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why Automatic differentiation and gradient tape need to use context manager?

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0

Context managers can change two two related operations into one.For example:

with open('some_file', 'w') as opened_file:
 opened_file.write('Hola!')

The above code is equivalent to:

file = open('some_file', 'w')
try:
 file.write('Hola!')
finally:
 file.close()

But in https://www.tensorflow.org/tutorials/eager/custom_training_walkthrough#define_the_loss_and_gradient_function
I found:

def grad(model, inputs, targets):
 with tf.GradientTape() as tape:
 loss_value = loss(model, inputs, targets)
 return loss_value, tape.gradient(loss_value, model.trainable_variables)

what is it equivalent to?

python tensorflow automatic-differentiation

share|improve this question

asked Mar 23 at 4:42

andyandy

98118

add a comment | 

0

Context managers can change two two related operations into one.For example:

with open('some_file', 'w') as opened_file:
 opened_file.write('Hola!')

The above code is equivalent to:

file = open('some_file', 'w')
try:
 file.write('Hola!')
finally:
 file.close()

But in https://www.tensorflow.org/tutorials/eager/custom_training_walkthrough#define_the_loss_and_gradient_function
I found:

def grad(model, inputs, targets):
 with tf.GradientTape() as tape:
 loss_value = loss(model, inputs, targets)
 return loss_value, tape.gradient(loss_value, model.trainable_variables)

what is it equivalent to?

python tensorflow automatic-differentiation

share|improve this question

asked Mar 23 at 4:42

andyandy

98118

add a comment | 

Context managers can change two two related operations into one.For example:

with open('some_file', 'w') as opened_file:
 opened_file.write('Hola!')

The above code is equivalent to:

file = open('some_file', 'w')
try:
 file.write('Hola!')
finally:
 file.close()

But in https://www.tensorflow.org/tutorials/eager/custom_training_walkthrough#define_the_loss_and_gradient_function
I found:

def grad(model, inputs, targets):
 with tf.GradientTape() as tape:
 loss_value = loss(model, inputs, targets)
 return loss_value, tape.gradient(loss_value, model.trainable_variables)

what is it equivalent to?

python tensorflow automatic-differentiation

share|improve this question

asked Mar 23 at 4:42

andyandy

98118

with open('some_file', 'w') as opened_file:
 opened_file.write('Hola!')

file = open('some_file', 'w')
try:
 file.write('Hola!')
finally:
 file.close()

def grad(model, inputs, targets):
 with tf.GradientTape() as tape:
 loss_value = loss(model, inputs, targets)
 return loss_value, tape.gradient(loss_value, model.trainable_variables)

share|improve this question

asked Mar 23 at 4:42

andyandy

98118

share|improve this question

asked Mar 23 at 4:42

andyandy

98118

asked Mar 23 at 4:42

andyandy

98118

asked Mar 23 at 4:42

andyandy

98118