我正在尝试使用 Keras 重写一段 tflearn 代码。
目标是组合两个输入，其中一个输入跳过第一层。以下代码在 tflearn 中有效：
# Two different inputs.
inputs = tflearn.input_data(shape=[None, 10])
action = tflearn.input_data(shape=[None, 10])
#First layer used only by the inputs
net = tflearn.fully_connected(inputs, 400, activation='relu')
# Add the action tensor in the 2nd hidden layer
# Use two temp layers to get the corresponding weights and biases
t1 = tflearn.fully_connected(net, 300)
t2 = tflearn.fully_connected(action, 300)
# Combine the two layers using the weights from t1 and t2 and the bias from t2
net = tflearn.activation(tf.matmul(net,t1.W) + tf.matmul(action, t2.W) + t2.b, activation='relu')
我正在尝试使用以下代码在 Keras 中复制此代码：
# Two different inputs.
inputs = tf.placeholder(tf.float32, [None, 10])
action = tf.placeholder(tf.float32, [None, 10])
#First layer used only by the inputs
t1 = Sequential()
t1.add(Dense(400, activation='relu', input_shape=(1,10)))
# Add the action tensor in the 2nd hidden layer
# Use two temp layers to get the corresponding weights and biases
t1.add(Dense(300))
t2 = Sequential()
t2.add(Dense(300, input_shape=(1,10)))
# Combine the two layers
critnet = Sequential()
critnet.add(Merge([t1, t2], mode='sum'))
critnet.add(Activation('relu'))
# Create the net using the inputs and action placeholder
net = critnet([inputs, action])
keras 中的代码表现不同。如何在 keras 中组合两层以获得与 tflearn 中相同的结果？