In [25]:
import tensorflow as tf
In [26]:
interactive_session=tf.InteractiveSession()
In [27]:
x=tf.constant([[3.0,4.0]])
In [28]:
y_=tf.constant([16.0])
In [29]:
w=tf.Variable([[100.0],[80.0]])
In [30]:
y=tf.matmul(x,w)
In [31]:
loss=tf.abs(tf.reduce_mean(y-y_))
In [37]:
optim = tf.train.GradientDescentOptimizer(0.05)
#help(tf.train.AdamOptimizer)
Help on class AdamOptimizer in module tensorflow.python.training.adam:
class AdamOptimizer(tensorflow.python.training.optimizer.Optimizer)
| Optimizer that implements the Adam algorithm.
|
| See [Kingma et. al., 2014](http://arxiv.org/abs/1412.6980)
| ([pdf](http://arxiv.org/pdf/1412.6980.pdf)).
|
| Method resolution order:
| AdamOptimizer
| tensorflow.python.training.optimizer.Optimizer
| builtins.object
|
| Methods defined here:
|
| __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False, name='Adam')
| Construct a new Adam optimizer.
|
| Initialization:
|
| ```
| m_0 <- 0 (Initialize initial 1st moment vector)
| v_0 <- 0 (Initialize initial 2nd moment vector)
| t <- 0 (Initialize timestep)
| ```
|
| The update rule for `variable` with gradient `g` uses an optimization
| described at the end of section2 of the paper:
|
| ```
| t <- t + 1
| lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t)
|
| m_t <- beta1 * m_{t-1} + (1 - beta1) * g
| v_t <- beta2 * v_{t-1} + (1 - beta2) * g * g
| variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon)
| ```
|
| The default value of 1e-8 for epsilon might not be a good default in
| general. For example, when training an Inception network on ImageNet a
| current good choice is 1.0 or 0.1.
|
| The sparse implementation of this algorithm (used when the gradient is an
| IndexedSlices object, typically because of `tf.gather` or an embedding
| lookup in the forward pass) does apply momentum to variable slices even if
| they were not used in the forward pass (meaning they have a gradient equal
| to zero). Momentum decay (beta1) is also applied to the entire momentum
| accumulator. This means that the sparse behavior is equivalent to the dense
| behavior (in contrast to some momentum implementations which ignore momentum
| unless a variable slice was actually used).
|
| Args:
| learning_rate: A Tensor or a floating point value. The learning rate.
| beta1: A float value or a constant float tensor.
| The exponential decay rate for the 1st moment estimates.
| beta2: A float value or a constant float tensor.
| The exponential decay rate for the 2nd moment estimates.
| epsilon: A small constant for numerical stability.
| use_locking: If True use locks for update operations.
| name: Optional name for the operations created when applying gradients.
| Defaults to "Adam".
|
| ----------------------------------------------------------------------
| Methods inherited from tensorflow.python.training.optimizer.Optimizer:
|
| apply_gradients(self, grads_and_vars, global_step=None, name=None)
| Apply gradients to variables.
|
| This is the second part of `minimize()`. It returns an `Operation` that
| applies gradients.
|
| Args:
| grads_and_vars: List of (gradient, variable) pairs as returned by
| `compute_gradients()`.
| global_step: Optional `Variable` to increment by one after the
| variables have been updated.
| name: Optional name for the returned operation. Default to the
| name passed to the `Optimizer` constructor.
|
| Returns:
| An `Operation` that applies the specified gradients. If `global_step`
| was not None, that operation also increments `global_step`.
|
| Raises:
| TypeError: If `grads_and_vars` is malformed.
| ValueError: If none of the variables have gradients.
|
| compute_gradients(self, loss, var_list=None, gate_gradients=1, aggregation_method=None, colocate_gradients_with_ops=False, grad_loss=None)
| Compute gradients of `loss` for the variables in `var_list`.
|
| This is the first part of `minimize()`. It returns a list
| of (gradient, variable) pairs where "gradient" is the gradient
| for "variable". Note that "gradient" can be a `Tensor`, an
| `IndexedSlices`, or `None` if there is no gradient for the
| given variable.
|
| Args:
| loss: A Tensor containing the value to minimize.
| var_list: Optional list or tuple of `tf.Variable` to update to minimize
| `loss`. Defaults to the list of variables collected in the graph
| under the key `GraphKey.TRAINABLE_VARIABLES`.
| gate_gradients: How to gate the computation of gradients. Can be
| `GATE_NONE`, `GATE_OP`, or `GATE_GRAPH`.
| aggregation_method: Specifies the method used to combine gradient terms.
| Valid values are defined in the class `AggregationMethod`.
| colocate_gradients_with_ops: If True, try colocating gradients with
| the corresponding op.
| grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`.
|
| Returns:
| A list of (gradient, variable) pairs. Variable is always present, but
| gradient can be `None`.
|
| Raises:
| TypeError: If `var_list` contains anything else than `Variable` objects.
| ValueError: If some arguments are invalid.
|
| get_name(self)
|
| get_slot(self, var, name)
| Return a slot named `name` created for `var` by the Optimizer.
|
| Some `Optimizer` subclasses use additional variables. For example
| `Momentum` and `Adagrad` use variables to accumulate updates. This method
| gives access to these `Variable` objects if for some reason you need them.
|
| Use `get_slot_names()` to get the list of slot names created by the
| `Optimizer`.
|
| Args:
| var: A variable passed to `minimize()` or `apply_gradients()`.
| name: A string.
|
| Returns:
| The `Variable` for the slot if it was created, `None` otherwise.
|
| get_slot_names(self)
| Return a list of the names of slots created by the `Optimizer`.
|
| See `get_slot()`.
|
| Returns:
| A list of strings.
|
| minimize(self, loss, global_step=None, var_list=None, gate_gradients=1, 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()`. If you want to process the gradient before applying
| them call `compute_gradients()` and `apply_gradients()` explicitly instead
| of using this function.
|
| Args:
| loss: A `Tensor` containing the value to minimize.
| global_step: Optional `Variable` to increment by one after the
| variables have been updated.
| var_list: Optional list or tuple of `Variable` objects to update to
| minimize `loss`. Defaults to the list of variables collected in
| the graph under the key `GraphKeys.TRAINABLE_VARIABLES`.
| gate_gradients: How to gate the computation of gradients. Can be
| `GATE_NONE`, `GATE_OP`, or `GATE_GRAPH`.
| aggregation_method: Specifies the method used to combine gradient terms.
| Valid values are defined in the class `AggregationMethod`.
| colocate_gradients_with_ops: If True, try colocating gradients with
| the corresponding op.
| name: Optional name for the returned operation.
| grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`.
|
| Returns:
| An Operation that updates the variables in `var_list`. If `global_step`
| was not `None`, that operation also increments `global_step`.
|
| Raises:
| ValueError: If some of the variables are not `Variable` objects.
|
| ----------------------------------------------------------------------
| Data descriptors inherited from tensorflow.python.training.optimizer.Optimizer:
|
| __dict__
| dictionary for instance variables (if defined)
|
| __weakref__
| list of weak references to the object (if defined)
|
| ----------------------------------------------------------------------
| Data and other attributes inherited from tensorflow.python.training.optimizer.Optimizer:
|
| GATE_GRAPH = 2
|
| GATE_NONE = 0
|
| GATE_OP = 1
In [33]:
train_step=optim.minimize(loss)
In [34]:
init_op=tf.initialize_all_variables()
WARNING:tensorflow:From <ipython-input-34-beca12014393>:1: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
In [35]:
interactive_session.run(init_op)
In [36]:
for i in range(500):
interactive_session.run(train_step)
l=interactive_session.run(loss)
weight=interactive_session.run(w)
yy=interactive_session.run(y)
print(weight)
print(l)
print(yy)
[[ 99.84999847]
[ 79.80000305]]
602.75
[[ 618.75]]
[[ 99.69999695]
[ 79.6000061 ]]
601.5
[[ 617.5]]
[[ 99.54999542]
[ 79.40000916]]
600.25
[[ 616.25]]
[[ 99.3999939 ]
[ 79.20001221]]
599.0
[[ 615.]]
[[ 99.24999237]
[ 79.00001526]]
597.75
[[ 613.75]]
[[ 99.09999084]
[ 78.80001831]]
596.5
[[ 612.50006104]]
[[ 98.94998932]
[ 78.60002136]]
595.25
[[ 611.25006104]]
[[ 98.79998779]
[ 78.40002441]]
594.0
[[ 610.00006104]]
[[ 98.64998627]
[ 78.20002747]]
592.75
[[ 608.75006104]]
[[ 98.49998474]
[ 78.00003052]]
591.5
[[ 607.50006104]]
[[ 98.34998322]
[ 77.80003357]]
590.25
[[ 606.25012207]]
[[ 98.19998169]
[ 77.60003662]]
589.0
[[ 605.00012207]]
[[ 98.04998016]
[ 77.40003967]]
587.75
[[ 603.75012207]]
[[ 97.89997864]
[ 77.20004272]]
586.5
[[ 602.50012207]]
[[ 97.74997711]
[ 77.00004578]]
585.25
[[ 601.25012207]]
[[ 97.59997559]
[ 76.80004883]]
584.0
[[ 600.00012207]]
[[ 97.44997406]
[ 76.60005188]]
582.75
[[ 598.75012207]]
[[ 97.29997253]
[ 76.40005493]]
581.5
[[ 597.50012207]]
[[ 97.14997101]
[ 76.20005798]]
580.25
[[ 596.25012207]]
[[ 96.99996948]
[ 76.00006104]]
579.0
[[ 595.00012207]]
[[ 96.84996796]
[ 75.80006409]]
577.75
[[ 593.75012207]]
[[ 96.69996643]
[ 75.60006714]]
576.5
[[ 592.50018311]]
[[ 96.5499649 ]
[ 75.40007019]]
575.25
[[ 591.25018311]]
[[ 96.39996338]
[ 75.20007324]]
574.0
[[ 590.00018311]]
[[ 96.24996185]
[ 75.00007629]]
572.75
[[ 588.75018311]]
[[ 96.09996033]
[ 74.80007935]]
571.5
[[ 587.50018311]]
[[ 95.9499588]
[ 74.6000824]]
570.25
[[ 586.25024414]]
[[ 95.79995728]
[ 74.40008545]]
569.0
[[ 585.00024414]]
[[ 95.64995575]
[ 74.2000885 ]]
567.75
[[ 583.75024414]]
[[ 95.49995422]
[ 74.00009155]]
566.5
[[ 582.50024414]]
[[ 95.3499527]
[ 73.8000946]]
565.25
[[ 581.25024414]]
[[ 95.19995117]
[ 73.60009766]]
564.0
[[ 580.00024414]]
[[ 95.04994965]
[ 73.40010071]]
562.75
[[ 578.75024414]]
[[ 94.89994812]
[ 73.20010376]]
561.5
[[ 577.50024414]]
[[ 94.74994659]
[ 73.00010681]]
560.25
[[ 576.25024414]]
[[ 94.59994507]
[ 72.80010986]]
559.0
[[ 575.00024414]]
[[ 94.44994354]
[ 72.60011292]]
557.75
[[ 573.75024414]]
[[ 94.29994202]
[ 72.40011597]]
556.5
[[ 572.50030518]]
[[ 94.14994049]
[ 72.20011902]]
555.25
[[ 571.25030518]]
[[ 93.99993896]
[ 72.00012207]]
554.0
[[ 570.00030518]]
[[ 93.84993744]
[ 71.80012512]]
552.75
[[ 568.75030518]]
[[ 93.69993591]
[ 71.60012817]]
551.5
[[ 567.50030518]]
[[ 93.54993439]
[ 71.40013123]]
550.25
[[ 566.25036621]]
[[ 93.39993286]
[ 71.20013428]]
549.0
[[ 565.00036621]]
[[ 93.24993134]
[ 71.00013733]]
547.75
[[ 563.75036621]]
[[ 93.09992981]
[ 70.80014038]]
546.5
[[ 562.50036621]]
[[ 92.94992828]
[ 70.60014343]]
545.25
[[ 561.25036621]]
[[ 92.79992676]
[ 70.40014648]]
544.0
[[ 560.00036621]]
[[ 92.64992523]
[ 70.20014954]]
542.75
[[ 558.75036621]]
[[ 92.49992371]
[ 70.00015259]]
541.5
[[ 557.50036621]]
[[ 92.34992218]
[ 69.80015564]]
540.25
[[ 556.25036621]]
[[ 92.19992065]
[ 69.60015869]]
539.0
[[ 555.00036621]]
[[ 92.04991913]
[ 69.40016174]]
537.75
[[ 553.75036621]]
[[ 91.8999176 ]
[ 69.20016479]]
536.5
[[ 552.50042725]]
[[ 91.74991608]
[ 69.00016785]]
535.25
[[ 551.25042725]]
[[ 91.59991455]
[ 68.8001709 ]]
534.0
[[ 550.00042725]]
[[ 91.44991302]
[ 68.60017395]]
532.75
[[ 548.75042725]]
[[ 91.2999115]
[ 68.400177 ]]
531.5
[[ 547.50042725]]
[[ 91.14990997]
[ 68.20018005]]
530.25
[[ 546.25048828]]
[[ 90.99990845]
[ 68.00018311]]
529.0
[[ 545.00048828]]
[[ 90.84990692]
[ 67.80018616]]
527.75
[[ 543.75048828]]
[[ 90.6999054 ]
[ 67.60018921]]
526.5
[[ 542.50048828]]
[[ 90.54990387]
[ 67.40019226]]
525.25
[[ 541.25048828]]
[[ 90.39990234]
[ 67.20019531]]
524.0
[[ 540.00048828]]
[[ 90.24990082]
[ 67.00019836]]
522.75
[[ 538.75048828]]
[[ 90.09989929]
[ 66.80020142]]
521.5
[[ 537.50048828]]
[[ 89.94989777]
[ 66.60020447]]
520.25
[[ 536.25048828]]
[[ 89.79989624]
[ 66.40020752]]
519.0
[[ 535.00048828]]
[[ 89.64989471]
[ 66.20021057]]
517.75
[[ 533.75048828]]
[[ 89.49989319]
[ 66.00021362]]
516.501
[[ 532.50054932]]
[[ 89.34989166]
[ 65.80021667]]
515.251
[[ 531.25054932]]
[[ 89.19989014]
[ 65.60021973]]
514.001
[[ 530.00054932]]
[[ 89.04988861]
[ 65.40022278]]
512.751
[[ 528.75054932]]
[[ 88.89988708]
[ 65.20022583]]
511.501
[[ 527.50054932]]
[[ 88.74988556]
[ 65.00022888]]
510.251
[[ 526.25061035]]
[[ 88.59988403]
[ 64.80023193]]
509.001
[[ 525.00061035]]
[[ 88.44988251]
[ 64.60023499]]
507.751
[[ 523.75061035]]
[[ 88.29988098]
[ 64.40023804]]
506.501
[[ 522.50061035]]
[[ 88.14987946]
[ 64.20024109]]
505.251
[[ 521.25061035]]
[[ 87.99987793]
[ 64.00024414]]
504.001
[[ 520.00061035]]
[[ 87.8498764 ]
[ 63.80024338]]
502.751
[[ 518.75061035]]
[[ 87.69987488]
[ 63.60024261]]
501.501
[[ 517.50061035]]
[[ 87.54987335]
[ 63.40024185]]
500.251
[[ 516.25061035]]
[[ 87.39987183]
[ 63.20024109]]
499.001
[[ 515.00061035]]
[[ 87.2498703 ]
[ 63.00024033]]
497.751
[[ 513.75054932]]
[[ 87.09986877]
[ 62.80023956]]
496.501
[[ 512.50061035]]
[[ 86.94986725]
[ 62.6002388 ]]
495.251
[[ 511.25054932]]
[[ 86.79986572]
[ 62.40023804]]
494.001
[[ 510.00054932]]
[[ 86.6498642 ]
[ 62.20023727]]
492.751
[[ 508.75054932]]
[[ 86.49986267]
[ 62.00023651]]
491.501
[[ 507.5005188]]
[[ 86.34986115]
[ 61.80023575]]
490.251
[[ 506.25054932]]
[[ 86.19985962]
[ 61.60023499]]
489.001
[[ 505.0005188]]
[[ 86.04985809]
[ 61.40023422]]
487.75
[[ 503.75048828]]
[[ 85.89985657]
[ 61.20023346]]
486.501
[[ 502.5005188]]
[[ 85.74985504]
[ 61.0002327 ]]
485.25
[[ 501.25048828]]
[[ 85.59985352]
[ 60.80023193]]
484.0
[[ 500.00048828]]
[[ 85.44985199]
[ 60.60023117]]
482.75
[[ 498.75048828]]
[[ 85.29985046]
[ 60.40023041]]
481.5
[[ 497.50048828]]
[[ 85.14984894]
[ 60.20022964]]
480.25
[[ 496.25048828]]
[[ 84.99984741]
[ 60.00022888]]
479.0
[[ 495.00045776]]
[[ 84.84984589]
[ 59.80022812]]
477.75
[[ 493.75042725]]
[[ 84.69984436]
[ 59.60022736]]
476.5
[[ 492.50042725]]
[[ 84.54984283]
[ 59.40022659]]
475.25
[[ 491.25042725]]
[[ 84.39984131]
[ 59.20022583]]
474.0
[[ 490.00042725]]
[[ 84.24983978]
[ 59.00022507]]
472.75
[[ 488.75042725]]
[[ 84.09983826]
[ 58.8002243 ]]
471.5
[[ 487.50042725]]
[[ 83.94983673]
[ 58.60022354]]
470.25
[[ 486.25042725]]
[[ 83.79983521]
[ 58.40022278]]
469.0
[[ 485.00039673]]
[[ 83.64983368]
[ 58.20022202]]
467.75
[[ 483.75036621]]
[[ 83.49983215]
[ 58.00022125]]
466.5
[[ 482.50036621]]
[[ 83.34983063]
[ 57.80022049]]
465.25
[[ 481.25036621]]
[[ 83.1998291 ]
[ 57.60021973]]
464.0
[[ 480.00036621]]
[[ 83.04982758]
[ 57.40021896]]
462.75
[[ 478.75036621]]
[[ 82.89982605]
[ 57.2002182 ]]
461.5
[[ 477.50036621]]
[[ 82.74982452]
[ 57.00021744]]
460.25
[[ 476.25036621]]
[[ 82.599823 ]
[ 56.80021667]]
459.0
[[ 475.00033569]]
[[ 82.44982147]
[ 56.60021591]]
457.75
[[ 473.75030518]]
[[ 82.29981995]
[ 56.40021515]]
456.5
[[ 472.50030518]]
[[ 82.14981842]
[ 56.20021439]]
455.25
[[ 471.25030518]]
[[ 81.99981689]
[ 56.00021362]]
454.0
[[ 470.00030518]]
[[ 81.84981537]
[ 55.80021286]]
452.75
[[ 468.75030518]]
[[ 81.69981384]
[ 55.6002121 ]]
451.5
[[ 467.50030518]]
[[ 81.54981232]
[ 55.40021133]]
450.25
[[ 466.25030518]]
[[ 81.39981079]
[ 55.20021057]]
449.0
[[ 465.00027466]]
[[ 81.24980927]
[ 55.00020981]]
447.75
[[ 463.75024414]]
[[ 81.09980774]
[ 54.80020905]]
446.5
[[ 462.50024414]]
[[ 80.94980621]
[ 54.60020828]]
445.25
[[ 461.25024414]]
[[ 80.79980469]
[ 54.40020752]]
444.0
[[ 460.00024414]]
[[ 80.64980316]
[ 54.20020676]]
442.75
[[ 458.75024414]]
[[ 80.49980164]
[ 54.00020599]]
441.5
[[ 457.50024414]]
[[ 80.34980011]
[ 53.80020523]]
440.25
[[ 456.25024414]]
[[ 80.19979858]
[ 53.60020447]]
439.0
[[ 455.00021362]]
[[ 80.04979706]
[ 53.4002037 ]]
437.75
[[ 453.75018311]]
[[ 79.89979553]
[ 53.20020294]]
436.5
[[ 452.50018311]]
[[ 79.74979401]
[ 53.00020218]]
435.25
[[ 451.25018311]]
[[ 79.59979248]
[ 52.80020142]]
434.0
[[ 450.00018311]]
[[ 79.44979095]
[ 52.60020065]]
432.75
[[ 448.75018311]]
[[ 79.29978943]
[ 52.40019989]]
431.5
[[ 447.50018311]]
[[ 79.1497879 ]
[ 52.20019913]]
430.25
[[ 446.25018311]]
[[ 78.99978638]
[ 52.00019836]]
429.0
[[ 445.00015259]]
[[ 78.84978485]
[ 51.8001976 ]]
427.75
[[ 443.75012207]]
[[ 78.69978333]
[ 51.60019684]]
426.5
[[ 442.50012207]]
[[ 78.5497818 ]
[ 51.40019608]]
425.25
[[ 441.25012207]]
[[ 78.39978027]
[ 51.20019531]]
424.0
[[ 440.00012207]]
[[ 78.24977875]
[ 51.00019455]]
422.75
[[ 438.75012207]]
[[ 78.09977722]
[ 50.80019379]]
421.5
[[ 437.50012207]]
[[ 77.9497757 ]
[ 50.60019302]]
420.25
[[ 436.25012207]]
[[ 77.79977417]
[ 50.40019226]]
419.0
[[ 435.00009155]]
[[ 77.64977264]
[ 50.2001915 ]]
417.75
[[ 433.75006104]]
[[ 77.49977112]
[ 50.00019073]]
416.5
[[ 432.50006104]]
[[ 77.34976959]
[ 49.80018997]]
415.25
[[ 431.25006104]]
[[ 77.19976807]
[ 49.60018921]]
414.0
[[ 430.00006104]]
[[ 77.04976654]
[ 49.40018845]]
412.75
[[ 428.75006104]]
[[ 76.89976501]
[ 49.20018768]]
411.5
[[ 427.50006104]]
[[ 76.74976349]
[ 49.00018692]]
410.25
[[ 426.25006104]]
[[ 76.59976196]
[ 48.80018616]]
409.0
[[ 425.00003052]]
[[ 76.44976044]
[ 48.60018539]]
407.75
[[ 423.75]]
[[ 76.29975891]
[ 48.40018463]]
406.5
[[ 422.5]]
[[ 76.14975739]
[ 48.20018387]]
405.25
[[ 421.25]]
[[ 75.99975586]
[ 48.00018311]]
404.0
[[ 420.]]
[[ 75.84975433]
[ 47.80018234]]
402.75
[[ 418.75]]
[[ 75.69975281]
[ 47.60018158]]
401.5
[[ 417.5]]
[[ 75.54975128]
[ 47.40018082]]
400.25
[[ 416.25]]
[[ 75.39974976]
[ 47.20018005]]
399.0
[[ 414.99996948]]
[[ 75.24974823]
[ 47.00017929]]
397.75
[[ 413.74993896]]
[[ 75.0997467 ]
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396.5
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