[WIP]grad cam in keras

grad_cam_tf_keras.py

K = tf.keras.backend

class GradCam(object):
    def __init__(self, model, loss=tf.keras.losses.CategoricalCrossentropy()):
        self._model = model
        self._loss_model_truth = tf.keras.layers.Input((model.layers[-1].output.shape[-1],))
        self._loss_model = loss(self._loss_model_truth, model.layers[-1].output)
        
    def run(self, X, target_class_vector, target_layer):
        conv_output = model.get_layer(name=target_layer).output
        
        grads = K.gradients(self._loss_model, conv_output)[0]
        gradient_function = K.function([
            self._model.layers[0].input,
            self._loss_model_truth
        ], [conv_output, grads])
        
        output, grads_val = gradient_function([X[np.newaxis, :], target_class_vector[np.newaxis, :]])
        output, grads_val = output[0], grads_val[0]
        
        gradient_weight = np.mean(grads_val, axis=(0, 1)) #GAP

        score = output * gradient_weight
        score = score * (score > 0).astype(np.float32) # relu
        
        return score