神经网络的Keras模型load_weights

Question

我正在使用Keras库在python中创建一个神经网络.我已经加载了训练数据(txt文件),启动了网络并"适应"了神经网络的权重.然后我编写了代码来生成输出文本.这是代码:

#!/usr/bin/env python

# load the network weights
filename = "weights-improvement-19-2.0810.hdf5"
model.load_weights(filename)
model.compile(loss='categorical_crossentropy', optimizer='adam')

我的问题是:执行时会产生以下错误:

 model.load_weights(filename)
 NameError: name 'model' is not defined

我添加了以下内容,但错误仍然存​​在:

from keras.models import Sequential
from keras.models import load_model

任何帮助,将不胜感激.

Answer 1

你需要先创建一个被调用的网络对象model,然后在调用后才能编译它model.load_weights(fname)

工作实例:

from keras.models import Sequential
from keras.layers import Dense, Activation


def build_model():
    model = Sequential()

    model.add(Dense(output_dim=64, input_dim=100))
    model.add(Activation("relu"))
    model.add(Dense(output_dim=10))
    model.add(Activation("softmax"))

    # you can either compile or not the model
    model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
    return model


model1 = build_model()
model1.save_weights('my_weights.model')


model2 = build_model()
model2.load_weights('my_weights.model')

# do stuff with model2 (e.g. predict())