Python cntk.abs() Examples

The following are 30 code examples of cntk.abs(). 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. You may also want to check out all available functions/classes of the module cntk , or try the search function .
Example #1
Source File: cntk_smoothL1_loss.py    From cntk-python-web-service-on-azure with MIT License 6 votes vote down vote up 
def SmoothL1Loss(sigma, bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights):
    """
        From https://github.com/smallcorgi/Faster-RCNN_TF/blob/master/lib/fast_rcnn/train.py

        ResultLoss = outside_weights * SmoothL1(inside_weights * (bbox_pred - bbox_targets))
        SmoothL1(x) = 0.5 * (sigma * x)^2,    if |x| < 1 / sigma^2
                        |x| - 0.5 / sigma^2,    otherwise
    """
    sigma2 = sigma * sigma

    inside_mul_abs = C.abs(C.element_times(bbox_inside_weights, C.minus(bbox_pred, bbox_targets)))

    smooth_l1_sign = C.less(inside_mul_abs, 1.0 / sigma2)
    smooth_l1_option1 = C.element_times(C.element_times(inside_mul_abs, inside_mul_abs), 0.5 * sigma2)
    smooth_l1_option2 = C.minus(inside_mul_abs, 0.5 / sigma2)
    smooth_l1_result = C.plus(C.element_times(smooth_l1_option1, smooth_l1_sign),
                              C.element_times(smooth_l1_option2, C.minus(1.0, smooth_l1_sign)))

    return C.element_times(bbox_outside_weights, smooth_l1_result)
Example #2
Source File: cntk_smoothL1_loss.py    From raster-deep-learning with Apache License 2.0 6 votes vote down vote up 
def SmoothL1Loss(sigma, bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights):
    """
        From https://github.com/smallcorgi/Faster-RCNN_TF/blob/master/lib/fast_rcnn/train.py

        ResultLoss = outside_weights * SmoothL1(inside_weights * (bbox_pred - bbox_targets))
        SmoothL1(x) = 0.5 * (sigma * x)^2,    if |x| < 1 / sigma^2
                        |x| - 0.5 / sigma^2,    otherwise
    """
    sigma2 = sigma * sigma

    inside_mul_abs = C.abs(C.element_times(bbox_inside_weights, C.minus(bbox_pred, bbox_targets)))

    smooth_l1_sign = C.less(inside_mul_abs, 1.0 / sigma2)
    smooth_l1_option1 = C.element_times(C.element_times(inside_mul_abs, inside_mul_abs), 0.5 * sigma2)
    smooth_l1_option2 = C.minus(inside_mul_abs, 0.5 / sigma2)
    smooth_l1_result = C.plus(C.element_times(smooth_l1_option1, smooth_l1_sign),
                              C.element_times(smooth_l1_option2, C.minus(1.0, smooth_l1_sign)))

    return C.element_times(bbox_outside_weights, smooth_l1_result)
Example #3
Source File: cntk_smoothL1_loss.py    From cntk-hotel-pictures-classificator with MIT License 6 votes vote down vote up 
def SmoothL1Loss(sigma, bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights):
    """
        From https://github.com/smallcorgi/Faster-RCNN_TF/blob/master/lib/fast_rcnn/train.py

        ResultLoss = outside_weights * SmoothL1(inside_weights * (bbox_pred - bbox_targets))
        SmoothL1(x) = 0.5 * (sigma * x)^2,    if |x| < 1 / sigma^2
                        |x| - 0.5 / sigma^2,    otherwise
    """
    sigma2 = sigma * sigma

    inside_mul_abs = C.abs(C.element_times(bbox_inside_weights, C.minus(bbox_pred, bbox_targets)))

    smooth_l1_sign = C.less(inside_mul_abs, 1.0 / sigma2)
    smooth_l1_option1 = C.element_times(C.element_times(inside_mul_abs, inside_mul_abs), 0.5 * sigma2)
    smooth_l1_option2 = C.minus(inside_mul_abs, 0.5 / sigma2)
    smooth_l1_result = C.plus(C.element_times(smooth_l1_option1, smooth_l1_sign),
                              C.element_times(smooth_l1_option2, C.minus(1.0, smooth_l1_sign)))

    return C.element_times(bbox_outside_weights, smooth_l1_result)
Example #4
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def sign(x):
    return x / C.abs(x)
Example #5
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def softsign(x):
    return x / (1 + C.abs(x))
Example #6
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def abs(x):
    return C.abs(x)
Example #7
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def sign(x):
    return x / C.abs(x)
Example #8
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def abs(x):
    return C.abs(x)
Example #9
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def sign(x):
    return x / C.abs(x)
Example #10
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def softsign(x):
    return x / (1 + C.abs(x))
Example #11
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def abs(x):
    return C.abs(x)
Example #12
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def abs(x):
    return C.abs(x)
Example #13
Source File: cntk_backend.py    From deepQuest with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def abs(x):
    return C.abs(x)
Example #14
Source File: cntk_backend.py    From deepQuest with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def sign(x):
    return x / C.abs(x)
Example #15
Source File: cntk_backend.py    From deepQuest with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def softsign(x):
    return x / (1 + C.abs(x))
Example #16
Source File: cntk_backend.py    From keras-lambda with MIT License 5 votes vote down vote up 
def abs(x):
    return C.abs(x)
Example #17
Source File: cntk_backend.py    From keras-lambda with MIT License 5 votes vote down vote up 
def sign(x):
    return x / C.abs(x)
Example #18
Source File: cntk_backend.py    From keras-lambda with MIT License 5 votes vote down vote up 
def softsign(x):
    return x / (1 + C.abs(x))
Example #19
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def sign(x):
    return x / C.abs(x)
Example #20
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def sign(x):
    return x / C.abs(x)
Example #21
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def abs(x):
    return C.abs(x)
Example #22
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def softsign(x):
    return x / (1 + C.abs(x))
Example #23
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def sign(x):
    return x / C.abs(x)
Example #24
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def abs(x):
    return C.abs(x)
Example #25
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def sign(x):
    return x / C.abs(x)
Example #26
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def abs(x):
    return C.abs(x)
Example #27
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def softsign(x):
    return x / (1 + C.abs(x))
Example #28
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def sign(x):
    return x / C.abs(x)
Example #29
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def abs(x):
    return C.abs(x)
Example #30
Source File: train_end2end.py    From end2end_AU_speech with MIT License 5 votes vote down vote up 
def l1_reg_loss(output):
    # don't need C.abs(output), because output is already non-negative
    # use abs() if your desired output could be negative
    return C.reduce_mean(output)


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# create computational graph and learner
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