如何在keras模型中设置常数作为权重？

Question

I build my model using tf.keras.layers.Dense . In the first layer of my model I want some weights to be constant Zero. As in the gradient calculation these weights should be get a gradient = zero (as the last term in the chain rule corresponds to the weight, which is 0 for a constant). This is my approach so far:

import tensorflow as tf
import tensorflow.contrib.eager as tfe    
import numpy as np

tf.enable_eager_execution()

model = tf.keras.Sequential([
  tf.keras.layers.Dense(2, activation=tf.sigmoid, input_shape=(2,)),
  tf.keras.layers.Dense(2, activation=tf.sigmoid)
])

weights=[np.array([[tf.constant(0), 0.25],[0.2,0.3]]),np.array([0.35,0.35]),np.array([[0.4,0.5],[0.45, 0.55]]),np.array([0.6,0.6])]
model.set_weights(weights)
def loss(model, x, y):
  y_ = model(x)
  return tf.losses.mean_squared_error(labels=y, predictions=y_)

    def grad(model, inputs, targets):
  with tf.GradientTape() as tape:
    loss_value = loss(model, inputs, targets)
  return loss_value, tape.gradient(loss_value, model.trainable_variables) 

But in the gradient calculation the weight tf.constant(0) has a gradient not equal zero. Do I have an understanding problem?

How can I set a weight(or some weights) in a layer( not all weights in one layer) to a constant value (which should not change during training)?

Answer 1

My answer is based on the CustomConnected layer from this answer . As I said in a comment, when you multiply a weight w_ij by c_ij=0 via the connections matrix, the gradient of the loss with respect to that weight becomes zero as well (since the last factor in the chain rule corresponds to c_ij=0 ).

Here is a minimal example in Keras:

# Using CustomConnected from:
# https://stackoverflow.com/questions/50290769/specify-connections-in-nn-in-keras  
import tensorflow as tf
import numpy as np

tf.enable_eager_execution()

# Define model
inp = tf.keras.layers.Input(shape=(2,))
c = np.array([[1., 1.], [1., 0.]], dtype=np.float32)
h = CustomConnected(2, c)(inp)
model = tf.keras.models.Model(inp, h)

# Set initial weights and compile
w = [np.random.rand(2, 2) * c]
model.set_weights(w)
model.compile(tf.train.AdamOptimizer(), 'mse')

# Check gradients
x = tf.constant(np.random.rand(10, 2), dtype=tf.float32)
y = np.random.rand(10, 2)

with tf.GradientTape() as tape:
    loss_value = tf.losses.mean_squared_error(labels=y, predictions=model(x))
    grad = tape.gradient(loss_value, model.trainable_variables)
    print('Gradients: ', grad[0])

Note that I set c[1,1]=0 so the gradient corresponding to weight w[1,1] is 0 regardless of the input.