python Fine Tuneingコードについて

Question

下記のエラーが出て困っています。
ソースの改善のご指摘を頂けますでしょうか。

参考のURLは下記になります。
https://torusblog.org/toru-hyperastunningdence/

エラー内容
info(val_acc, val_loss,hist)
TypeError: info() missing 1 required positional argument: 'hist'

import keras
import matplotlib.pyplot as plt
from keras import models, optimizers
from keras.datasets import mnist
from keras.layers import Dense, Dropout
from keras.models import Sequential
from keras.optimizers import SGD
 
 
def get_data() :
 
        (x_train, y_train), (x_test, y_test) = mnist.load_data()
 
        x_train = x_train.reshape(60000, 784)
        x_test = x_test.reshape(10000, 784)
 
        x_train = x_train.astype('float32')
        x_test = x_test.astype('float32')
 
        x_train /= 255
        x_test /= 255
 
        print(x_train.shape[0], 'train samples')
        print(x_test.shape[0], 'test samples')
 
        y_train = keras.utils.to_categorical(y_train, 10)
        y_test = keras.utils.to_categorical(y_test, 10)
 
        return x_train, y_train, x_test, y_test
 
def model(x_train, y_train, x_test, y_test) :
 
        model = Sequential()
        model.add(Dense(256, activation = 'relu', input_shape = (784,)))
        model.add(Dropout(0.2))
        model.add(Dense(128, activation = 'relu'))
        model.add(Dropout(0.2))
        model.add(Dense(10, activation = 'softmax'))
        model.summary()
 
        model.compile(loss = 'categorical_crossentropy', optimizer = \
                                SGD(lr = 0.05, clipnorm = 1., nesterov = True), 
                                metrics = ['accuracy'])
        hist = model.fit(x_train, y_train, batch_size = 128, epochs = 20, 
                                verbose = 1, validation_data = (x_test, y_test))
        val_loss, val_acc = model.evaluate(x_test, y_test, verbose = 1)
 
        return val_loss, val_acc, hist
 
def info(val_acc, val_loss, start_time, hist) :
 
        print(" ")
        print("===============================")
        print("Accuracy:{:.2f}".format(val_acc * 100), "%")
        print('loss:{:.2f}'.format(val_loss * 100), "%")
 
        #Accuracy Graph
        plt.rc('font', family = 'serif')
        fig = plt.figure(figsize = (13, 6))
        plt.subplot(1, 2, 1)
        plt.xlim([0, 20])
        plt.plot(hist.history['accuracy'], label = 'Train Acc', color = 'red')
        plt.plot(hist.history['val_accuracy'], label = 'Test Acc', color = 'm')
        plt.title('Model Accuracy')
        plt.xlabel('Epochs')
        plt.ylabel('accuracy')
        plt.legend(bbox_to_anchor = (0.7, 0.5), loc = 'center left', borderaxespad = 0,\
                                 fontsize = 8)
 
        #Loss Graph
        plt.rc('font', family = 'serif')
        plt.subplot(1, 2, 2)
        plt.xlim([0, 20])
        plt.plot(hist.history['loss'], label = 'Train Loss', color = 'blue')
        plt.plot(hist.history['val_loss'], label = 'Test Loss', color = 'c')
        plt.title('Model Loss')
        plt.xlabel('Epochs')
        plt.ylabel('loss')
        plt.legend(bbox_to_anchor = (0.7, 0.5), loc = 'center left', borderaxespad = 0, fontsize = 8)
        #plt.savefig ('mnist_graph.png')
        plt.show()
 
#===Learning===
 
x_train, y_train, x_test, y_test = get_data()
val_loss, val_acc, hist = model(x_train, y_train, x_test, y_test)
info(val_acc, val_loss,hist)
 
#==============

Answer 1

info()の定義が

def info(val_acc, val_loss, start_time, hist) :

となってるけど、「start_time」はinfo()内では使われてないので、そこを

def info(val_acc, val_loss, hist) :

と変えるか、あるいは、info()を使う方(コードの最後)を

info(val_acc, val_loss, 0, hist)

とするか、どちらか