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Oct 10, 2019 · 39. Yes, absolutely. From my own experience, it's very useful to Adam with learning rate decay. Without decay, you have to set a very small learning rate so the loss won't begin to diverge after decrease to a point. Here, I post the code to use Adam with learning rate decay using TensorFlow. Hope it is helpful to someone.
To me, this answer like similar others has a major disadvantage. Where and how we should specify the optimizer inside the .compile() method of the model. In your example above you specify LearningRateScheduler which is fine and the model.fit(). But where is the model.compile() statement with the initialization of the Adam optimizer.
Jul 3, 2020 · I had a similar problem after a whole day lost on this. I found that just: from tensorflow.python.keras.optimizers import adam_v2. adam_v2.Adam(learning_rate=0.0001, clipnorm=1.0, clipvalue=0.5) works for me (i had v2.11.0 of tensorflow). I also find these other optimizers in tensorflow.python.keras.optimizers:
4. Adam is an optimizer method, the result depend of two things: optimizer (including parameters) and data (including batch size, amount of data and data dispersion). Then, I think your presented curve is ok. Concerning the learning rate, Tensorflow, Pytorch and others recommend a learning rate equal to 0.001.
Dec 17, 2020 · In the paper Attention is all you need, under section 5.3, the authors suggested to increase the learning rate linearly and then decrease proportionally to the inverse square root of steps. How do...
Sep 17, 2021 · For most PyTorch codes we use the following definition of Adam optimizer, optim = torch.optim.Adam(model.parameters(), lr=cfg['lr'], weight_decay=cfg['weight_decay']) However, after repeated trials, I found that the following definition of Adam gives 1.5 dB higher PSNR which is huge.
It seems as some Adam update node modifies the value of my upconv_logits5_fs towards nan. This transposed convolution op is the very last of my network and therefore the first one to be updated. I'm working with a tf.nn.softmax_cross_entropy_with_logits() loss and put tf.verify_tensor_all_finite() on all it's in- and outputs, but they don't trigger errors.
Oct 31, 2020 · Both are subclassed from optimizer.Optimizer and in fact, their source codes are almost identical; in particular, the variables updated in each iteration are the same. The only difference is that the definition of Adam's weight_decay is deferred to the parent class while AdamW's weight_decay is defined in the AdamW class itself.
Jul 17, 2018 · batch_size is used in optimizer that divide the training examples into mini batches. Each mini batch is of size batch_size. I am not familiar with adam optimization, but I believe it is a variation of the GD or Mini batch GD. Gradient Descent - has one big batch (all the data), but multiple epochs.
Sep 12, 2021 · Generally, Maybe you used a different version for the layers import and the optimizer import. tensorflow.python.keras API for model and layers and keras.optimizers for SGD. They are two different Keras versions of TensorFlow and pure Keras.
