密集输出列表

Question

I'm making a model whose output I want to be of dims (A,B). So I'm making a list of denses (A elements, B outputs each) and I wanted my output to be (No_samples, A,B). It is a list of A elements with (No_samples,B). The method of having one dense with output AxB does not help because for every row I want to softmax accross only that

I've attempted to use tf.concatenate, tf.reshape, but there is always either an error or the same undesirable output. My difficulty is that in order to proceed I have to do some really weird reshaping and I wish to avoid that by

for i in range(0, A):
    outputs.append(Dense(B, activation="softmax")(out))

And I've tried everything below (separately):

outputs = tf.stack(outputs)
outputs = Reshape(self.output_shape)(outputs)

outputs = tf.convert_to_tensor(outputs)

The expected outcome is that the output has shape of (A,?,B) instead of (?, A, B). Is there another method that I can have multiple denses in parralel with the above behaviour?

Answer 1

Simple example with A=3, B=1.

from keras import backend as K
from keras.layers import Concatenate, Dense, Input, Lambda
from keras.models import Model
import numpy as np

def expand_dims(x):
    return K.expand_dims(x, axis=-2) #expand (None, 1) to (None, 1, 1)

x = Input((2,))
A = 3
B = 1
y = Lambda(expand_dims)(Dense(B, activation="softmax")(x))
for i in range(0, A-1):
    # Concatenate on the newly added dimension
    y = Concatenate(axis=-2)([y,Lambda(expand_dims)(Dense(B, activation="softmax")(x))])

model = Model(x, y)
print(model.predict(np.ones((4,2))).shape)
(4, 3, 1)  # Output shape is (No_samples, A,B)