如何在 Keras 中连接两层？

我有一个两层神经网络的例子。第一层接受两个参数并有一个输出。第二个参数应该采用第一层的结果和一个额外的参数。它应该是这样的:

x1  x2  x3
\  /   /
y1   /
\  /
y2

因此，我创建了一个具有两个层的模型，并试图合并它们，但它返回一个错误: result.add(merged)行上的 The first layer in a Sequential model must get an "input_shape" or "batch_input_shape" argument.。

Model:

first = Sequential()
first.add(Dense(1, input_shape=(2,), activation='sigmoid'))


second = Sequential()
second.add(Dense(1, input_shape=(1,), activation='sigmoid'))


result = Sequential()
merged = Concatenate([first, second])
ada_grad = Adagrad(lr=0.1, epsilon=1e-08, decay=0.0)
result.add(merged)
result.compile(optimizer=ada_grad, loss=_loss_tensor, metrics=['accuracy'])
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最佳答案

You're getting the error because result defined as Sequential() is just a container for the model and you have not defined an input for it.

给定您试图构建的集合 result以获取第三个输入 x3。

first = Sequential()
first.add(Dense(1, input_shape=(2,), activation='sigmoid'))


second = Sequential()
second.add(Dense(1, input_shape=(1,), activation='sigmoid'))


third = Sequential()
# of course you must provide the input to result which will be your x3
third.add(Dense(1, input_shape=(1,), activation='sigmoid'))


# lets say you add a few more layers to first and second.
# concatenate them
merged = Concatenate([first, second])


# then concatenate the two outputs


result = Concatenate([merged,  third])


ada_grad = Adagrad(lr=0.1, epsilon=1e-08, decay=0.0)


result.compile(optimizer=ada_grad, loss='binary_crossentropy',
metrics=['accuracy'])

但是，构建具有此类输入结构的模型的首选方法是使用 功能性 API。

下面是您的需求的一个实现，可以帮助您开始:

from keras.models import Model
from keras.layers import Concatenate, Dense, LSTM, Input, concatenate
from keras.optimizers import Adagrad


first_input = Input(shape=(2, ))
first_dense = Dense(1, )(first_input)


second_input = Input(shape=(2, ))
second_dense = Dense(1, )(second_input)


merge_one = concatenate([first_dense, second_dense])


third_input = Input(shape=(1, ))
merge_two = concatenate([merge_one, third_input])


model = Model(inputs=[first_input, second_input, third_input], outputs=merge_two)
ada_grad = Adagrad(lr=0.1, epsilon=1e-08, decay=0.0)
model.compile(optimizer=ada_grad, loss='binary_crossentropy',
metrics=['accuracy'])

在评论中回答这个问题:

  1. How are result and merged connected? Assuming you mean how are they concatenated.

Concatenation works like this:

  a        b         c
a b c   g h i    a b c g h i
d e f   j k l    d e f j k l

即行刚刚连接。

  1. 现在，x1输入到第一个，x2输入到第二个，x3输入到第三个。
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您可以尝试使用 model.summary()(请注意连接层的大小)

# merge samples, two input must be same shape
inp1 = Input(shape=(10,32))
inp2 = Input(shape=(10,32))
cc1 = concatenate([inp1, inp2],axis=0) # Merge data must same row column
output = Dense(30, activation='relu')(cc1)
model = Model(inputs=[inp1, inp2], outputs=output)
model.summary()


# merge row must same column size
inp1 = Input(shape=(20,10))
inp2 = Input(shape=(32,10))
cc1 = concatenate([inp1, inp2],axis=1)
output = Dense(30, activation='relu')(cc1)
model = Model(inputs=[inp1, inp2], outputs=output)
model.summary()


# merge column must same row size
inp1 = Input(shape=(10,20))
inp2 = Input(shape=(10,32))
cc1 = concatenate([inp1, inp2],axis=1)
output = Dense(30, activation='relu')(cc1)
model = Model(inputs=[inp1, inp2], outputs=output)
model.summary()

你可在此查阅详情: https://nbviewer.jupyter.org/github/anhhh11/DeepLearning/blob/master/Concanate_two_layer_keras.ipynb

小开

添加上述接受的答案，以便它帮助那些谁正在使用 tensorflow 2.0


import tensorflow as tf


# some data
c1 = tf.constant([[1, 1, 1], [2, 2, 2]], dtype=tf.float32)
c2 = tf.constant([[2, 2, 2], [3, 3, 3]], dtype=tf.float32)
c3 = tf.constant([[3, 3, 3], [4, 4, 4]], dtype=tf.float32)


# bake layers x1, x2, x3
x1 = tf.keras.layers.Dense(10)(c1)
x2 = tf.keras.layers.Dense(10)(c2)
x3 = tf.keras.layers.Dense(10)(c3)


# merged layer y1
y1 = tf.keras.layers.Concatenate(axis=1)([x1, x2])


# merged layer y2
y2 = tf.keras.layers.Concatenate(axis=1)([y1, x3])


# print info
print("-"*30)
print("x1", x1.shape, "x2", x2.shape, "x3", x3.shape)
print("y1", y1.shape)
print("y2", y2.shape)
print("-"*30)

结果:

------------------------------
x1 (2, 10) x2 (2, 10) x3 (2, 10)
y1 (2, 20)
y2 (2, 30)
------------------------------

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