Tensorflow2.0学习（12）：用tensorflow1.0完成分类

Tf1-dense-network

• 导包

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__)

1.15.0
sys.version_info(major=3, minor=7, micro=6, releaselevel='final', serial=0)
matplotlib 3.1.3
numpy 1.18.1
pandas 1.0.1
sklearn 0.22.1
tensorflow 1.15.0
tensorflow.python.keras.api._v1.keras 2.2.4-tf

• 下载、读取数据

# 读取keras中的进阶版mnist数据集
fashion_mnist = keras.datasets.fashion_mnist
# 加载数据集，切分为训练集和测试集
(x_train_all, y_train_all),(x_test, y_test) = fashion_mnist.load_data()
# 从训练集中将后五千张作为验证集，前五千张作为训练集
# [:5000]默认从头开始，从头开始取5000个
# [5000:]从第5000开始(不包含5000)，结束位置默认为最后
x_valid, x_train = x_train_all[:5000],x_train_all[5000:]
y_valid, y_train = y_train_all[:5000],y_train_all[5000:]
# 打印这些数据集的大小
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,)

• 归一化数据

# 归一化处理：x = (x - u)/std :减去均值除以方差，是均值为0，方差为1 -> 正态分布

from sklearn.preprocessing import StandardScaler
# 初始化一个StandarScaler对象
scaler = StandardScaler()
# fit_transform要求为二维矩阵，因此需要先转换
# 要进行除法，因此先转化为浮点型
# x_train是三维矩阵[None,28,28]，先将其转换为二维矩阵[None,784],再将其转回三维矩阵
# reshape（-1， 1）转化为一列(-1代表不确定几行)
# fit: 求得训练集的均值、方差、最大值、最小值等训练集固有的属性
# transform: 在fit的基础上，进行标准化，降维，归一化等操作

x_train_scaled = scaler.fit_transform(
    x_train.astype(np.float32).reshape(-1, 1)).reshape(-1, 28 * 28)
x_valid_scaled = scaler.transform(
    x_valid.astype(np.float32).reshape(-1, 1)).reshape(-1, 28 * 28)
x_test_scaled = scaler.transform(
    x_test.astype(np.float32).reshape(-1, 1)).reshape(-1, 28 * 28)

• 构建计算图

# 定义全连接层有两层，每次有100个神经元
hidden_units = [100, 100]
# 类别数
class_num = 10

# 建立两个placeholder用于存放数据和标签
# placeholder是占位符，数据通过占位符输入到网络
# placeholder()函数是在神经网络构建graph的时候在模型中的占位，此时并没有把要输入的数据传入模型，它只会分配必要的内存。等建立session，在会话中，运行模型的时候通过feed_dict()函数向占位符喂入数据。
x = tf.placeholder(tf.float32, [None, 28*28])
y = tf.placeholder(tf.int64, [None])

# 定义层次
# 输入
input_for_next_layer = x
# 隐藏层
for hidden_unit in hidden_units:
    input_for_next_layer = tf.layers.dense(input_for_next_layer, 
                                           hidden_unit,
                                          activation=tf.nn.relu)
# 输出层
logits = tf.layers.dense(input_for_next_layer,class_num)

# 定义损失函数：tf.losses.sparse_softmax_cross_entropy
# 1.最后一个隐层的输出*最后一组权重=输出神经节点的输出值->softmax->变成了概率
# 2.对labels做one-hot编码
# 3.计算交叉熵
loss = tf.losses.sparse_softmax_cross_entropy(labels = y,
                                             logits = logits)

# 获得精确度
# 预测值，就是logits中最大的那个值对应的索引
prediction = tf.argmax(logits, 1)
correct_prediction = tf.equal(prediction, y)
# tf.reduce_mean用来计算张量tensor沿着指定轴的平均值
# tf.cast执行tensorflow中张量数据类型的转换
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))

# 运行一遍train_op,网络就被训练一次
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)

WARNING:tensorflow:From <ipython-input-4-40ee1aae8fd0>:19: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.
Instructions for updating:
Use keras.layers.Dense instead.
WARNING:tensorflow:From E:\Anaconda\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\layers\core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.__call__` method instead.
WARNING:tensorflow:From E:\Anaconda\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\ops\losses\losses_impl.py:121: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where

• 运行会话以训练模型

print(x)
print(logits)

Tensor("Placeholder:0", shape=(?, 784), dtype=float32)
Tensor("dense_2/BiasAdd:0", shape=(?, 10), dtype=float32)

# 构建完图之后，运行图
# session

init = tf.global_variables_initializer()
batch_size = 20
epochs = 10
train_steps_per_epoch = x_train.shape[0] // batch_size
valid_steps = x_valid.shape[0] // batch_size

# 每一个epoch计算一下精度的均值
def eval_with_sess(sess, x, y, accuracy, images, labels, batch_size):
    eval_steps = images.shape[0] // batch_size
    eavl_accuracies = []
    for step in range(eval_steps):
        batch_data = images[step * batch_size:(step+1) * batch_size]
        batch_label = labels[step * batch_size:(step+1) * batch_size]
        accuracy_val = sess.run(accuracy, 
                                feed_dict ={
                                    x:batch_data,
                                    y:batch_label
                                })
        eavl_accuracies.append(accuracy_val)
    return np.mean(eavl_accuracies)

# 打开一个session
with tf.Session() as sess:
    # 初始化
    sess.run(init)
    for epoch in range(epochs):
        for step in range(train_steps_per_epoch):
            batch_data = x_train_scaled[
                step * batch_size:(step+1) * batch_size]
            batch_label = y_train[
                step * batch_size:(step+1) * batch_size]
            loss_val, accuracy_val, _ =sess.run([loss, accuracy, train_op],
                feed_dict = {
                    x:batch_data,
                    y:batch_label
            })
            print('\r[Train] epoch: %d, step:%d, loss: %3.5f, accuracy: %2.2f'
                 % (epoch, step, loss_val, accuracy_val), end="")
        valid_accuracy = eval_with_sess(sess, x, y, accuracy,
                                    x_valid_scaled, y_valid, batch_size)
        print("\t[Valid] acc: %2.2f" % (valid_accuracy))

[Train] epoch: 0, step:2749, loss: 0.25137, accuracy: 0.90	[Valid] acc: 0.86
[Train] epoch: 1, step:2749, loss: 0.24022, accuracy: 0.90	[Valid] acc: 0.87
[Train] epoch: 2, step:2749, loss: 0.20952, accuracy: 0.90	[Valid] acc: 0.88
[Train] epoch: 3, step:2749, loss: 0.16674, accuracy: 0.90	[Valid] acc: 0.88
[Train] epoch: 4, step:2680, loss: 0.65273, accuracy: 0.85[Train] epoch: 4, step:2749, loss: 0.13731, accuracy: 0.90	[Valid] acc: 0.88
[Train] epoch: 5, step:2749, loss: 0.22307, accuracy: 0.95	[Valid] acc: 0.88
[Train] epoch: 6, step:2749, loss: 0.16866, accuracy: 0.95	[Valid] acc: 0.88
[Train] epoch: 7, step:2749, loss: 0.15522, accuracy: 0.95	[Valid] acc: 0.88
[Train] epoch: 8, step:2749, loss: 0.10591, accuracy: 0.95	[Valid] acc: 0.88
[Train] epoch: 9, step:2749, loss: 0.14000, accuracy: 0.90	[Valid] acc: 0.88