ImageDataGneratorを使用する際のエラーについて

pythonのエラーについての質問です。

画像を読み込んで100×100に分割し、その画像と教師ラベルを使用してCNNで学習をしたいです。
その際に、kerasのImageDataGeneratorクラスを用いて画像を学習直前で変換したいのですが、下記エラーが出てしまいうまくいきません。
また、環境は次のとおりです
windows10（64bit）
anaconda 　4.13.0
python　3.7.13
TensorFlow　2.3
Spyder 5.1.5を利用

エラー

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

ソースコード

# -*- coding: utf-8 -*-
# coding: utf-8
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam


def kiritori(img):
    img_array=np.array(img,dtype='int16')         #(50行,83列,3次元)の3次元配列
    a=np.split(img_array,img.height/100)
    img_data=np.split(a[0],img.width/100,axis=1)#stackする土台づくり
    for i in range(len(a)-1):
        img_data=np.vstack([img_data,np.split(a[i+1],img.width/100,axis=1)])

    return img_data


x_train=kiritori(Image.open('3Gatu_Data/DJI_0730.JPG'))#(#(1200,100,100,3)


train_label=np.loadtxt(fname='label/3Gatu_730_100.csv',delimiter=',',dtype='int16')#(30, 40)
train_label=np.reshape(train_label,(train_label.size))                    #(1200)

rati_train_data          =x_train[np.where(train_label==0)]
hamagou_train_data       =x_train[np.where(train_label==1)]
kouboumugi_train_data    =x_train[np.where(train_label==2)]



x_train=kiritori(Image.open('3Gatu_Data/DJI_0777.JPG'))#(1200,100,100,3)

train_label=np.loadtxt(fname='label/3Gatu_777_100.csv',delimiter=',',dtype='int16')#(30,40)
train_label=np.reshape(train_label,(train_label.size))                    #(1200)

rati_train_data =np.vstack([rati_train_data,x_train[np.where(train_label==0)]])
hamagou_train_data =np.vstack([hamagou_train_data,x_train[np.where(train_label==1)]])
kouboumugi_train_data =np.vstack([kouboumugi_train_data,x_train[np.where(train_label==2)]])


x_train=kiritori(Image.open('3Gatu_Data/DJI_0908.JPG'))#(1200,100,100,3)

train_label=np.loadtxt(fname='label/3Gatu_908_100.csv',delimiter=',',dtype='int16')#(30,40)
train_label=np.reshape(train_label,(train_label.size))                    #(1200)

rati_train_data =np.vstack([rati_train_data,x_train[np.where(train_label==0)]])
hamagou_train_data =np.vstack([hamagou_train_data,x_train[np.where(train_label==1)]])
kouboumugi_train_data =np.vstack([kouboumugi_train_data,x_train[np.where(train_label==2)]])


x_train=rati_train_data
x_train=np.vstack([x_train,hamagou_train_data])
x_train=np.vstack([x_train,kouboumugi_train_data])


                             

val_test=kiritori(Image.open('3Gatu_Data/DJI_0957.JPG'))

test_label=np.loadtxt(fname='label/3Gatu_957_100.csv',delimiter=',',dtype='int16')#(30,40)
test_label=np.reshape(test_label,(test_label.size))                    #(1200)

rati_test_data         =val_test[np.where(test_label==0)]
hamagou_test_data      =val_test[np.where(test_label==1)]
kouboumugi_test_data   =val_test[np.where(test_label==2)]


val_test=rati_test_data
val_test=np.vstack([val_test,hamagou_test_data])
val_test=np.vstack([val_test,kouboumugi_test_data])


test_data=Image.open('3Gatu_Data/DJI_0957.JPG')
copy_back=test_data.copy()
x_test=kiritori(test_data)

y_train=np.full((rati_train_data.shape[0],1),0)
y_train=np.vstack([y_train,np.full((hamagou_train_data.shape[0],1),1)])
y_train=np.vstack([y_train,np.full((kouboumugi_train_data.shape[0],1),2)])

y_test=np.full((rati_test_data.shape[0],1),0)
y_test=np.vstack([y_test,np.full((hamagou_test_data.shape[0],1),1)])
y_test=np.vstack([y_test,np.full((kouboumugi_test_data.shape[0],1),2)])


x_train = x_train/255.
x_test  = x_test/255.

from tensorflow.keras.utils import to_categorical
y_train = to_categorical(y_train,3)
y_test = to_categorical(y_test,3)


x_train_datagen = ImageDataGenerator(
                                  rotation_range=90,
                                  vertical_flip=True,
                                  brightness_range = [0.7, 1.117],
                                  horizontal_flip=True,
                                  )


x_train_generator = x_train_datagen.flow(x_train,
                                         subset="training",
                                         batch_size=10
                                         )

val_datagen = ImageDataGenerator(rescale=1.0 / 255)

val_test_generator = val_datagen.flow(val_test,
                              subset="validation",
                               batch_size=10
                               )
                                     

from tensorflow.python.keras.models import Sequential
model = Sequential()

from tensorflow.python.keras.layers import Conv2D
model.add(                                                            #畳み込み
    Conv2D(
        filters=6,         #出力
        input_shape=(100,100,3),
        kernel_size=(3,3),  #フィルタサイズ
        strides=(1,1),
        padding='valid',
        activation='relu'
    )
)

from tensorflow.python.keras.layers import MaxPooling2D
from tensorflow.python.keras.layers import Dropout

model.add(MaxPooling2D(pool_size=(2,2)))                    #マックスプーリング
model.add(Dropout(0.25))                                        #ドロップアウト


from tensorflow.python.keras.layers import Flatten
model.add(Flatten())                                               #2次元配列に

from tensorflow.python.keras.layers import Dense

model.add(Dense(units=3456,activation='relu'))     #全結合
from tensorflow.python.keras.layers import Dropout

model.add(Dropout(0.5))
model.add(Dense(units=3,activation='softmax',name="f3"))                         #全結合


model.summary()

model.compile(
    optimizer='adam',                   #自動で学習率が設定される
    loss='categorical_crossentropy',    #多分類のときにしていできる交差エントロピー
    metrics=['accuracy']
)


history_model = model.fit_generator(
    x_train_generator,
    y_train, 
    epochs=2,
    steps_per_epoch = len(x_train)/20,
    validation_data=(val_test_generator, y_test),
    validation_steps = len(val_test)/20,
    shuffle=True,
)

#精度のアウトプット
probs=model.predict(x_test)         #学習したパラメータで精度を出力
probs_max=np.reshape(np.argmax(probs,axis=1),(int(test_data.height/100),int(test_data.width/100)))#(30,40)2次元配列
np.savetxt('Output_file/cnn_output_3Gatu_100_500.txt',probs_max,fmt="%d",delimiter=",")

#モデルの保存
model_json_str = model.to_json()
open('Output_file/cnn_model_3Gatu_100_500.json', 'w').write(model_json_str)
model.save_weights('Output_file/mnist_mlp_3Gatu_100_500_weights.h5');

score = model.evaluate(val_test, y_test, verbose=0)
print('Test loss :', score[0])
print('Test accuracy :', score[1])

loss     = history_model.history['loss']    #訓練データの誤差
val_loss = history_model.history['val_loss']#テストデータ誤差
accuracy      = history_model.history['accuracy']    #訓練データの誤差
val_accuracy  = history_model.history['val_accuracy']#テストデータ誤差

np.savetxt('Output_file/cnn_loss_3Gatu_100.csv',loss,fmt="%.6f",delimiter=",")
np.savetxt('Output_file/cnn_val_loss_3Gatu_100.csv',val_loss,fmt="%.6f",delimiter=",")
np.savetxt('Output_file/cnn_acc_3Gatu_100.csv',accuracy,fmt="%.6f",delimiter=",")
np.savetxt('Output_file/cnn_val_acc_3Gatu_100.csv',val_accuracy,fmt="%.6f",delimiter=",")

すみませんが，どなたかご教授頂けると幸いです。
よろしくお願いします。