从零基础入门Tensorflow2.0 ----五、23TF1.0计算图构建,模型训练

every blog every motto:

0. 前言

计算图的构建，模型训练

1. 代码部分

1. 导入模块

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__)

2. 读取数据

fashion_mnist = keras.datasets.fashion_mnist
# print(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:]
# 打印格式
print(x_valid.shape,y_valid.shape)
print(x_train.shape,y_train.shape)
print(x_test.shape,y_test.shape)

print(np.max(x_train),np.min(x_train))

3. 数据归一化

# 数据归一化
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
# x_train:[None,28,28] -> [None,784]
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)

print(np.max(x_train_scaled),np.min(x_train_scaled))

4. 计算图

hidden_units = [100,100]
class_num = 10

x = tf.placeholder(tf.float32,[None,28*28])
y = tf.placeholder(tf.int64,[None])
# 隐藏层
input_for_next_layer = x
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)
# last_hidden_output * w(logits) -> softmax -> pro
# 1. logit -> softmax -> prob
# 2. labels -> ont_hot
# 3. calculate cross entropy
loss = tf.losses.sparse_softmax_cross_entropy(labels=y,logits=logits)

# get accuracy
prediction = tf.argmax(logits,1)
correct_prediction = tf.equal(prediction,y)
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float64))

# 训练
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)

print(x)
print(logits)

5. 开始训练

# 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

def eval_with_sess(sess,x,y,accuracy,images,labels,batch_size):
    eval_steps = images.shape[0] // batch_size
    eval_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
        })
        eval_accuracies.append(accuracy_val)
    return np.mean(eval_accuracies)

with tf.Session() as sess:
    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]
#             print(batch_label.shape)
#             print(batch_data.shape)
            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))