记(一）
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2020-12-21
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TensorFlow2.0学习笔记学习笔记(一）一）
TensorFlow2.0学习笔记学习笔记(一）一）数据读取和展示模型构建数据归一化回调函数回归模型分类模型深度神经网络批归一化激活函数
droupoutWide&Deep模型子类API实现wide&deep多输入、多输出超参数搜索sklearn超参数搜索
数据读取和展示数据读取和展示
首先我们导入相关的头文件和库
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
print(module.__name__, module.__version__)
输出我的环境配置：
2.0.1
sys.version_info(major=3, minor=7, micro=6, releaselevel='final', serial=0)
matplotlib 3.2.1
numpy 1.18.2
pandas 1.0.3
sklearn 0.22.2.post1
tensorflow 2.0.1
tensorflow_core.keras 2.2.4-tf
下载数据，加载数据，并对训练数据进行划分，前5000为验证集，之后的为训练集。
fashion_mnist = keras.datasets.fashion_mnist
(x_train_all, y_train_all), (x_test, y_test) = fashion_mnist.load_data()
x_valid, x_train = x_train_all[:5000], x_train_all[5000:] y_valid, y_train = y_train_all[:5000], y_train_all[5000:]
print(x_valid.shape, y_valid.shape)
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)
(5000, 28, 28) (5000,)
(55000, 28, 28) (55000,)
(10000, 28, 28) (10000,)
展示一张照片：
def show_single_image(img_arr):
plt.imshow(img_arr, cmap="binary")
plt.show()
show_single_image(x_train[0])
输出结果如下：
按照3*5的格子展示图片，并标注每张图片的标签
def show_imgs(n_rows, n_cols, x_data, y_data, class_names):
assert len(x_data) == len(y_data)
assert n_rows * n_cols < len(x_data)
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