Keras Custom Layer: ValueError: An operation has `None` for gradient

Question

I am trying to implement Graph Convolution Layer using Keras custom layer that is mentioned in the following paper: GCNN.

When I am trying to train my model, It gives me the following error:

Traceback (most recent call last):
File "main.py", line 35, in <module>
model.fit(train_images, train_labels, validation_data=(test_images, test_labels), epochs=50, batch_size=32)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1010, in fit
self._make_train_function()
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 509, in _make_train_function
loss=self.total_loss)
File "/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/keras/optimizers.py", line 256, in get_updates
grads = self.get_gradients(loss, params)
File "/usr/local/lib/python2.7/dist-packages/keras/optimizers.py", line 91, in get_gradients
raise ValueError('An operation has `None` for gradient. '
ValueError: An operation has `None` for gradient. Please make sure that all of your ops have a gradient defined (i.e. are differentiable). Common ops without gradient: K.argmax, K.round, K.eval.

I don't know how to get rid of this problem.

Can someone explain me briefly what should I do?

I have gone through Keras official documentation about writing custom layer but it didn't specify about it. Link

Following is the code for my custom layer.

class GraphConvolutionalLayer(Layer):

def __init__(self, A, num_input_features, num_output_features, **kwargs):
    self.A = A
    self.num_input_features = num_input_features
    self.num_output_features = num_output_features

    self.num_vertices = A.get_shape().as_list()[0]
    self.input_spec = (self.num_vertices, num_input_features)

    super(GraphConvolutionalLayer, self).__init__(**kwargs)

def build(self, input_shape):
    self.k0 = self.add_weight(name='k0', 
                                  shape=(self.num_output_features, self.num_input_features),
                                  initializer='uniform',
                                  trainable=True)
    self.k1 = self.add_weight(name='k1', 
                          shape=(self.num_output_features, self.num_input_features),
                          initializer='uniform',
                          trainable=True)

    self.H = tf.einsum('ab,cd->abcd', tf.convert_to_tensor(self.k0, dtype=tf.float32), tf.eye(self.num_vertices)) 
    self.built = True

def call(self, Vin):

    Vin2 = tf.reshape(tf.transpose(Vin, [0, 2, 1]), [Vin.get_shape().as_list()[1] * Vin.get_shape().as_list()[2], -1])

    H_tmp = tf.reshape(tf.transpose(self.H, [0, 2, 1, 3]), [ self.num_output_features, self.num_vertices, self.num_vertices * self.num_input_features])

    Vout = tf.transpose(K.dot(H_tmp, Vin2), [2, 1, 0])

    return Vout

def compute_output_shape(self, input_shape):
    return (self.num_vertices, self.num_output_features)

Following is the code for the main file.

main_input = Input(shape=train_images[0].shape)
Vout1 = GraphConvolutionalLayer(A, 1, 4)(main_input)
Vout2 = GraphConvolutionalLayer(A, 4, 8)(Vout1)
Vout3 = Flatten()(Vout2)
Vout4 = Dense(10, activation='sigmoid')(Vout3)
print(train_images.shape, train_labels.shape)

model = Model(inputs=main_input, outputs=Vout4)
print(model.summary())
model.compile(optimizer='rmsprop', loss='binary_crossentropy')
model.fit(train_images, train_labels, validation_data=(test_images, test_labels), epochs=50, batch_size=32)

Answer 1

Here, I take uniform as an initializer. When I changed it, I didn't get any error. I don't know why this happened but I could be able to solve my error just changing that line.