Self.gamma_constraint = gamma_constraint def build(self, input_shape): Self.gamma_regularizer = gamma_regularizer Self.gamma_initializer = gamma_initializer Gamma_initializer=tf.zeros_initializer(), Plz help where i am going wrong ,I am a begginerД®nter image description here from keras.layers import Layer When i train the model ,in first epoch itself itis throughing a type error:TypeError: 'NoneType' object is not callable.

I am trying to apply attention layer on VGG16's last convolution layer. How do I get the model to treat the shape(n, 5) as a single input? To do that, I tried this nse1 = (Д«ut that did not do anything to the output shape. Does this mean all n feature sets were checked against the one label? I want the model to treat the shape(n, 5) as a single input. When I give the model features of shape(1, n, 5) and labels of shape(1, 1), it returns shape(1, n, 5). Here is the model class Model(keras.Model):Дef call(self, x2, training=True, mask=None): I built a model from a tutorial that works well with features shape(n, 5) and labels shape(n, 1) and returns an output of shape(n, 1), which matches the input label. I am trying to build a sequential model in Keras that will take an input feature of shape(1, n, 5) and an input label of shape(1, 1) and return an output of shape(1, 1) to match the input label.

Keras one-hot text encoding