使用 tf.keras 过程中，如果要使用 learning rate decay，不要使用 tf.train.AdamOptimizer() 等 tf.train 内的优化器，因为学习率的命名不同，导致 tf.keras 中学习率衰减的函数无法使用，一般都会报错 “AttributeError: 'TFOptimizer' object has no attribute 'lr'”，这个时候即使我们对 "lr" 参数赋值，也没有办法在之后过程中

Specifically, the accuracy we managed to get in 30 epochs (which is the necessary time for SGD to get to 94% accuracy with a 1cycle policy) with Adam and L2 regularization was at 93.96% on average, going over 94% one time out of two. We consistently reached values between 94% and 94.25% with Adam and weight decay.

Exponential decay. Another popular learning rate schedule is to drop the learning rate at an exponential rate. Formally, it is defined as: learning_rate = initial_lr * e^(−k * epoch) Where initial_lr is the initial learning rate such as 0.01, k is a hyperparameter, and epoch is the current epoch number. Defaults to "Adam". **kwargs: keyword arguments. Allowed to be {clipnorm, clipvalue, lr, decay}. clipnorm is clip gradients by norm; clipvalue is clip gradients by value, decay is included for backward compatibility to allow time inverse decay of learning rate.

Eager Compatibility. When eager execution is enabled, learning_rate, beta1, beta2, and epsilon can each be a callable that takes no arguments and returns the actual value to use. This can be useful for changing these values across different invocations of optimizer functions. Methods tf.train.AdamOptimizer.apply_gradients Learning rate schedule. Initial rate can be left as system default or can be selected using a range of techniques. A learning rate schedule changes the learning rate during learning and is most often changed between epochs/iterations.

Ah it’s interesting how you make the learning rate scheduler first in TensorFlow, then pass it into your optimizer. In PyTorch, we first make the optimizer: my_model = torchvision.models.resnet50() my_optim = torch.optim.Adam(params=my_model.params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)

I do not want the 'staircase = true' version. The decay_steps for me feels like the number of steps that the learning rate keeps constant. But I am not sure about this and Tensorflow has not stated it in their documentation. Any help is much appreciated.

decay: 学习率随每次更新进行衰减. amsgrad: 布尔型，是否使用 AMSGrad 变体. 下面我们来看看decay是如何发挥作用的：. if self.initial_decay > 0: lr = lr * (1. / (1. + self.decay * K.cast(self.iterations,K.dtype(self.decay)))) 写为数学表达式的形式为：. 为了更好的观察学习率的衰减情况，我们将学习率lr的衰减过程画出来，lr取0.01，decay取0.01. lr=0.01,decacy=0.0001,iterations=500.

optimizer – Wrapped optimizer. step_size – Period of learning rate decay. The following are 30 code examples for showing how to use keras.optimizers.Adam().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

The schedule is a 1-arg callable that produces a decayed learning rate when passed the current optimizer step. learning_rate: A Tensor or a floating point value. The learning rate.
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Adam class. tf.keras.optimizers.Adam( learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False, name="Adam", **kwargs ) Optimizer that implements the Adam algorithm. Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments. Trying to read a little more about learning rate decay and Adam makes me think that I probably don't fully understand how various optimizers operate over batches in Tensorflow. Taking a step back from RL, it's pretty evident that the effective learning rate decreases over the batches in each epoch with a vanilla deep learning model.

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tf.keras.optimizers.Adam( learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False, name="Adam", **kwargs ) Optimizer that implements the Adam algorithm. Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments.