エラーの意味が分からない

前提・実現したいこと

Grad-CAM++で特徴量の可視化を行いたいと考えています。
以下のエラーの意味が分からないので、教えていただきたいです

発生している問題・エラーメッセージ

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-17-42f01163d8b3> in <module>()
      7 
      8     img = img_to_array(load_img(image_path,target_size=(row,col,3)))
----> 9     img_GCAMplusplus = Grad_Cam_plus_plus(model, target_layer, img, row, col)
     10     time = time.ctime()
     11     img_Gplusplusname = image_path+time+"_GCAM++_%s.jpg"

2 frames
<ipython-input-16-5889d0d9b241> in Grad_Cam_plus_plus(input_model, layer_name, x, row, col)
     18     grads =K.gradients(class_output, conv_output)[0]
     19     #first_derivative：１階微分
---> 20     first_derivative = K.exp(class_output)[0][class_idx] * grads
     21     #second_derivative：２階微分
     22     second_derivative = K.exp(class_output)[0][class_idx] * grads * grads

/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py in error_handler(*args, **kwargs)
    151     except Exception as e:
    152       filtered_tb = _process_traceback_frames(e.__traceback__)
--> 153       raise e.with_traceback(filtered_tb) from None
    154     finally:
    155       del filtered_tb

/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/op_def_library.py in _apply_op_helper(op_type_name, name, **keywords)
    532           except ValueError as err:
    533             raise ValueError(
--> 534                 f"Tried to convert '{input_name}' to a tensor and failed. "
    535                 f"Error: {err}")
    536           prefix = ("Input '%s' of '%s' Op has type %s that does not match" %

ValueError: Tried to convert 'y' to a tensor and failed. Error: None values not supported.

ソースコード

import pandas as pd
import numpy as np
import cv2
import argparse
import keras
import time
import sys
from keras import backend as K
from keras.preprocessing.image import array_to_img, img_to_array, load_img
from keras.applications.resnet import ResNet50
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
K.set_learning_phase(1)

def Grad_Cam_plus_plus(input_model, layer_name, x, row, col):

    model = input_model

    # 前処理
    X = np.expand_dims(x, axis=0)
    X = X.astype('float32')
    preprocessed_input = X / 255.0


    # 予測クラスの算出
    predictions = model.predict(preprocessed_input)
    class_idx = np.argmax(predictions[0])

    #  使用する重みの抽出、高階微分の計算
    class_output = model.layers[-1].output
    conv_output = model.get_layer(layer_name).output
    grads =K.gradients(class_output, conv_output)[0]
    #first_derivative：１階微分
    first_derivative = K.exp(class_output)[0][class_idx] * grads
    #second_derivative：２階微分
    second_derivative = K.exp(class_output)[0][class_idx] * grads * grads
    #third_derivative：３階微分
    third_derivative = K.exp(class_output)[0][class_idx] * grads * grads * grads

    #関数の定義
    gradient_function = K.function([model.input], [conv_output, first_derivative, second_derivative, third_derivative])  # model.inputを入力すると、conv_outputとgradsを出力する関数


    conv_output, conv_first_grad, conv_second_grad, conv_third_grad = gradient_function([preprocessed_input])
    conv_output, conv_first_grad, conv_second_grad, conv_third_grad = conv_output[0], conv_first_grad[0], conv_second_grad[0], conv_third_grad[0]

    #alphaを求める
    global_sum = np.sum(conv_output.reshape((-1, conv_first_grad.shape[2])), axis=0)
    alpha_num = conv_second_grad
    alpha_denom = conv_second_grad*2.0 + conv_third_grad*global_sum.reshape((1,1,conv_first_grad.shape[2]))
    alpha_denom = np.where(alpha_denom!=0.0, alpha_denom, np.ones(alpha_denom.shape))
    alphas = alpha_num / alpha_denom

    #alphaの正規化
    alpha_normalization_constant = np.sum(np.sum(alphas, axis = 0), axis = 0)
    alpha_normalization_constant_processed = np.where(alpha_normalization_constant != 0.0, alpha_normalization_constant, np.ones(alpha_normalization_constant.shape))
    alphas /= alpha_normalization_constant_processed.reshape((1,1,conv_first_grad.shape[2]))

    #wの計算
    weights = np.maximum(conv_first_grad, 0.0)
    deep_linearization_weights = np.sum((weights * alphas).reshape((-1, conv_first_grad.shape[2])))

    #Lの計算
    grad_CAM_map = np.sum(deep_linearization_weights * conv_output, axis=2)
    grad_CAM_map = np.maximum(grad_CAM_map, 0)
    grad_CAM_map = grad_CAM_map / np.max(grad_CAM_map)

    #ヒートマップを描く
    grad_CAM_map = cv2.resize(grad_CAM_map, (row, col), cv2.INTER_LINEAR)
    jetcam = cv2.applyColorMap(np.uint8(255 * grad_CAM_map), cv2.COLORMAP_JET)  # モノクロ画像に疑似的に色をつける
    jetcam = (np.float32(jetcam) + x / 2)   # もとの画像に合成

    return jetcam

if __name__ == '__main__':
    model = keras.models.load_model("/content/drive/MyDrive/ji/saved_model/my_model")
    target_layer = 'inception_v3'
    image_path = '/content/drive/MyDrive/Cs-N013.jpg'
    row = 224
    col = 224

    img = img_to_array(load_img(image_path,target_size=(row,col,3)))
    img_GCAMplusplus = Grad_Cam_plus_plus(model, target_layer, img, row, col)
    time = time.ctime()
    img_Gplusplusname = image_path+time+"_GCAM++_%s.jpg"
    cv2.imwrite(img_Gplusplusname, img_GCAMplusplus)
    print("Completed.")