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本文主要讨论windows下基于tensorflow的keras
1、找到tensorflow的根目录
如果安装时使用anaconda且使用默认安装路径，则在 C:\ProgramData\Anaconda3\envs\tensorflow-gpu\Lib\site-packages\tensorflow处可以找到(此处为GPU版本)，cpu版本可在C:\ProgramData\Anaconda3\Lib\site-packages\tensorflow处找到。若并非使用默认安装路径，可参照根目录查看找到。
2、找到keras在tensorflow下的根目录
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需要特别注意的是找到keras在tensorflow下的根目录而不是找到keras的根目录。一般来说，完成tensorflow以及keras的配置后即可在tensorflow目录下的python目录中找到keras目录，以GPU为例keras在tensorflow下的根目录为C:\ProgramData\Anaconda3\envs\tensorflow-gpu\Lib\site-packages\tensorflow\python\keras
3、找到keras目录下的optimizers.py文件并添加自己的优化器
找到optimizers.py中的adam等优化器类并在后面添加自己的优化器类
以本文来说，我在第718行添加如下代码
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | @tf_export('keras.optimizers.adamsss')class Adamsss(Optimizer): def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=None, schedule_decay=0.004, **kwargs): super(Adamsss, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.m_schedule = K.variable(1., name='m_schedule') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') if epsilon is None: epsilon = K.epsilon() self.epsilon = epsilon self.schedule_decay = schedule_decay def get_updates(self, loss, params): grads = self.get_gradients(loss, params) self.updates = [state_ops.assign_add(self.iterations, 1)] t = math_ops.cast(self.iterations, K.floatx()) + 1 # Due to the recommendations in [2], i.e. warming momentum schedule momentum_cache_t = self.beta_1 * ( 1. - 0.5 * (math_ops.pow(K.cast_to_floatx(0.96), t * self.schedule_decay))) momentum_cache_t_1 = self.beta_1 * ( 1. - 0.5 * (math_ops.pow(K.cast_to_floatx(0.96), (t + 1) * self.schedule_decay))) m_schedule_new = self.m_schedule * momentum_cache_t m_schedule_next = self.m_schedule * momentum_cache_t * momentum_cache_t_1 self.updates.append((self.m_schedule, m_schedule_new)) shapes = [K.int_shape(p) for p in params] ms = [K.zeros(shape) for shape in shapes] vs = [K.zeros(shape) for shape in shapes] self.weights = [self.iterations] + ms + vs for p, g, m, v in zip(params, grads, ms, vs): # the following equations given in [1] g_prime = g / (1. - m_schedule_new) m_t = self.beta_1 * m + (1. - self.beta_1) * g m_t_prime = m_t / (1. - m_schedule_next) v_t = self.beta_2 * v + (1. - self.beta_2) * math_ops.square(g) v_t_prime = v_t / (1. - math_ops.pow(self.beta_2, t)) m_t_bar = ( 1. - momentum_cache_t) * g_prime + momentum_cache_t_1 * m_t_prime self.updates.append(state_ops.assign(m, m_t)) self.updates.append(state_ops.assign(v, v_t)) p_t = p - self.lr * m_t_bar / (K.sqrt(v_t_prime) + self.epsilon) new_p = p_t # Apply constraints. if getattr(p, 'constraint', None) is not None: new_p = p.constraint(new_p) self.updates.append(state_ops.assign(p, new_p)) return self.updates def get_config(self): config = { 'lr': float(K.get_value(self.lr)), 'beta_1': float(K.get_value(self.beta_1)), 'beta_2': float(K.get_value(self.beta_2)), 'epsilon': self.epsilon, 'schedule_decay': self.schedule_decay } base_config = super(Adamsss, self).get_config() return dict(list(base_config.items()) + list(config.items())) |
然后修改之后的优化器调用类添加我自己的优化器adamss
需要修改的有(下面的两处修改依旧在optimizers.py内)
1 2 3 4 5 6 7 8 9 10 | # Aliases.sgd = SGDrmsprop = RMSpropadagrad = Adagradadadelta = Adadeltaadam = Adamadamsss = Adamsssadamax = Adamaxnadam = Nadam |
以及
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | def deserialize(config, custom_objects=None): """Inverse of the `serialize` function. Arguments: config: Optimizer configuration dictionary. custom_objects: Optional dictionary mapping names (strings) to custom objects (classes and functions) to be considered during deserialization. Returns: A Keras Optimizer instance. """ if tf2.enabled(): all_classes = { 'adadelta': adadelta_v2.Adadelta, 'adagrad': adagrad_v2.Adagrad, 'adam': adam_v2.Adam, 'adamsss': adamsss_v2.Adamsss, 'adamax': adamax_v2.Adamax, 'nadam': nadam_v2.Nadam, 'rmsprop': rmsprop_v2.RMSprop, 'sgd': gradient_descent_v2.SGD } else: all_classes = { 'adadelta': Adadelta, 'adagrad': Adagrad, 'adam': Adam, 'adamax': Adamax, 'nadam': Nadam, 'adamsss': Adamsss, 'rmsprop': RMSprop, 'sgd': SGD, 'tfoptimizer': TFOptimizer } |
这里我们并没有v2版本，所以if后面的部分不改也可以。
4、调用我们的优化器对模型进行设置
model.compile(loss = 'crossentropy', optimizer = 'adamss', metrics=['accuracy'])
5、训练模型
train_history = model.fit(x, y_label, validation_split = 0.2, epoch = 10, batch = 128, verbose = 1)
补充知识：keras设置学习率--优化器的用法
优化器的用法
优化器 (optimizer) 是编译 Keras 模型的所需的两个参数之一：
1 2 3 4 5 6 7 8 | from keras import optimizers model = Sequential()model.add(Dense(64, kernel_initializer='uniform', input_shape=(10,)))model.add(Activation('softmax')) sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)model.compile(loss='mean_squared_error', optimizer=sgd) |
你可以先实例化一个优化器对象，然后将它传入 model.compile()，像上述示例中一样， 或者你可以通过名称来调用优化器。在后一种情况下，将使用优化器的默认参数。
# 传入优化器名称: 默认参数将被采用
model.compile(loss='mean_squared_error', optimizer='sgd')
以上这篇如何在keras中添加自己的优化器(如adam等)就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。
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