Python 使用Keras+；张量流

我正在尝试为我的模型获取渐变wrt输入：

input_mat = np.random.rand(1,252,252,1)
with tf.Session() as sess:
    input_tensor = tf.placeholder(shape=input_mat.shape,dtype=tf.float32)
    outmat = tf.convert_to_tensor(np.dstack((np.identity(252)[:,:,np.newaxis],np.zeros((252,252,36))))[np.newaxis,:,:,:])
    input_layer = tf.keras.layers.Input(shape=(252,252,1))
    layer = tf.keras.layers.Conv2D(activation='relu',kernel_size=(3,3),filters=37,padding='same')(input_layer)
    m = tf.keras.Model(input_layer,layer)

    prob_dist = m(input_tensor)

    loss_dist = tf.keras.losses.categorical_crossentropy(y_pred=prob_dist,y_true=outmat,from_logits=True)      
    grads = K.gradients(loss_dist,m.input)  
    sess.run(tf.global_variables_initializer())
    y = sess.run(grads, feed_dict={input_tensor:input_mat})
            

但是，我得到了以下错误：

TypeError: Fetch argument None has invalid type <class 'NoneType'>

TypeError:Fetch参数None的类型无效

显然，梯度似乎没有。

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如何修复它？

根据

tf.占位符计算渐变：

grads = K.gradients(loss_dist,input_tensor)