Python theano.tensor.any() Examples

The following are 30 code examples of theano.tensor.any(). 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 theano.tensor , or try the search function .
Example #1
Source File: theano_backend.py    From Att-ChemdNER with Apache License 2.0 6 votes vote down vote up 
def in_top_k(predictions, targets, k):
    '''Returns whether the `targets` are in the top `k` `predictions`

    # Arguments
        predictions: A tensor of shape batch_size x classess and type float32.
        targets: A tensor of shape batch_size and type int32 or int64.
        k: An int, number of top elements to consider.

    # Returns
        A tensor of shape batch_size and type int. output_i is 1 if
        targets_i is within top-k values of predictions_i
    '''
    predictions_top_k = T.argsort(predictions)[:, -k:]
    result, _ = theano.map(lambda prediction, target: any(equal(prediction, target)), sequences=[predictions_top_k, targets])
    return result


# CONVOLUTIONS
Example #2
Source File: theano_backend.py    From deepQuest with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def stop_gradient(variables):
    """Returns `variables` but with zero gradient w.r.t. every other variable.

    # Arguments
        variables: tensor or list of tensors to consider constant with respect
            to any other variable.

    # Returns
        A single tensor or a list of tensors (depending on the passed argument)
            that has constant gradient with respect to any other variable.
    """
    if isinstance(variables, (list, tuple)):
        return map(theano.gradient.disconnected_grad, variables)
    else:
        return theano.gradient.disconnected_grad(variables)


# CONTROL FLOW
Example #3
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def stop_gradient(variables):
    """Returns `variables` but with zero gradient w.r.t. every other variable.

    # Arguments
        variables: tensor or list of tensors to consider constant with respect
            to any other variable.

    # Returns
        A single tensor or a list of tensors (depending on the passed argument)
            that has constant gradient with respect to any other variable.
    """
    if isinstance(variables, (list, tuple)):
        return map(theano.gradient.disconnected_grad, variables)
    else:
        return theano.gradient.disconnected_grad(variables)


# CONTROL FLOW
Example #4
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def stop_gradient(variables):
    """Returns `variables` but with zero gradient w.r.t. every other variable.

    # Arguments
        variables: tensor or list of tensors to consider constant with respect
            to any other variable.

    # Returns
        A single tensor or a list of tensors (depending on the passed argument)
            that has constant gradient with respect to any other variable.
    """
    if isinstance(variables, (list, tuple)):
        return map(theano.gradient.disconnected_grad, variables)
    else:
        return theano.gradient.disconnected_grad(variables)


# CONTROL FLOW
Example #5
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def stop_gradient(variables):
    """Returns `variables` but with zero gradient w.r.t. every other variable.

    # Arguments
        variables: tensor or list of tensors to consider constant with respect
            to any other variable.

    # Returns
        A single tensor or a list of tensors (depending on the passed argument)
            that has constant gradient with respect to any other variable.
    """
    if isinstance(variables, (list, tuple)):
        return map(theano.gradient.disconnected_grad, variables)
    else:
        return theano.gradient.disconnected_grad(variables)


# CONTROL FLOW
Example #6
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def stop_gradient(variables):
    """Returns `variables` but with zero gradient w.r.t. every other variable.

    # Arguments
        variables: tensor or list of tensors to consider constant with respect
            to any other variable.

    # Returns
        A single tensor or a list of tensors (depending on the passed argument)
            that has constant gradient with respect to any other variable.
    """
    if isinstance(variables, (list, tuple)):
        return map(theano.gradient.disconnected_grad, variables)
    else:
        return theano.gradient.disconnected_grad(variables)


# CONTROL FLOW
Example #7
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def stop_gradient(variables):
    """Returns `variables` but with zero gradient w.r.t. every other variable.

    # Arguments
        variables: tensor or list of tensors to consider constant with respect
            to any other variable.

    # Returns
        A single tensor or a list of tensors (depending on the passed argument)
            that has constant gradient with respect to any other variable.
    """
    if isinstance(variables, (list, tuple)):
        return map(theano.gradient.disconnected_grad, variables)
    else:
        return theano.gradient.disconnected_grad(variables)


# CONTROL FLOW
Example #8
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def stop_gradient(variables):
    """Returns `variables` but with zero gradient w.r.t. every other variable.

    # Arguments
        variables: tensor or list of tensors to consider constant with respect
            to any other variable.

    # Returns
        A single tensor or a list of tensors (depending on the passed argument)
            that has constant gradient with respect to any other variable.
    """
    if isinstance(variables, (list, tuple)):
        return map(theano.gradient.disconnected_grad, variables)
    else:
        return theano.gradient.disconnected_grad(variables)


# CONTROL FLOW
Example #9
Source File: test_elemwise.py    From attention-lvcsr with MIT License 6 votes vote down vote up 
def test_c(self):
        if not theano.config.cxx:
            raise SkipTest("G++ not available, so we need to skip this test.")

        for dtype in ["floatX", "complex64", "complex128", "int8", "uint8"]:
            self.with_linker(gof.CLinker(), scalar.add, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.mul, dtype=dtype)
        for dtype in ["floatX", "int8", "uint8"]:
            self.with_linker(gof.CLinker(), scalar.minimum, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.maximum, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.and_, dtype=dtype,
                             tensor_op=tensor.all)
            self.with_linker(gof.CLinker(), scalar.or_, dtype=dtype,
                             tensor_op=tensor.any)
        for dtype in ["int8", "uint8"]:
            self.with_linker(gof.CLinker(), scalar.or_, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.and_, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.xor, dtype=dtype)
Example #10
Source File: test_elemwise.py    From D-VAE with MIT License 6 votes vote down vote up 
def test_c(self):
        if not theano.config.cxx:
            raise SkipTest("G++ not available, so we need to skip this test.")

        for dtype in ["floatX", "complex64", "complex128", "int8", "uint8"]:
            self.with_linker(gof.CLinker(), scalar.add, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.mul, dtype=dtype)
        for dtype in ["floatX", "int8", "uint8"]:
            self.with_linker(gof.CLinker(), scalar.minimum, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.maximum, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.and_, dtype=dtype,
                             tensor_op=tensor.all)
            self.with_linker(gof.CLinker(), scalar.or_, dtype=dtype,
                             tensor_op=tensor.any)
        for dtype in ["int8", "uint8"]:
            self.with_linker(gof.CLinker(), scalar.or_, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.and_, dtype=dtype)
            self.with_linker(gof.CLinker(), scalar.xor, dtype=dtype)
Example #11
Source File: theano_backend.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up 
def stop_gradient(variables):
    """Returns `variables` but with zero gradient w.r.t. every other variable.

    # Arguments
        variables: tensor or list of tensors to consider constant with respect
            to any other variable.

    # Returns
        A single tensor or a list of tensors (depending on the passed argument)
            that has constant gradient with respect to any other variable.
    """
    if isinstance(variables, (list, tuple)):
        return map(theano.gradient.disconnected_grad, variables)
    else:
        return theano.gradient.disconnected_grad(variables)


# CONTROL FLOW
Example #12
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def any(x, axis=None, keepdims=False):
    """Bitwise reduction (logical OR).
    """
    return T.any(x, axis=axis, keepdims=keepdims)
Example #13
Source File: theano_backend.py    From keras-lambda with MIT License 5 votes vote down vote up 
def any(x, axis=None, keepdims=False):
    """Bitwise reduction (logical OR).
    """
    return T.any(x, axis=axis, keepdims=keepdims)
Example #14
Source File: theano_util.py    From cs-ranking with Apache License 2.0 5 votes vote down vote up 
def replace_nan_theano(p):
    if tt.any(tt.isnan(p)):
        p = tt.switch(tt.isnan(p), 1.0, p)
        p = tt.switch(tt.isinf(p), 1.0, p)
        p = normalize(p)
    return p
Example #15
Source File: theano_util.py    From cs-ranking with Apache License 2.0 5 votes vote down vote up 
def replace_inf_theano(x):
    if tt.any(tt.isinf(x)):
        x = tt.switch(tt.isinf(x), 2e300, x)
        x = tt.switch(tt.isnan(x), 2e300, x)
    return x
Example #16
Source File: theano_backend.py    From KerasNeuralFingerprint with MIT License 5 votes vote down vote up 
def any(x, axis=None, keepdims=False):
    '''Bitwise reduction (logical OR).
    '''
    return T.any(x, axis=axis, keepdims=keepdims)
Example #17
Source File: theano_backend.py    From Att-ChemdNER with Apache License 2.0 5 votes vote down vote up 
def any(x, axis=None, keepdims=False):
    '''Bitwise reduction (logical OR).
    '''
    return T.any(x, axis=axis, keepdims=keepdims)
Example #18
Source File: theano_backend.py    From deepQuest with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def any(x, axis=None, keepdims=False):
    """Bitwise reduction (logical OR).
    """
    return T.any(x, axis=axis, keepdims=keepdims)
Example #19
Source File: theano_backend.py    From reading-text-in-the-wild with GNU General Public License v3.0 5 votes vote down vote up 
def any(x, axis=None, keepdims=False):
    '''Bitwise reduction (logical OR).
    '''
    return T.any(x, axis=axis, keepdims=keepdims)
Example #20
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def any(x, axis=None, keepdims=False):
    """Bitwise reduction (logical OR).
    """
    return T.any(x, axis=axis, keepdims=keepdims)
Example #21
Source File: core.py    From CAPTCHA-breaking with MIT License 5 votes vote down vote up 
def get_output_mask(self, train=False):
        X = self.get_input(train)
        return T.any(T.ones_like(X) * (1. - T.eq(X, self.mask_value)), axis=-1)
Example #22
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def any(x, axis=None, keepdims=False):
    """Bitwise reduction (logical OR).
    """
    return T.any(x, axis=axis, keepdims=keepdims)
Example #23
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def any(x, axis=None, keepdims=False):
    """Bitwise reduction (logical OR).
    """
    return T.any(x, axis=axis, keepdims=keepdims)
Example #24
Source File: test_elemwise.py    From D-VAE with MIT License 5 votes vote down vote up 
def test_perform(self):
        for dtype in ["floatX", "complex64", "complex128", "int8", "uint8"]:
            self.with_linker(gof.PerformLinker(), scalar.add, dtype=dtype)
            self.with_linker(gof.PerformLinker(), scalar.mul, dtype=dtype)
            self.with_linker(gof.PerformLinker(), scalar.maximum, dtype=dtype)
            self.with_linker(gof.PerformLinker(), scalar.minimum, dtype=dtype)
            self.with_linker(gof.PerformLinker(), scalar.and_, dtype=dtype,
                             tensor_op=tensor.all)
            self.with_linker(gof.PerformLinker(), scalar.or_, dtype=dtype,
                             tensor_op=tensor.any)
        for dtype in ["int8", "uint8"]:
            self.with_linker(gof.PerformLinker(), scalar.or_, dtype=dtype)
            self.with_linker(gof.PerformLinker(), scalar.and_, dtype=dtype)
            self.with_linker(gof.PerformLinker(), scalar.xor, dtype=dtype)
Example #25
Source File: test_elemwise.py    From D-VAE with MIT License 5 votes vote down vote up 
def test_perform_nan(self):
        for dtype in ["floatX", "complex64", "complex128"]:
            self.with_linker(gof.PerformLinker(), scalar.add, dtype=dtype,
                             test_nan=True)
            self.with_linker(gof.PerformLinker(), scalar.mul, dtype=dtype,
                             test_nan=True)
            self.with_linker(gof.PerformLinker(), scalar.maximum, dtype=dtype,
                             test_nan=True)
            self.with_linker(gof.PerformLinker(), scalar.minimum, dtype=dtype,
                             test_nan=True)
            self.with_linker(gof.PerformLinker(), scalar.or_, dtype=dtype,
                             test_nan=True, tensor_op=tensor.any)
            self.with_linker(gof.PerformLinker(), scalar.and_, dtype=dtype,
                             test_nan=True, tensor_op=tensor.all)
Example #26
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def any(x, axis=None, keepdims=False):
    """Bitwise reduction (logical OR).
    """
    return T.any(x, axis=axis, keepdims=keepdims)
Example #27
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def any(x, axis=None, keepdims=False):
    """Bitwise reduction (logical OR).
    """
    return T.any(x, axis=axis, keepdims=keepdims)
Example #28
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def any(x, axis=None, keepdims=False):
    """Bitwise reduction (logical OR).
    """
    return T.any(x, axis=axis, keepdims=keepdims)
Example #29
Source File: test_elemwise.py    From D-VAE with MIT License 5 votes vote down vote up 
def test_any_grad(self):
        x = tensor.bmatrix('x')
        x_all = x.any()
        gx = theano.grad(x_all, x)
        f = theano.function([x], gx)
        x_random = self.rng.binomial(n=1, p=0.5, size=(5, 7)).astype('int8')
        for x_val in (x_random,
                      numpy.zeros_like(x_random),
                      numpy.ones_like(x_random)):
            gx_val = f(x_val)
            assert gx_val.shape == x_val.shape
            assert numpy.all(gx_val == 0)
Example #30
Source File: theano_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def any(x, axis=None, keepdims=False):
    """Bitwise reduction (logical OR).
    """
    return T.any(x, axis=axis, keepdims=keepdims)