IndexError: tuple index out of rangeとでる

前提・実現したいこと

ＣＮＮを用いて、筆者識別を行いたい。
下記のソースコード実行時に、エラーが発生した。
実際に認識を行い、特徴抽出を行いたい。

発生している問題・エラーメッセージ

runfile('C:/Users/sugitalab/Desktop/S315401/char/cnn.py', wdir='C:/Users/sugitalab/Desktop/S315401/char')
Traceback (most recent call last):

  File "<ipython-input-2-098afa90c49c>", line 1, in <module>
    runfile('C:/Users/sugitalab/Desktop/S315401/char/cnn.py', wdir='C:/Users/sugitalab/Desktop/S315401/char')

  File "C:\Users\sugitalab\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 827, in runfile
    execfile(filename, namespace)

  File "C:\Users\sugitalab\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "C:/Users/sugitalab/Desktop/S315401/char/cnn.py", line 162, in <module>
    test(file_path="./test_char_su/*", day_name=day_name)

  File "C:/Users/sugitalab/Desktop/S315401/char/cnn.py", line 133, in test
    cnn(X, y, 0, file_path, day_name=day_name)

  File "C:/Users/sugitalab/Desktop/S315401/char/cnn.py", line 75, in cnn
    height, width = X.shape[1], X.shape[2]

IndexError: tuple index out of range

該当のソースコード

from keras.models import Sequential
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPool2D
from keras.optimizers import Adam

from keras.layers.core import Dense, Activation, Dropout, Flatten
from keras.layers.advanced_activations import LeakyReLU
from keras.utils import plot_model
from keras.callbacks import TensorBoard
from keras.utils import np_utils

import numpy as np
from PIL import Image
import cv2
import glob
from sklearn.model_selection import train_test_split
import tensorflow as tf
 
from keras import backend as K

from keras.preprocessing import image

import matplotlib.pyplot as plt
import os
K.clear_session()
#tf.reset_default_graph()
import math
from keras.models import load_model
#from keras import get_activations
from keras.models import Model
from keras.callbacks import EarlyStopping, TensorBoard, ModelCheckpoint

from sklearn.preprocessing import MinMaxScaler
import sys
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report



np.set_printoptions(threshold=np.inf)
fig, (axL, axR) = plt.subplots(ncols=2, figsize=(10,4))

def binary(file_path, save_path): # return img
    file_list = glob.glob(file_path)
    for i in range(len(file_list)):
        name=os.path.basename(file_list[i])
        save_name=name[0]
        gray_img = cv2.imread(file_path[:-1]+name, cv2.IMREAD_GRAYSCALE)
        blur_img = cv2.GaussianBlur(gray_img, (5,5), 0)
        ret, bin_img = cv2.threshold(blur_img, 0, 255, cv2.THRESH_OTSU)
        cv2.imwrite(save_path+'{}_{}.jpg'.format(save_name,i), bin_img)
    



def make_list(file_path, flag=None):
    X = []
    y = []
    file_list = glob.glob(file_path)
    for i in range(len(file_list)):
        file = file_list[i]
        file_name = os.path.basename(file)[0]
        img = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
        ret, tmp_img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
        X.append(tmp_img)

        if file_name == 'a':
            y.append(0)
        elif file_name == 'b':
            y.append(1)
    X = np.array(X)
    y = np.array(y)
    return X, y

def cnn(X, y, flag=None, file_path=None, day_name=None): # return value : <training:model>, <test:None>
    height, width = X.shape[1], X.shape[2]
    print(height, width)


    print("X.shape:", X.shape)
    print("X shape:", X[0].shape)
    X = X.astype('float')/255.0

    

    if flag==0: 
        print("X: ", X.shape)
        X_test = X.reshape(X.shape[0], height, width, 1)
        print("X: ", X_test.shape)
        y_test = np_utils.to_categorical(y, 2)
        print("X_test.shape :",X_test.shape)

        model = load_model("./model/model_{}.h5".format(day_name))
        model.load_weights("./model_weight/weight_ep1000_es_{}.h5".format(day_name))
        model.summary()
        score = model.evaluate(X_test, y_test)
        print("test loss: ", score[0], "acc: {:.4f}".format(score[1]))
        predict = model.predict(X_test)
        print(predict.shape)
        for i in range(predict.shape[0]):
            print("{} : <{}, {}>  <{:4f}%, {:4f}%>".format(i, y_test[i][0], y_test[i][1], predict[i][0]*100, predict[i][1]*100))
        ### metrics
        cnt_o, cnt_t = 0, 0
        for i in range(len(y_test)):
            if y_test[i][0]==1:
                cnt_o+=1
            elif y_test[i][1]==1:
                cnt_t+=1
        print("cnt_o: {}, cnt_t: {}".format(cnt_o, cnt_t))
        target_names = ['class0', 'class1']
        print(confusion_matrix(y_test.argmax(axis=1), predict.argmax(axis=1)))
        print(accuracy_score(y_test.argmax(axis=1), predict.argmax(axis=1)))
        print(classification_report(y_test.argmax(axis=1), predict.argmax(axis=1)))
        print(y_test.argmax(axis=1))
        print(predict.argmax(axis=1))


        print('*'*20)
        print(predict.argmax(axis=1))
        ###
        predict = list(predict)

        with open("./acc.csv", "w") as f:
            for i in range(40):
                f.write("{},{}\n".format(predict[i][0], predict[i][1]))

        return None




def test(file_path=None, day_name=None):
    X, y = make_list(file_path)
    cnn(X, y, 0, file_path, day_name=day_name)



        
 # loss
def plot_history_loss(fit):
    # Plot the loss in the history
    axL.plot(fit.history['loss'],label="トレーニングデータ")
    axL.plot(fit.history['val_loss'],label="検証用データ")
    axL.set_title('エポックごとの損失')
    axL.set_xlabel('エポック [回]')
    axL.set_ylabel('損失')
    axL.legend(loc='upper right')

# acc
def plot_history_acc(fit):
    # Plot the loss in the history
    axR.plot(fit.history['acc'],label="トレーニングデータ")
    axR.plot(fit.history['val_acc'],label="検証用データ")
    axR.set_title('エポックごとの正解率')
    axR.set_xlabel('エポック [回]')
    axR.set_ylabel('正解率')
    axR.legend(loc='lower right')  



if __name__ == "__main__":
    day_name="20190207_0"
    test(file_path="./test_char_su/*", day_name=day_name)