# 获取权限：访问云端硬盘中的所有文件
from os.path import join
from google.colab import drive

ROOT = "/content/drive"
drive.mount(ROOT)

import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.ticker as ticker
import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
import os, time
import pandas as pd
import numpy as np

!gdown --id 1fsKERl26TNTFIY25PhReoCujxwJvfyHn
zhfont = mpl.font_manager.FontProperties(fname='SimHei .ttf')
zhfont2 = mpl.font_manager.FontProperties(fname='New Times Roman .ttf')
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"

# 参数设置
# 参数设置
batch_size = 128
class_num = 10
epochs = 100
learning_rate_original = 0.01

# 数据类型转换
def preprocess(x, y):
    
    x = tf.cast(x, dtype=tf.float32) / 255.
    y = tf.cast(y, dtype=tf.int32)
    y = tf.one_hot(y, depth=class_num)
    return x,y

# 导入mnist数据
(x, y), (x_test, y_test) = datasets.mnist.load_data()

x = np.expand_dims(x, axis=3)
x = np.pad(x, ((0,0), (2,2), (2,2),(0,2)), 'edge')
x_test = np.expand_dims(x_test, axis=3)
x_test = np.pad(x_test, ((0,0), (2,2), (2,2),(0,2)), 'edge')

train_num = int(x.shape[0])
val_num = int(x_test.shape[0])

print(x.shape, y.shape, train_num, val_num)

db = tf.data.Dataset.from_tensor_slices((x,y))
db = db.map(preprocess).repeat().shuffle(10000).batch(batch_size)

db_test = tf.data.Dataset.from_tensor_slices((x_test,y_test))
db_test = db_test.map(preprocess).repeat().batch(batch_size)

# 打印数据形状
db_iter = iter(db)
sample = next(db_iter)
print('batch:', sample[0].shape, sample[1].shape)

db_iter = iter(db_test)
sample = next(db_iter)
print('batch:', sample[0].shape, sample[1].shape)

# Ir
import math
def lr_schedule(epoch):

    learning_rate = learning_rate_original*math.exp(0.01)

    if epoch > epochs*0.8:
        learning_rate = learning_rate_original*math.exp(-0.69)
    elif epoch > epochs*0.5:
        learning_rate = learning_rate_original*math.exp(-0.51)
    elif epoch > epochs*0.3:
        learning_rate = learning_rate_original*math.exp(-0.35)

    return learning_rate

lr_callback = tf.keras.callbacks.LearningRateScheduler(lr_schedule)

rng = [i for i in range(epochs)]
y = [lr_schedule(x) for x in rng]
plt.plot(rng, y)
print("Learning rate schedule: {:.3g} to {:.3g} to {:.3g}".format(y[0], max(y), y[-1]))

# model_01：EfficientNetB1
from tensorflow.keras.applications import EfficientNetB1

feature = EfficientNetB1(include_top=False, weights='imagenet', input_shape=(32,32,3))
model_01 = tf.keras.Sequential([feature,
                tf.keras.layers.GlobalAvgPool2D(),
                tf.keras.layers.Dropout(rate=0.1),
                tf.keras.layers.Dense(512),
                tf.keras.layers.Dropout(rate=0.1),
                tf.keras.layers.Dense(128),
                tf.keras.layers.Dense(class_num)])

model_01.summary()

start_1 =time.perf_counter()
model_01.compile(optimizer=tf.keras.optimizers.SGD(learning_rate = learning_rate_original,momentum=0.9),
              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
              metrics=["accuracy"])

callback = [tf.keras.callbacks.ModelCheckpoint(filepath='/content/save_weights/modle_01/my_model_{epoch}.h5',
                        save_best_only=True,
                        save_weights_only=True,
                        monitor='val_accuracy'),
                        lr_callback]
history = model_01.fit(x = db,
           steps_per_epoch = train_num // batch_size,
           epochs = epochs,
           validation_data = db_test,
           validation_steps = val_num // batch_size,
           callbacks = callback)
end_1 = time.perf_counter()

history_dict = history.history
train_loss = history_dict["loss"]
train_accuracy = history_dict["accuracy"]
val_loss = history_dict["val_loss"]
val_accuracy = history_dict["val_accuracy"]

print("Accuracy:", max(val_accuracy))
print('Tranning time:\t %s'%(end_1 - start_1))

for i in range(0,epochs):
    val_accuracy[i] = val_accuracy[i] * 100
    train_accuracy[i] = train_accuracy[i] * 100

# figure 1
plt.figure(dpi = 100)
plt.gca().yaxis.set_major_formatter(ticker.FormatStrFormatter('%.2f'))#设置坐标轴精度
plt.plot(range(epochs), train_loss, label='Train', linestyle='--',linewidth = '0.8')
plt.plot(range(epochs), val_loss, color = 'red', label='Test',linewidth = '0.8')
plt.xlabel(u"训练迭代次数",fontproperties=zhfont,fontsize=14)
plt.ylabel(u"损失值",fontproperties=zhfont,fontsize=14)

plt.xticks(np.arange(0, epochs+1, 5),fontproperties = 'Times New Roman', size = 12)
plt.legend(fontsize=12)
plt.show()

# figure 2
plt.figure(dpi = 100)
plt.plot(range(epochs), train_accuracy, label='Train', linestyle='--',linewidth = '0.8')
plt.plot(range(epochs), val_accuracy,color = 'red', label='Test',linewidth = '0.8')
plt.xlabel(u"训练迭代次数",fontproperties=zhfont,fontsize=14)
plt.ylabel(u"准确率/%",fontproperties=zhfont,fontsize=14)

#设置label位置
plt.legend(loc='lower right',fontsize=12) 

plt.xticks(np.arange(0, epochs+1, 5),fontproperties = 'Times New Roman', size = 12)
plt.yticks(np.arange(0, 101,10),fontproperties = 'Times New Roman', size = 12)

plt.show()

训练总结

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