如何在Keras中添加恒定张量? [英] How to add constant tensor in Keras?

问题描述

我想做的是在网络输出中添加一个常数张量:

What I'm trying to do is to add a constant tensor to output of network:

inputs = Input(shape=(config.N_FRAMES_IN_SEQUENCE, config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS))
cnn = VGG16(include_top=False, weights='imagenet', input_shape=(config.IMAGE_H, config.IMAGE_W, config.N_CHANNELS))
x = TimeDistributed(cnn)(inputs)
x = TimeDistributed(Flatten())(x)
x = LSTM(256)(x)
x = Dense(config.N_LANDMARKS * 2, activation='linear')(x)

mean_landmarks = np.array(config.MEAN_LANDMARKS, np.float32)
mean_landmarks = mean_landmarks.flatten()
mean_landmarks_tf = tf.convert_to_tensor(mean_landmarks)
x = x + mean_landmarks_tf

model = Model(inputs=inputs, outputs=x)
optimizer = Adadelta()
model.compile(optimizer=optimizer, loss='mae')

但是我得到了错误:

ValueError: Output tensors to a Model must be the output of a Keras `Layer` (thus holding past layer metadata). Found: Tensor("add:0", shape=(?, 136), dtype=float32)

它在张量流中是微不足道的，但是如何在Keras中实现呢?

It's trivial in tensorflow, but how to do it in Keras?

推荐答案

似乎可以通过Lamda层完成:

Seems it can be done with Lamda layer:

from keras.layers import Lambda

def add_mean_landmarks(x):
    mean_landmarks = np.array(config.MEAN_LANDMARKS, np.float32)
    mean_landmarks = mean_landmarks.flatten()
    mean_landmarks_tf = tf.convert_to_tensor(mean_landmarks)
    x = x + mean_landmarks_tf
    return x


x = Lambda(add_mean_landmarks)(x)

这篇关于如何在Keras中添加恒定张量?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！