如何从tf.train.AdamOptimizer获取当前学习速率？内容来源于 Stack Overflow，并遵循 CC BY-SA 3.0 许可协议进行翻译与使用回答 ( 3 )

If your code works in TensorFlow 2.x using tf.compat.v1.disable_v2_behavior , there v1.train.AdamOptimizer can be converted to use tf.keras.optimizers.Adam . If the loss is a callable (such as a function), use Optimizer.minimize t

# minimize rate = tf.Variable(0.1) # learning rate, alpha optimizer = tf.train.GradientDescentOptimizer(rate) train = optimizer.minimize(cost) ValueError: tf.function-decorated function tried to create variables on non-first call. Problem looks like tf.keras.optimizers.Adam(0.5).minimize(loss, var_list=[y_N]) creates new variable on > first call, while using @tf.function. If I must wrap adam_optimizer under @tf.function, is it possible? looks like a bug?

TensorFlow version: 2.0.0-dev20190618; Python version: 3.6; Describe the current behavior I am trying to minimize a function using tf.keras.optimizers.Adam.minimize() and I am getting a TypeError Here are the examples of the python api tensorflow.train.AdamOptimizer taken from open source projects. By voting up you can indicate which examples are most useful and appropriate. In most Tensorflow code I have seen Adam Optimizer is used with a constant Learning Rate of 1e-4 (i.e. 0.0001). The code usually looks the following:build the model # Add the optimizer train_op = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) # Add the ops to initialize variables.

tf.AdamOptimizer apply_gradients. Mr Ko. AI is my favorite domain as a professional Researcher. What I am doing is Reinforcement Learning,Autonomous Driving,Deep Learning,Time series …

Describe the current behavior I am trying to minimize a function using tf.keras.optimizers.Adam.minimize () and I am getting a TypeError. Describe the expected behavior First, in the TF 2.0 docs, it says the loss can be callable taking no arguments which returns the value to minimize. whereas the type error reads "'tensorflow.python.framework.ops.

tf.train.AdamOptimizer.minimize 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.

minimize 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(). 2017-07-02 It’s calculating [math]\frac{dL}{dW}[/math]. In other words, it find gradients of the loss with respect to all the weights/variables that are trainable inside your graph. It then do gradient descent one step: [math]W = W - \alpha\frac{dL}{dW}[/mat VGP (data, kernel, likelihood) optimizer = tf. optimizers. Adam optimizer.

System information TensorFlow version: 2.0.0-dev20190618 Python version: 3.6 Describe the current behavior I am trying to minimize a function using tf.keras.optimizers.Adam.minimize() and I am gett Optimizer that implements the Adam algorithm. See Kingma et al., 2014 .
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optimizer = tf.train.AdamOptimizer().minimize(cost) Within AdamOptimizer(), you can optionally specify the learning_rate as a parameter.

2019-04-01 2020-12-11 adam = tf.train.AdamOptimizer(learning_rate=0.3) # the optimizer We need a way to call the optimization function on each step of gradient descent. We do this by assigning the call to minimize to a 2021-01-18 Optimizer that implements the Adam algorithm.
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Minimize loss by updating var_list. This method simply computes gradient using tf.GradientTape and calls apply_gradients (). If you want to process the gradient before applying then call tf.GradientTape and apply_gradients () explicitly instead of using this function.

else: raise NotImplementedError("Reduce in tower-mode is not implemented.") [docs] def Adam.

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Branched from tf.train.AdamOptimizer. The only difference is to pass global step for computing beta1 and beta2 accumulators, instead of having optimizer keep its own independent beta1 and beta2 accumulators as non-slot variables. What this does is that, if you had put prior as uniform, the optimizer will have to search from 1e-4 (0.0001 ) to 1e-1 (0.1) in a uniform distribution. But when declared as log-uniform, the optimizer will search between -4 and -1, thus making the process much more efficient. Define optimizer or solver scopes with tf.name_scope('adam_optimizer'): optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001) train_op = optimizer.minimize( loss=loss, global_step=tf.train.get_global_step()) Define the LMSHook Gradient Centralization TensorFlow . This Python package implements Gradient Centralization in TensorFlow, a simple and effective optimization technique for Deep Neural Networks as suggested by Yong et al. in the paper Gradient Centralization: A New Optimization Technique for Deep Neural Networks.It can both speedup training process and improve the final generalization performance of … 2021-02-10 · Compute gradients of loss for the variables in var_list.

tf tf.AggregationMethod tf.argsort tf… VGP (data, kernel, likelihood) optimizer = tf. optimizers. Adam optimizer.