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python - 如何在 Keras 中添加常量张量？

问题描述

我想要做的是在网络的输出中添加一个常量张量：

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')

但我得到错误：

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)

这在 tensorflow 中是微不足道的，但在 Keras 中怎么做呢？

标签： pythontensorflowkerasdeep-learning

解决方案

似乎可以用 Lamda 层完成：

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)

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