Example #1
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File: model.py Project: lxastro/dlx 
    def weighted(y_true, y_pred, weights, mask=None):
        # score_array has ndim >= 2
        score_array = fn(y_true, y_pred)
        if mask is not None:
            # Cast the mask to floatX to avoid float64 upcasting in theano
            mask = K.cast(mask, K.floatX)
            # mask should have the same shape as score_array
            score_array *= mask
            #  the loss per batch should be proportional
            #  to the number of unmasked samples.
            score_array /= K.mean(mask)

        # reduce score_array to 1D
        ndim = K.ndim(score_array)
        for _ in range(ndim-1):
            score_array = K.mean(score_array, axis=-1)

        if weights is not None:
            score_array *= weights
        return K.mean(score_array)
Example #2
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    def weighted(y_true, y_pred, weights, mask=None):
        # score_array has ndim >= 2
        score_array = fn(y_true, y_pred)
        if mask is not None:
            # Cast the mask to floatX to avoid float64 upcasting in theano
            mask = K.cast(mask, K.floatX)
            # mask should have the same shape as score_array
            score_array *= mask
            #  the loss per batch should be proportional
            #  to the number of unmasked samples.
            score_array /= K.mean(mask)

        # reduce score_array to 1D
        ndim = K.ndim(score_array)
        for _ in range(ndim - 1):
            score_array = K.mean(score_array, axis=-1)

        if weights is not None:
            score_array *= weights
        return K.mean(score_array)
Example #3
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File: core.py Project: lxastro/dlx 
 def output(self, train=False):
     X = self.get_input('input')(train)
     if self.mode == 'ave':
         s = K.mean(X, axis=1)
         return s
     if self.mode == 'sum':
         s = K.sum(X, axis=1)
         return s
     elif self.mode == 'mul':
         s = K.prod(X, axis=1)
         return s
     else:
         raise Exception('Unknown merge mode')
Example #4
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 def output(self, train=False):
     X = self.get_input('input')(train)
     if self.mode == 'ave':
         s = K.mean(X, axis=1)
         return s
     if self.mode == 'sum':
         s = K.sum(X, axis=1)
         return s
     elif self.mode == 'mul':
         s = K.prod(X, axis=1)
         return s
     else:
         raise Exception('Unknown merge mode')
Example #5
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def mean_absolute_percentage_error(y_true, y_pred):
    diff = K.abs(
        (y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), np.inf))
    return 100. * K.mean(diff, axis=-1)
Example #6
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def mean_absolute_error(y_true, y_pred):
    return K.mean(K.abs(y_pred - y_true), axis=-1)
Example #7
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def cosine_proximity(y_true, y_pred):
    assert K.ndim(y_true) == 2
    assert K.ndim(y_pred) == 2
    y_true = K.l2_normalize(y_true, axis=1)
    y_pred = K.l2_normalize(y_pred, axis=1)
    return -K.mean(y_true * y_pred, axis=1)
Example #8
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def binary_crossentropy(y_true, y_pred):
    return K.mean(K.binary_crossentropy(y_pred, y_true), axis=-1)
Example #9
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def hinge(y_true, y_pred):
    return K.mean(K.maximum(1. - y_true * y_pred, 0.), axis=-1)
Example #10
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def mean_categorical_crossentropy(y_true, y_pred):
    '''Expects a binary class matrix instead of a vector of scalar classes.
    '''
    return K.mean(K.categorical_crossentropy(y_pred, y_true), axis=-1)
Example #11
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def hinge(y_true, y_pred):
    return K.mean(K.maximum(1. - y_true * y_pred, 0.), axis=-1)
Example #12
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def squared_hinge(y_true, y_pred):
    return K.mean(K.square(K.maximum(1. - y_true * y_pred, 0.)), axis=-1)
Example #13
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def mean_squared_logarithmic_error(y_true, y_pred):
    first_log = K.log(K.clip(y_pred, K.epsilon(), np.inf) + 1.)
    second_log = K.log(K.clip(y_true, K.epsilon(), np.inf) + 1.)
    return K.mean(K.square(first_log - second_log), axis=-1)
Example #14
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def mean_absolute_percentage_error(y_true, y_pred):
    diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true), K.epsilon(), np.inf))
    return 100. * K.mean(diff, axis=-1)
Example #15
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def mean_absolute_error(y_true, y_pred):
    return K.mean(K.abs(y_pred - y_true), axis=-1)
Example #16
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def mean_squared_logarithmic_error(y_true, y_pred):
    first_log = K.log(K.clip(y_pred, K.epsilon(), np.inf) + 1.)
    second_log = K.log(K.clip(y_true, K.epsilon(), np.inf) + 1.)
    return K.mean(K.square(first_log - second_log), axis=-1)
Example #17
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def squared_hinge(y_true, y_pred):
    return K.mean(K.square(K.maximum(1. - y_true * y_pred, 0.)), axis=-1)
Example #18
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def binary_crossentropy(y_true, y_pred):
    return K.mean(K.binary_crossentropy(y_pred, y_true), axis=-1)
Example #19
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def mean_categorical_crossentropy(y_true, y_pred):
    '''Expects a binary class matrix instead of a vector of scalar classes.
    '''
    return K.mean(K.categorical_crossentropy(y_pred, y_true), axis=-1)
Example #20
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def poisson(y_true, y_pred):
    return K.mean(y_pred - y_true * K.log(y_pred + K.epsilon()), axis=-1)
Example #21
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def poisson(y_true, y_pred):
    return K.mean(y_pred - y_true * K.log(y_pred + K.epsilon()), axis=-1)
Example #22
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def cosine_proximity(y_true, y_pred):
    assert K.ndim(y_true) == 2
    assert K.ndim(y_pred) == 2
    y_true = K.l2_normalize(y_true, axis=1)
    y_pred = K.l2_normalize(y_pred, axis=1)
    return -K.mean(y_true * y_pred, axis=1)
Example #23
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def root_mean_squared_error(y_true, y_pred):
    return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))
Example #24
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def root_mean_squared_error(y_true, y_pred):
    return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))