assert _backend in {'theano', 'tensorflow', 'cntk'}

python animal_cnn_aug.py

ターミナルで実行すると下記のようなエラーが生じます。

#ターミナルでの実行結果

assert _backend in {'theano', 'tensorflow', 'cntk'}

のエラー原因はなんでしょうか？

#animal_cnn_aug.py

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.utils import np_utils 

import numpy as np
import keras

classes = ["monkey", "boar", "crow"]
num_classes = len(classes)
image_size = 50

def main():
    X_train, X_test, Y_train, Y_test=np.load("./animal_aug.npy",allow_pickle=True)
    #X_train, X_test, y_train, y_test = np.load("./animal.npy",allow_pickle=True)
    #np.load("./animal.npy")
    X_train = X_train.astype("float")/256
    X_test = X_test.astype("float")/256
    Y_train = np_utils.to_categorical(Y_train,num_classes)
    Y_test = np_utils.to_categorical(Y_test,num_classes)

    model = model_train(X_train, Y_train)
    model_eval(model, X_test, Y_test)

def model_train(X,Y):
    model = Sequential()
    model.add(Conv2D(32,(3,3),padding='same',input_shape=X.shape[1:]))
    model.add(Activation('relu'))
    model.add(Conv2D(32,(3,3)))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(64,(3,3), padding='same'))
    model.add(Activation('relu'))
    model.add(Conv2D(64,(3,3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(3))
    model.add(Activation('softmax'))

    opt = keras.optimizers.RMSprop(lr=0.0001, decay=1e-6)
    #opt = tensorflow.keras.optimizers.RMSprop(lr=0.0001, decay=1e-6)
    X_train, X_test, Y_train, Y_test = np.load("./animal_aug.npy",allow_pickle=True)

    model.compile(loss='categorical_crossentropy',
                    optimizer=opt,metrics=['accuracy'])

    model.fit(X,Y, batch_size=32, epochs=100)


#モデルの保存
    model.save('./animal_cnn_aug.h5')

    return model

def model_eval(model, X, Y):
    scores = model.evaluate(X, Y, verbose=1)
    print('Test Loss:', scores[0])
    print('Test Accuracy:', scores[1])

if __name__ == "__main__":
    main()
``