在卷积层CNN上更改过滤器-Python/TensorFlow

在TF2中可以这样做:

model = models.Sequential()
# one 3x3 filter
model.add(layers.Conv2D(1, (3, 3), input_shape=(None, None, 1)))
# access to the target layer
layer = model.layers[0]
current_w, current_bias = layer.get_weights()  # see the current weights
new_w = tf.constant([[1,2, 3],
                     [4, 5, 6],
                     [7, 8, 9]])
new_w = tf.reshape(new_w, custom_w.shape)  # fix the shape
new_bias = tf.constant([0])
layer.set_weights([new_w, new_bias])
model.summary()
# let's see ..
tf.print(model.layers[0].get_weights())

如果您想通过一些预定义的值初始化权重,可以使用 tf.constant_initializer tf.constant tf.Variable

def new_weights(init_vaue, is_const):
    if (is_const) :
        return tf.constant(init_vaue, name='weights')
    else:
        initializer = tf.constant_initializer(init_vaue)
        return tf.get_variable('weights', shape = init_vaue.shape, initializer=initializer)

weights = np.ones([3,3,1,8], dtype=np.float)
print(weights.shape)

value = new_weights(weights, True)
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    value_ = sess.run(value) 
    print(value_)

您只需将权重定义为不可训练,并将新权重定义为:

new_weights = tf.Variable( tf.reshape(weights, (3,3,1,8)),trainable=False)
# then apply on the inputs 
layer = tf.nn.conv2d(inputs, filter=new_weights, strides=[1, 1, 1, 1], padding='SAME')