22. 在上面的代码里有几个关键的部分需要注意:optimizer、wa,wb,wc,wd、loss、grads、apply_gradients这五个部分。optimizer是优化器,常用的有SGD和Adam,前者是梯度下降后者则可以理解为加强版,对于本示例只有 9 条数据这种样本比较少的情况有奇效。因为Adam对每个参数使用相同的学习率,并随着学习的进行而独立地适应。
apply_gradients(zip(gradients, self.policy.neural_network.trainable_variables)) # 训练强化学习算法 neural_network = NeuralNetwork() policy = Policy(neural_network) reinforcement_learning_algorithm = ReinforcementLearningAlgorithm(policy, learning_rate=0.01) for episode in range(1000): state = env....
1. ops = tf.train.GradientDescentOptimizer(learning_rate) 构建优化器 参数说明:learning_rate 表示输入的学习率 2.ops.compute_gradients(loss, tf.train_variables(), colocate_gradients_with_ops=True) 参数说明:loss表示损失值, tf.train_variables() 表示需要更新的参数, colocate_gradients_with_ops= True...
optimizer = tf.train.AdamOptimizer(learningRate) # apply_gradients(应用梯度)将之前用(Gradient Clipping)梯度裁剪过的梯度 应用到可被训练的变量上去,做梯度下降 # apply_gradients 其实是 minimize 方法里面的第二步,第一步是 计算梯度 trainOP = optimizer.apply_gradients(zip(grads, trainableVariables)) glob...
# 定义训练函数deftrain_step(features, labels):withtf.GradientTape()astape:predictions = linear_regression(features)loss_value = loss_fn(labels, predictions)gradients = tape.gradient(loss_value, [w, b])optimizer.apply_gradients(zip(gradients, [w, b])) ...
(d_loss, self.discriminator.trainable_weights) self.d_optimizer.apply_gradients( zip(grads, self.discriminator.trainable_weights) ) random_latent_vectors = tf.random.normal(shape=(batch_size, self.latent_dim)) # 赋生成网络样本的标签(都赋为真实样本) misleading_labels = tf.zeros((batch_size, ...
def train_step(inputs, targets): with tf.GradientTape() as tape: predictions = model(inputs, training=True) loss = loss_fn(targets, predictions) gradients = tape.gradients(loss, model.trainable_weights) optimizer.apply_gradients(zip(model.trainable_weights, gradients)) 7.4.2 指标的低级使用 ...
advantages, returns):...cost = loss(actions, observations, advantages, returns)grads = g.gradient(cost, params)grads = [grad if grad is not None else tf.zeros_like(var)for var, grad in zip(params, grads)]self._optimizer.apply_gradients(zip(grads, params),experimental_aggregate_gradients=...
...: apply( ...: lambda row: ...: '{} Vs {}'.format( ...: row[ ...: 'king_pair' ...: ][0], ...: row[ ...: 'king_pair' ...: ][1]), ...: axis=1) ...: archenemy_df.sort_values('battle_count', ...: inplace=True, ...: ascending=False) ...: ......
apply(input, weight, bias) 也可以将 LinearFunction 封装到 nn.Module 里面,以便更简单的使用。 检查梯度计算是否正确 pytorch 提供了一个简单的 接口用来检查 定义的 梯度计算是否正确 代码语言:javascript 代码运行次数:0 运行 AI代码解释 from torch.autograd import gradcheck # Check gradients computed via ...