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This method simply combines calls compute_gradients () and apply_gradients (). Here are the examples of the python api tensorflow.train.AdamOptimizer.minimize taken from open source projects. By voting up you can indicate which examples are most useful and appropriate. minimize (loss, global_step=None, var_list=None, gate_gradients=GATE_OP, aggregation_method=None, colocate_gradients_with_ops=False, name=None, grad_loss=None) Add operations to minimize loss by updating var_list. This method simply combines calls compute_gradients () and apply_gradients (). losses = tfp.math.minimize( loss_fn, num_steps=1000, optimizer=tf.optimizers.Adam(learning_rate=0.1), convergence_criterion=( tfp.optimizers.convergence_criteria.LossNotDecreasing(atol=0.01))) Here num_steps=1000 defines an upper bound: the optimization will be stopped after 1000 steps even if no convergence is detected. Optimizer that implements the Adam algorithm.
Compat aliases for migration. See Migration guide for more details. tf.compat.v1.keras.optimizers.Optimizer. tf.keras.optimizers.Optimizer ( name, gradient_aggregator=None, gradient_transformers=None, **kwargs ) You should not use this class directly, but instead instantiate one of its subclasses such as tf.keras.
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2020-12-11 · Calling minimize () takes care of both computing the gradients and applying them to the variables. If you want to process the gradients before applying them you can instead use the optimizer in three steps: Compute the gradients with tf.GradientTape. Process the gradients as you wish. from tensorflow.python.keras.optimizers import Adam, SGD print(tf.version.VERSION) optim = Adam() optim.minimize(loss, var_list=network.weights) output: 2.0.0-alpha0 Traceback (most recent call last): File "/Users/ikkamens/Library/Preferences/PyCharmCE2018.3/scratches/testo.py", line 18, in
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. Adam optimizer goes haywire after 200k batches, training loss grows (2) . I've been seeing a very strange behavior when training a network, where after a couple of 100k iterations (8 to 10 hours) of learning fine, everything breaks and the training loss grows:. The training data itself is randomized and spread across many .tfrecord files containing 1000 examples each, then shuffled again in
To do that we will need an optimizer.
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Variable ( 0 ) learning_rate = tf . train .