将张量流图实现到 Keras 模型中

Question

我正在尝试在 Keras（最好）或 Tensorflow 中大致实现以下架构。

          ___________      _________      _________     ________    ______
          | Conv    |     | Max    |     | Dense  |    |       |   |     |
Input0--> | Layer 1 | --> | Pool 1 | --> | Layer  | -->|       |   |     |
          |_________|     |________|     |________|    | Sum   |   | Out |
                                                       | Layer |-->|_____|
Input1    ----------- Converted to trainable weights-->|       |              
                                                       |_______|                                                                               |_______|

简而言之，它几乎是一个具有两个输入的模型，使用 Add([input0, input1]) 层合并为一个输出。诀窍是输入之一必须被视为变量 = 可训练权重。

Keras 层 Add() 不允许这样，它把 input0 和 input1 作为不可训练的变量：

input0    = Input((28,28,1))
x         = Conv2D(32, kernel_size=(3, 3), activation='relu',input_shape=input_shape)(mod1)
x         = Conv2D(64, (3, 3), activation='relu')(input0)
x         = MaxPooling2D(pool_size=(2, 2))(x)
x         = Flatten()(x)
x         = Dense(128, activation='relu')(x)

input1    = Input((128,))

x         = Add()([x, input1])
x         = Dense(num_classes, activation='softmax')(x)
model     = Model(inputs = [mod1,TPM], outputs = x)
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

我可以在 tensorflow 中实现一个图，它添加一个占位符 X 和权重 b，并学习 b 相对于目标 Y 的值。

train_X = numpy.asarray([1.0, 2.0])
train_Y = numpy.asarray([0.0, 2.5])
n_samples = train_X.shape[0]

# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")

# Set model weights
b = tf.Variable([0.0, 0.0], name="bias")

# Construct a linear model
pred = tf.add(X, b)

loss = tf.reduce_mean(tf.square(pred - train_Y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
grads_and_vars = optimizer.compute_gradients(loss)

train = optimizer.apply_gradients(grads_and_vars)
#init = tf.initialize_all_variables()
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for step in range(epochs):
    sess.run(train, feed_dict={X: train_X, Y: train_Y})

这完全符合我的要求。输入和权重的简单可优化添加。但我不能将其包含在 Keras 中 model.I 我错过了如何合并这两个想法的步骤。

我如何包含一个仅将一个可训练张量与一个不可训练张量相加的层？

Answer 1

我不确定我是否完全理解您的需求。根据您的张量流代码，我认为您不必输入初始值。在那种情况下，我希望以下内容至少接近您想要的内容：

import numpy as np
import keras
from keras import backend as K
from keras.engine.topology import Layer
from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense, Add

class MyLayer(Layer):

    def __init__(self, bias_init, **kwargs):
        self.bias_init = bias_init
        super(MyLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        self.bias = self.add_weight(name='bias',
                                    shape=input_shape[1:],
                                    initializer=keras.initializers.Constant(self.bias_init),
                                    trainable=True)
        super(MyLayer, self).build(input_shape)  # Be sure to call this somewhere!

    def call(self, x):
        return x + self.bias

input0    = Input((28,28,1))
x         = Conv2D(32, kernel_size=(3, 3), activation='relu',input_shape=(28,28,1))(input0)
x         = Conv2D(64, (3, 3), activation='relu')(input0)
x         = MaxPooling2D(pool_size=(2, 2))(x)
x         = Flatten()(x)
x         = Dense(128, activation='relu')(x)

input1    = np.random.rand(128)

x         = MyLayer(input1)(x)
x         = Dense(10, activation='softmax')(x)
model     = Model(inputs=input0, outputs=x)
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

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