我想做的是向网络的输出添加一个常量张量：
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')
但我收到错误：
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中很简单，但是在Keras中如何做到呢？
似乎可以用 Lambda 层来完成：
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)
本文内容由网友自发贡献，版权归原作者所有，本站不承担相应法律责任。如您发现有涉嫌抄袭侵权的内容，请联系:hwhale#tublm.com(使用前将#替换为@)
