###前提・実現したいこと
Python（Keras）でCNNを用い2種類の画像を判別するプログラムを作っております。
その時に学習したモデルを活用して判定時にどこに注目したのかを
grad-camにより、表現したいと考えております。
grad-camについては
https://github.com/jacobgil/keras-grad-cam/blob/master/grad-cam.py
こちらのコードを参考にしており自分のモデルに合わせて対象箇所を変えたつもりです。

モデルはこちらになります。

Layer (type) Output Shape Param #

conv2d_1 (Conv2D) (None, 62, 62, 32) 896

max_pooling2d_1 (MaxPooling2 (None, 31, 31, 32) 0

dropout_1 (Dropout) (None, 31, 31, 32) 0

zero_padding2d_1 (ZeroPaddin (None, 33, 33, 32) 0

conv2d_2 (Conv2D) (None, 31, 31, 96) 27744

max_pooling2d_2 (MaxPooling2 (None, 15, 15, 96) 0

dropout_2 (Dropout) (None, 15, 15, 96) 0

zero_padding2d_2 (ZeroPaddin (None, 17, 17, 96) 0

conv2d_3 (Conv2D) (None, 15, 15, 96) 83040

max_pooling2d_3 (MaxPooling2 (None, 7, 7, 96) 0

flatten_1 (Flatten) (None, 4704) 0

dense_1 (Dense) (None, 1024) 4817920

dropout_3 (Dropout) (None, 1024) 0

dense_2 (Dense) (None, 2) 2050

Total params: 4,931,650
Trainable params: 4,931,650
Non-trainable params: 0

###発生している問題・エラーメッセージ

IndexError                                Traceback (most recent call last)
<ipython-input-3-2fe13b62daac> in <module>()
    128 
    129 predicted_class = np.argmax(predictions)
--> 130 cam, heatmap = grad_cam(model, preprocessed_input, predicted_class, "conv2d_3")
    131 cv2.imwrite("gradcam.jpg", cam)
    132 

<ipython-input-3-2fe13b62daac> in grad_cam(input_model, image, category_index, layer_name)
     92 
     93     loss = K.sum(model.layers[-1].output)
---> 94     conv_output =  [l for l in model.layers[0].layers if l.name is layer_name][0].output
     95     grads = normalize(K.gradients(loss, conv_output)[0])
     96     gradient_function = K.function([model.layers[0].input], [conv_output, grads])

IndexError: list index out of range

###該当のソースコード

python
from keras.preprocessing import image
from keras.layers.core import Lambda
from keras.models import Sequential ,load_model
from tensorflow.python.framework import ops
import keras.backend as K
import tensorflow as tf
import numpy as np
import keras
import sys
import cv2

def target_category_loss(x, category_index, nb_classes):
    return tf.multiply(x, K.one_hot([category_index], nb_classes))

def target_category_loss_output_shape(input_shape):
    return input_shape

def normalize(x):
    # utility function to normalize a tensor by its L2 norm
    return x / (K.sqrt(K.mean(K.square(x))) + 1e-5)

def load_image(path):
    img_path = path
    img = image.load_img(img_path, target_size=(64, 64))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    return x

def register_gradient():
    if "GuidedBackProp" not in ops._gradient_registry._registry:
        @ops.RegisterGradient("GuidedBackProp")
        def _GuidedBackProp(op, grad):
            dtype = op.inputs[0].dtype
            return grad * tf.cast(grad > 0., dtype) * \
                tf.cast(op.inputs[0] > 0., dtype)

def compile_saliency_function(model, activation_layer='conv2d_3'):
    input_img = model.input
    layer_dict = dict([(layer.name, layer) for layer in model.layers[1:]])
    layer_output = layer_dict[activation_layer].output
    max_output = K.max(layer_output, axis=3)
    saliency = K.gradients(K.sum(max_output), input_img)[0]
    return K.function([input_img, K.learning_phase()], [saliency])

def modify_backprop(model, name):
    g = tf.get_default_graph()
    with g.gradient_override_map({'Relu': name}):

        # get layers that have an activation
        layer_dict = [layer for layer in model.layers[1:]
                      if hasattr(layer, 'activation')]

        # replace relu activation
        for layer in layer_dict:
            if layer.activation == keras.activations.relu:
                layer.activation = tf.nn.relu

        # re-instanciate a new model
        new_model = model
    return new_model

def deprocess_image(x):
    '''
    Same normalization as in:
    https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py
    '''
    if np.ndim(x) > 3:
        x = np.squeeze(x)
    # normalize tensor: center on 0., ensure std is 0.1
    x -= x.mean()
    x /= (x.std() + 1e-5)
    x *= 0.1

    # clip to [0, 1]
    x += 0.5
    x = np.clip(x, 0, 1)

    # convert to RGB array
    x *= 255
    if K.image_dim_ordering() == 'th':
        x = x.transpose((1, 2, 0))
    x = np.clip(x, 0, 255).astype('uint8')
    return x

def grad_cam(input_model, image, category_index, layer_name):
    model = Sequential()
    model.add(input_model)

    nb_classes = 2
    target_layer = lambda x: target_category_loss(x, category_index, nb_classes)
    model.add(Lambda(target_layer,
                     output_shape = target_category_loss_output_shape))

    loss = K.sum(model.layers[-1].output)
    conv_output =  [l for l in model.layers[0].layers if l.name is layer_name][0].output
    grads = normalize(K.gradients(loss, conv_output)[0])
    gradient_function = K.function([model.layers[0].input], [conv_output, grads])

    output, grads_val = gradient_function([image])
    output, grads_val = output[0, :], grads_val[0, :, :, :]

    weights = np.mean(grads_val, axis = (0, 1))
    cam = np.ones(output.shape[0 : 2], dtype = np.float32)

    for i, w in enumerate(weights):
        cam += w * output[:, :, i]

    cam = cv2.resize(cam, (64, 64))
    cam = np.maximum(cam, 0)
    heatmap = cam / np.max(cam)

    #Return to BGR [0..255] from the preprocessed image
    image = image[0, :]
    image -= np.min(image)
    image = np.minimum(image, 255)

    cam = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET)
    cam = np.float32(cam) + np.float32(image)
    cam = 255 * cam / np.max(cam)
    return np.uint8(cam), heatmap

# 判定画像の読み込み
preprocessed_input = load_image('image.jpeg')

model = load_model('mymodel.h5')
model.summary()

predictions = model.predict(preprocessed_input)

predicted_class = np.argmax(predictions)
cam, heatmap = grad_cam(model, preprocessed_input, predicted_class, "conv2d_3")
cv2.imwrite("gradcam.jpg", cam)

register_gradient()
guided_model = modify_backprop(model, 'GuidedBackProp')
saliency_fn = compile_saliency_function(guided_model)
saliency = saliency_fn([preprocessed_input, 0])
gradcam = saliency[0] * heatmap[..., np.newaxis]
cv2.imwrite("guided_gradcam.jpg", deprocess_image(gradcam))