Question

我想基于我的自定义图层创建一个简单的NN模型，并从中提取权重。到目前为止，权重列表为空。起初，我无意训练，也没有定义任何损失函数或优化器。我应该如何更改以便可以看到和提取权重？

我知道Saving model weights in Keras: what is model weights?，并打算像在那儿一样对model.save_weights（）进行建模。

    import tensorflow as tf
    from tensorflow.contrib.keras import layers, models
    import numpy as np
    from tensorflow.contrib import eager as tfe
    tfe.enable_eager_execution()

    class MyLayer(layers.Layer):
        def __init__(self, out_channels):
        print("initializing MyLayer")
        super(MyLayer, self).__init__()
        self.out_channels = out_channels

        self.bn = layers.BatchNormalization(name="bn1", momentum=0.04)
        self.linear = layers.Dense(self.out_channels, activation=None, use_bias=False)
        self.bn2 = layers.BatchNormalization(name="bn2", momentum=0.04)
        self.linear2 = layers.Dense(32, activation=None, use_bias=False)


    def build(self, input_shape):
        print("building MyLayer")
        super(MyLayer, self).build(input_shape)


    def call(self, inputs, **kwargs):
        print("calling MyLayer")
        x = self.linear(tf.convert_to_tensor(inputs))
        x = self.bn(x)
        x = tf.nn.relu(x)
        x = self.linear2(x)
        x = self.bn2(x)
        x = tf.nn.relu(x)
        return x

if __name__ == '__main__':
    o = MyLayer(16)
    inputs = np.random.rand(1,2,3)

    input_layer = layers.Input(shape=(2,3), batch_size=1)

    features = o(input_layer)
    model = models.Model([input_layer], [features])
    feat_out = model.predict_on_batch([inputs])
    print("weights: {}".format(model.get_weights()))
    print("features: {}".format(feat_out))

Keras自定义图层在模型中不显示权重

0 个答案: