Python theano.Apply方法代码示例

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def make_node(self, acts, labels, input_lengths):
    # Unless specified, assume all sequences have full sequence length, i.e. acts_.shape[0]
    if input_lengths == None:
      input_lengths = T.cast(acts.shape[0], dtype="int32") * T.ones_like(acts[0,:,0], dtype=np.int32)

    # acts.shape = [seqLen, batchN, outputUnit]
    if acts.dtype != "float32":
      raise Exception("acts must be float32 instead of %s" % acts.dtype)
    # labels.shape = [batchN, labelLen]
    if labels.dtype != "int32":
      raise Exception("labels must be int32 instead of %s" % labels.dtype)
    # input_lengths.shape = [batchN]
    if input_lengths.dtype != "int32":
      raise Exception("input_lengths must be int32 instead of %s" % input_lengths.dtype)

    applyNode = theano.Apply(self, inputs=[acts, input_lengths, labels], outputs=[self.costs, self.gradients])

    # Return only the cost. Gradient will be returned by grad()
    self.default_output = 0 

    return applyNode 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def make_node(self, img, topgrad, shape=None):
        img = as_tensor_variable(img)
        topgrad = as_tensor_variable(topgrad)
        if img.type.ndim != 4:
            raise TypeError('img must be 4D tensor')
        if topgrad.type.ndim != 4:
            raise TypeError('topgrad must be 4D tensor')
        if self.subsample != (1, 1) or self.border_mode == "half":
            if shape is None:
                raise ValueError('shape must be given if subsample != (1, 1)'
                                 ' or border_mode == "half"')
            height_width = [as_tensor_variable(shape[0]).astype('int64'), as_tensor_variable(shape[1]).astype('int64')]
        else:
            height_width = []

        broadcastable = [topgrad.type.broadcastable[1], img.type.broadcastable[1],
                         False, False]
        dtype = img.type.dtype
        return Apply(self, [img, topgrad] + height_width,
                     [TensorType(dtype, broadcastable)()]) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def make_node(self, a, val, offset):
        a = tensor.as_tensor_variable(a)
        val = tensor.as_tensor_variable(val)
        offset = tensor.as_tensor_variable(offset)
        if a.ndim != 2:
            raise TypeError('%s: first parameter must have exactly'
                            ' two dimensions' % self.__class__.__name__)
        elif val.ndim != 0:
            raise TypeError('%s: second parameter must be a scalar'
                            % self.__class__.__name__)
        elif offset.ndim != 0:
            raise TypeError('%s: third parameter must be a scalar'
                            % self.__class__.__name__)
        val = tensor.cast(val, dtype=scalar.upcast(a.dtype, val.dtype))
        if val.dtype != a.dtype:
            raise TypeError('%s: type of second parameter must be the same'
                            ' as the first\'s' % self.__class__.__name__)
        elif offset.dtype[:3] != 'int':
            raise TypeError('%s: type of third parameter must be as integer'
                            ' use theano.tensor.cast( input, \'int32/int64\')'
                            % self.__class__.__name__)

        return gof.Apply(self, [a, val, offset], [a.type()]) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def c_init_code_struct(self, node, name, sub):
        """
        Optional: return a code string specific to the apply
        to be inserted in the struct initialization code.

        Parameters
        ----------
        node : an Apply instance in the graph being compiled
        name : str
            A unique name to distinguish variables from those of other nodes.
        sub
            A dictionary of values to substitute in the code.
            Most notably it contains a 'fail' entry that you should place in
            your code after setting a python exception to indicate an error.

        Raises
        ------
        MethodNotDefined
            The subclass does not override this method.

        """
        raise utils.MethodNotDefined("c_init_code_apply", type(self),
                                     self.__class__.__name__) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def c_support_code_struct(self, node, name):
        """
        Optional: return utility code for use by an `Op` that will be
        inserted at struct scope, that can be specialized for the
        support of a particular `Apply` node.

        Parameters
        ----------
        node : an Apply instance in the graph being compiled
        name : str
            A unique name to distinguish you variables from those of other
            nodes.

        Raises
        ------
        MethodNotDefined
            Subclass does not implement this method.

        """
        raise utils.MethodNotDefined("c_support_code_struct",
                                     type(self), self.__class__.__name__) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def c_cleanup_code_struct(self, node, name):
        """
        Optional: return a code string specific to the apply to be
        inserted in the struct cleanup code.

        Parameters
        ----------
        node : an Apply instance in the graph being compiled
        name : str
            A unique name to distinguish variables from those of other nodes.

        Raises
        ------
        MethodNotDefined
            The subclass does not override this method.

        """
        raise utils.MethodNotDefined("c_cleanup_code_struct", type(self),
                                     self.__class__.__name__) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def make_node(self, *inputs):

        if not hasattr(self, 'itypes'):
            raise NotImplementedError("You can either define itypes and otypes,\
             or implement make_node")

        if not hasattr(self, 'otypes'):
            raise NotImplementedError("You can either define itypes and otypes,\
             or implement make_node")

        if len(inputs) != len(self.itypes):
            raise ValueError("We expected %d inputs but got %d." %
                             (len(self.itypes), len(inputs)))
        if not all(inp.type == it for inp, it in zip(inputs, self.itypes)):
            raise TypeError(
                "We expected inputs of types '%s' but got types '%s' " %
                (str([inp.type for inp in inputs]), str(self.itypes)))
        return theano.Apply(self, inputs, [o() for o in self.otypes]) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def make_node(self, pvals, unis):
        assert pvals.dtype == 'float32'
        assert unis.dtype == 'float32'
        if not isinstance(pvals.type, CudaNdarrayType):
            raise TypeError('pvals must be cudandarray', pvals)
        if not isinstance(unis.type, CudaNdarrayType):
            raise TypeError('unis must be cudandarray', unis)
        if self.odtype == 'auto':
            odtype = pvals.dtype
        else:
            odtype = self.odtype
        if odtype != pvals.dtype:
            raise NotImplementedError(
                'GpuMultinomialFromUniform works only if '
                'self.odtype == pvals.dtype', odtype, pvals.dtype)
        br = (pvals.broadcastable[1], pvals.broadcastable[0])
        out = CudaNdarrayType(broadcastable=br)()
        return Apply(self, [pvals, unis], [out]) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def make_node(self, img, topgrad, shape=None):
        img = as_cuda_ndarray_variable(img)
        topgrad = as_cuda_ndarray_variable(topgrad)
        if img.type.ndim != 4:
            raise TypeError('img must be 4D tensor')
        if topgrad.type.ndim != 4:
            raise TypeError('topgrad must be 4D tensor')
        if self.subsample != (1, 1) or self.border_mode == "half":
            if shape is None:
                raise ValueError('shape must be given if subsample != (1, 1)'
                                 ' or border_mode == "half"')
            height_width = [shape[0], shape[1]]
            assert shape[0].ndim == 0
            assert shape[1].ndim == 0
        else:
            height_width = []

        broadcastable = [topgrad.type.broadcastable[1], img.type.broadcastable[1],
                         False, False]
        return Apply(self, [img, topgrad] + height_width, [CudaNdarrayType(broadcastable)()]) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def make_node(self, img_shape, kern_shape):
        if img_shape.type.ndim != 1 or img_shape.type.dtype != 'int64':
            raise TypeError('img must be 1D shape tensor')
        if kern_shape.type.ndim != 1 or kern_shape.type.dtype != 'int64':
            raise TypeError('kern must be 1D shape tensor')

        node = Apply(self, [img_shape, kern_shape],
                     [CDataType("cudnnConvolutionDescriptor_t",
                                freefunc="cudnnDestroyConvolutionDescriptor")()])
        # DebugMode cannot compare the values of CDataType variables, so by
        # default it returns False all the time. To prevent DebugMode from
        # complaining because of the MergeOptimizer, we make this variable
        # always compare to True.
        out = node.outputs[0]
        out.tag.values_eq_approx = tensor.type.values_eq_approx_always_true
        return node 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def make_node(self, *inputs):
        inputs = tuple(map(T.as_tensor_variable, inputs))
        output = T.tensor4(dtype=(self.output_dtype or inputs[0].dtype))
        return Apply(self, inputs, (output,)) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def make_node(self, input):
        output = T.as_tensor_variable(input).type()
        return theano.Apply(self, (input,), (output,)) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def make_node(self, V, d, WShape, dCdH):
        V_ = T.as_tensor_variable(V)
        d_ = T.as_tensor_variable(d)
        WShape_ = T.as_tensor_variable(WShape)
        dCdH_ = T.as_tensor_variable(dCdH)

        return theano.Apply(self,
                            inputs=[V_, d_, WShape_, dCdH_],
                            outputs=[T.TensorType(
                                V_.dtype,
                                (False, False, False, False, False))()]) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def make_node(self, W, b, d, H, RShape=None):
        """
        Parameters
        ----------
        W
            Weights, filter
        b
            Bias, shape == (W.shape[0],).
        d
            Strides when moving the filter over the input.
        H
            The output of Conv3D.

        """
        W_ = T.as_tensor_variable(W)
        b_ = T.as_tensor_variable(b)
        d_ = T.as_tensor_variable(d)
        H_ = T.as_tensor_variable(H)
        if RShape:
            RShape_ = T.as_tensor_variable(RShape)
        else:
            RShape_ = T.as_tensor_variable([-1, -1, -1])

        return theano.Apply(self,
                            inputs=[W_, b_, d_, H_, RShape_],
                            outputs=[T.TensorType(H_.dtype,
                                     (False, False, False, False, False))()]) 

# 需要导入模块: import theano [as 别名]
# 或者: from theano import Apply [as 别名]
def make_node(self, x):
        if x.type.ndim != 4:
            raise TypeError()
        # TODO: consider restricting the dtype?
        x = tensor.as_tensor_variable(x)
        # If the input shape are broadcastable we can have 0 in the output shape
        broad = x.broadcastable[:2] + (False, False)
        out = tensor.TensorType(x.dtype, broad)
        return gof.Apply(self, [x], [out()])