#先导入一些常用库，后续用到再增加
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
import sklearn
import os
import sys
#看一下版本，确认是2.0
print(tf.__version__)
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#使用keras自带的模块导入数据，并且切分训练集、验证集、测试集，对训练数据进行标准化处理
fashion_mnist=keras.datasets.fashion_mnist
(x_train_all,y_train_all),(x_test,y_test)=fashion_mnist.load_data()
print(x_train_all.shape)
print(y_train_all.shape)
print(x_test.shape)
print(y_test.shape)
#切分训练集和验证集
x_train,x_valid=x_train_all[5000:],x_train_all[:5000]
y_train,y_valid=y_train_all[5000:],y_train_all[:5000]
print(x_train.shape)
print(y_train.shape)
print(x_valid.shape)
print(y_valid.shape)
#标准化
from sklearn.preprocessing import StandardScaler
scaler=StandardScaler()
x_train_scaled=scaler.fit_transform(x_train.astype(np.float32).reshape(-1,1)).reshape(-1,28,28)
x_valid_scaled=scaler.fit_transform(x_valid.astype(np.float32).reshape(-1,1)).reshape(-1,28,28)
x_test_scaled=scaler.fit_transform(x_test.astype(np.float32).reshape(-1,1)).reshape(-1,28,28)
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#可视化一下图片以及对应的标签
#展示多张图片
def show_imgs(n_rows,n_cols,x_data,y_data,class_names):
    assert len(x_data)==len(y_data)#判断输入数据的信息是否对应一致
    assert n_rows*n_cols<=len(x_data)#保证不会出现数据量不够
    plt.figure(figsize=(n_cols*2,n_rows*1.6))
    for row in range(n_rows):
        for col in range(n_cols):
            index=n_cols*row+col   #得到当前展示图片的下标
            plt.subplot(n_rows,n_cols,index+1)
            plt.imshow(x_data[index],cmap="binary",interpolation="nearest")
            plt.axis("off")
            plt.title(class_names[y_data[index]])
    plt.show()
class_names=['t-shirt', 'trouser', 'pullover', 'dress', 'coat', 'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']
show_imgs(5,5,x_train,y_train,class_names)
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#搭建网络模型
model=keras.models.Sequential()
model.add(keras.layers.Flatten(input_shape=[28,28]))
model.add(keras.layers.Dense(300,activation="relu"))
model.add(keras.layers.Dense(100,activation="relu"))
model.add(keras.layers.Dense(10,activation="softmax"))
model.compile(loss="sparse_categorical_crossentropy",optimizer="adam",metrics=["acc"])
model.summary()
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#训练，并且保存最好的模型、训练的记录以及使用早停防止过拟合
import datetime
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
logdir = os.path.join('logs', current_time)
output_model=os.path.join(logdir,"fashionmnist_model.h5")
callbacks=[
    keras.callbacks.TensorBoard(log_dir=logdir),
    keras.callbacks.ModelCheckpoint(output_model,save_best_only=True),
    keras.callbacks.EarlyStopping(patience=5,min_delta=1e-3)
          ]
history=model.fit(x_train_scaled,y_train,epochs=30,validation_data=(x_valid_scaled,y_valid),callbacks=callbacks)
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def plot_learning_curves(history):
    pd.DataFrame(history.history).plot(figsize=(8,5))
    plt.grid()
    plt.gca().set_ylim(0,1)
    plt.show()
plot_learning_curves(history)
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#最后在测试集上的准确率
loss,acc=model.evaluate(x_test_scaled,y_test,verbose=0)
print("在测试集上的损失为:",loss)
print("在测试集上的准确率为:",acc)
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#得到测试集上的预测标签，可视化和真实标签的区别
y_pred=model.predict(x_test_scaled)
predict = np.argmax(y_pred,axis=1) 
show_imgs(3,5,x_test,predict,class_names)
show_imgs(3,5,x_test,y_test,class_names)
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model=keras.models.Sequential()
model.add(...)
model.add(...)
...
model.compile(...)
model.fit(...)
#当然也可以写成
model=keras.models.Sequential([
  ...
  ...
  ...
])
#这两者差别不大
#还有函数式的写法
inputs=...
hidden1=...(inputs)
....
#子类的写法
class ...:
  ...
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