我如何在Keras中获得图层的权重?
deep-learning
keras
keras-layer
python
5
0

我正在使用Windows 10,Python 3.5和tensorflow 1.1.0。我有以下脚本:

import tensorflow as tf
import tensorflow.contrib.keras.api.keras.backend as K
from tensorflow.contrib.keras.api.keras.layers import Dense

tf.reset_default_graph()
init = tf.global_variables_initializer()
sess =  tf.Session()
K.set_session(sess) # Keras will use this sesssion to initialize all variables

input_x = tf.placeholder(tf.float32, [None, 10], name='input_x')    
dense1 = Dense(10, activation='relu')(input_x)

sess.run(init)

dense1.get_weights()

我收到错误: AttributeError: 'Tensor' object has no attribute 'weights'

我在做什么错了,我怎么得到dense1的权重?我已经看过这个这个 SO帖子,但是我仍然无法使其正常工作。

参考资料:
Stack Overflow
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如果要获取所有图层的权重和偏差,则可以简单地使用:

for layer in model.layers: print(layer.get_config(), layer.get_weights())

这将打印所有相关信息。

如果要直接将权重作为numpy数组返回,则可以使用:

first_layer_weights = model.layers[0].get_weights()[0]
first_layer_biases  = model.layers[0].get_weights()[1]
second_layer_weights = model.layers[1].get_weights()[0]
second_layer_biases  = model.layers[1].get_weights()[1]

等等

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如果您写:

dense1 = Dense(10, activation='relu')(input_x)

那么dense1不是图层,而是图层的输出。该层是Dense(10, activation='relu')

所以看来您的意思是:

dense1 = Dense(10, activation='relu')
y = dense1(input_x)

这是完整的代码段:

import tensorflow as tf
from tensorflow.contrib.keras import layers

input_x = tf.placeholder(tf.float32, [None, 10], name='input_x')    
dense1 = layers.Dense(10, activation='relu')
y = dense1(input_x)

weights = dense1.get_weights()
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如果要查看图层的权重和偏差随时间变化的方式,可以添加一个回调以在每个训练时期记录其值。

例如,使用这样的模型,

import numpy as np
model = Sequential([Dense(16, input_shape=(train_inp_s.shape[1:])), Dense(12), Dense(6), Dense(1)])

在拟合期间添加回调** kwarg:

gw = GetWeights()
model.fit(X, y, validation_split=0.15, epochs=10, batch_size=100, callbacks=[gw])

回调在哪里定义

class GetWeights(Callback):
    # Keras callback which collects values of weights and biases at each epoch
    def __init__(self):
        super(GetWeights, self).__init__()
        self.weight_dict = {}

    def on_epoch_end(self, epoch, logs=None):
        # this function runs at the end of each epoch

        # loop over each layer and get weights and biases
        for layer_i in range(len(self.model.layers)):
            w = self.model.layers[layer_i].get_weights()[0]
            b = self.model.layers[layer_i].get_weights()[1]
            print('Layer %s has weights of shape %s and biases of shape %s' %(
                layer_i, np.shape(w), np.shape(b)))

            # save all weights and biases inside a dictionary
            if epoch == 0:
                # create array to hold weights and biases
                self.weight_dict['w_'+str(layer_i+1)] = w
                self.weight_dict['b_'+str(layer_i+1)] = b
            else:
                # append new weights to previously-created weights array
                self.weight_dict['w_'+str(layer_i+1)] = np.dstack(
                    (self.weight_dict['w_'+str(layer_i+1)], w))
                # append new weights to previously-created weights array
                self.weight_dict['b_'+str(layer_i+1)] = np.dstack(
                    (self.weight_dict['b_'+str(layer_i+1)], b))

此回调将建立一个包含所有层权重和偏差的字典,并用层号标记,因此您可以查看它们在训练模型时随时间的变化。您会注意到每个权重和偏差数组的形状取决于模型层的形状。为模型中的每一层保存一个权重数组和一个偏差数组。第三轴(深度)显示了它们随时间的变化。

在这里,我们使用了10个纪元和一个具有16、12、6和1个神经元层的模型:

for key in gw.weight_dict:
    print(str(key) + ' shape: %s' %str(np.shape(gw.weight_dict[key])))

w_1 shape: (5, 16, 10)
b_1 shape: (1, 16, 10)
w_2 shape: (16, 12, 10)
b_2 shape: (1, 12, 10)
w_3 shape: (12, 6, 10)
b_3 shape: (1, 6, 10)
w_4 shape: (6, 1, 10)
b_4 shape: (1, 1, 10)
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