from tensorflow.python.keras.preprocessing.image import load_img,img_to_array

from tensorflow.python.keras.models import Sequential,Model

from tensorflow.python.keras.layers import Dense,Flatten,Input

import tensorflow as tf

from tensorflow.python.keras.losses import sparse_categorical_crossentropy

from tensorflow.python import keras

import os

import numpy as np

class SingleNN(object):

    #建立神经网络模型

    model = keras.Sequential([

        keras.layers.Flatten(input_shape=(28,28)),

        keras.layers.Dense(128,activation=tf.nn.relu),

        keras.layers.Dense(10,activation=tf.nn.softmax)

    ])

    def __init__(self):

        (self.x_train,self.y_train),(self.x_test,self.y_test) = keras.datasets.fashion_mnist.load_data()

        #归一化

        self.x_train = self.x_train/255.0

        self.x_test = self.x_test/255.0

    def singlenn_compile(self):

        '''

        编译模型优化器、损失、准确率

        :return:

        '''

        SingleNN.model.compile(

            optimizer=keras.optimizers.SGD(lr=0.01),

            loss=keras.losses.sparse_categorical_crossentropy,

            metrics=['accuracy']

        )

    def singlenn_fit(self):

        """

        进行fit训练

        :return:

        """

        SingleNN.model.fit(self.x_train,self.y_train,epochs=5)

    def single_evalute(self):

        '''

        模型评估

        :return:

        '''

        test_loss,test_acc = SingleNN.model.evaluate(self.x_test,self.y_test)

        print(test_loss,test_acc)

    def single_predict(self):

        '''

        预测结果

        :return:

        '''

        # if os.path.exists("./ckpt/checkpoink"):

        #     SingleNN.model.load_weights("./ckpt/SingleNN")

        if os.path.exists("./ckpt/SingleNN.h5"):

            SingleNN.model.load_weights("./ckpt/SingleNN.h5")

        predictions = SingleNN.model.predict(self.x_test)

        return predictions

if __name__ == '__main__':

    snn = SingleNN()

    # snn.singlenn_compile()

    # snn.singlenn_fit()

    # snn.single_evalute()

    # # SingleNN.model.save_weights("./ckpt/SingleNN")

    # SingleNN.model.save_weights("./ckpt/SingleNN.h5")

    predictions = snn.single_predict()

    print(predictions)

    result = np.argmax(predictions,axis=1)

    print(result)