TensorFlow的layer类中有两个属性: trainable_variable和trainable_weights,它们有什么区别呢? 答案,一模一样,见trainable_variable和trainable_weights的代码实现: trainable_variable就是trainable_weights 实践结果如下: trainable_variable和trainable_weights对比试验结果 参考链接:https://github.com/tensorflow/tensorflow/b...
双等号(==) 符号检查松散相等，而三等号(===) 符号检查严格相等。不同之处在于 (==) 松散相等将...
trainable_weights)) trainable weights: 0 trainable weights: 6 可以通过构建网络层的方法来收集loss class LossLayer(layers.Layer): def __init__(self, rate=1e-2): super(LossLayer, self).__init__() self.rate = rate def call(self, inputs): self.add_loss(self.rate * tf.reduce_sum(...
trainable_weights属性:可训练的Variables列表,在模型训练时需要这个属性; non_trainable_weights属性:不可训练的Variables列表; weights属性:trainable_weights和non_trainable_weights的合集; trainable属性:可变动的bool值,决定layer是否可以训练。 Layer类是keras中最基本的类,对其有个全面的认识比较重要,具体可以看源码。...
self._action_output.trainable_weights + \ self._value_output.trainable_weights + \ [self._action_dist_std]defcall(self, inputs): x = self._layers0forlayerinself._layers[1:self._num_layers]: x = layer(x)returnself._value_output(x)deflog_prob(self, x):returnself._action_dist.log...
print('trainable weight:', my_layer.trainable_weights) [3. 3. 3.] [6. 6. 6.] weight: [] non-trainable weight: [] trainable weight: [] 当定义网络时不知道网络的维度是可以重写build()函数,用获得的shape构建网络 class MyLayer(layers.Layer): ...
def apply_gradient(optimizer, loss_object, model, x, y): with tf.GradientTape() as tape: logits = model(x) loss_value = loss_object(y_true=y, y_pred=logits) gradients = tape.gradient(loss_value, model.trainable_weights) optimizer.apply_gradients(zip(gradients, model.trainable_weights))...
optimizer.apply_gradients(zip(gradients, linear_layer.trainable_weights)) # Logging. ifstep %100==0: print(step, float(loss)) 5)层创建的权重可以是可训练的,也可以是不可训练的,是否可训练在trainable_weights和non_trainable_weights中查看。下面这个是一个不可训练的层: ...
如果声明变量时参数trainable为True,那么这个变量将会被自动加入到GraphKeys.TRAINABLE_VARIABLES集合。tensorflow中提供的神经网络优化算法会将GraphKeys.TRAINABLE_VARIABLES集合中的变量作为默认的优化对象。 类似张量,维度(shape)和类型(type)也是变量最重要的两个属性。和大部分程序语言类似,变量的类型是不可改变的。一个...
regularization_losses=self.losses, ) # Compute gradients trainable_vars = self.trainable_variables gradients = tape.gradient(loss, trainable_vars) # Update weights self.optimizer.apply_gradients(zip(gradients, trainable_vars)) self.compiled_metrics.update_state(y, y_pred) ou...