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在卷积层CNN上更改过滤器-Python/TensorFlow

convolution tensorflow python-2.7 python

whoisraibolt · 技术社区 · 8 年前

def new_weights(shape):
    return tf.Variable(tf.truncated_normal(shape, stddev=0.05))

def new_conv_layer(input,              # The previous layer
                   use_pooling=True):  # Use 2x2 max-pooling

    shape = [3, 3, 1, 8]

    weights = new_weights(shape=shape)

    biases = new_biases(length=8)

    layer = tf.nn.conv2d(input=input,
                         filter=weights,
                         strides=[1, 1, 1, 1],
                         padding='SAME')

    layer += biases

    if use_pooling:
        layer = tf.nn.max_pool(value=layer,
                               ksize=[1, 2, 2, 1],
                               strides=[1, 2, 2, 1],
                               padding='SAME')

    layer = tf.nn.relu(layer)

    # relu(max_pool(x)) == max_pool(relu(x)) we can
    # save 75% of the relu-operations by max-pooling first.

    return layer

因此我们可以观察到滤波器的大小是3x3,滤波器的数量是8。并且滤波器是用随机值定义的。

我需要做的是用固定值定义所有8个过滤器,即预定值,例如:

weigths = [
    [[0,  1, 0,],[0, -1, 0,],[0,  0, 0,],],
    [[0,  0, 1,],[0, -1, 0,],[0,  0, 0,],],
    [[0,  0, 0,],[0, -1, 1,],[0,  0, 0,],],
    [[0,  0, 0,],[0, -1, 0,],[0,  0, 1,],],
    [[0,  0, 0,],[0, -1, 0,],[0,  1, 0,],],
    [[0,  0, 0,],[0, -1, 0,],[1,  0, 0,],], 
    [[0,  0, 0,],[1, -1, 0,],[0,  0, 0,],],
    [[1,  0, 0,],[0, -1, 0,],[0,  0, 0,],]
]

我无法想象,我怎么能在我的代码里做这些修改,有人知道我怎么做吗?

3 回复 | 直到 8 年前

Ali Salehi 4 年前

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())

Vladimir Bystricky 8 年前

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_)

Vijay Mariappan 8 年前

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')