return op.Output(0)
}
-// DepthwiseConv2dNativeAttr is an optional argument to DepthwiseConv2dNative.
-type DepthwiseConv2dNativeAttr func(optionalAttr)
-
-// DepthwiseConv2dNativeDataFormat sets the optional data_format attribute to value.
-//
-// value: Specify the data format of the input and output data. With the
-// default format "NHWC", the data is stored in the order of:
-// [batch, height, width, channels].
-// Alternatively, the format could be "NCHW", the data storage order of:
-// [batch, channels, height, width].
-// If not specified, defaults to "NHWC"
-func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr {
- return func(m optionalAttr) {
- m["data_format"] = value
- }
-}
-
-// DepthwiseConv2dNativeDilations sets the optional dilations attribute to value.
-//
-// value: 1-D tensor of length 4. The dilation factor for each dimension of
-// `input`. If set to k > 1, there will be k-1 skipped cells between each filter
-// element on that dimension. The dimension order is determined by the value of
-// `data_format`, see above for details. Dilations in the batch and depth
-// dimensions must be 1.
-// If not specified, defaults to <i:1 i:1 i:1 i:1 >
-func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr {
- return func(m optionalAttr) {
- m["dilations"] = value
- }
-}
-
-// Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
-//
-// Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
-// and a filter / kernel tensor of shape
-// `[filter_height, filter_width, in_channels, channel_multiplier]`, containing
-// `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies
-// a different filter to each input channel (expanding from 1 channel to
-// `channel_multiplier` channels for each), then concatenates the results
-// together. Thus, the output has `in_channels * channel_multiplier` channels.
-//
-// ```
-// for k in 0..in_channels-1
-// for q in 0..channel_multiplier-1
-// output[b, i, j, k * channel_multiplier + q] =
-// sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
-// filter[di, dj, k, q]
-// ```
-//
-// Must have `strides[0] = strides[3] = 1`. For the most common case of the same
-// horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
+// Table initializer that takes two tensors for keys and values respectively.
//
// Arguments:
+// table_handle: Handle to a table which will be initialized.
+// keys: Keys of type Tkey.
+// values: Values of type Tval.
//
-//
-// strides: 1-D of length 4. The stride of the sliding window for each dimension
-// of `input`.
-// padding: The type of padding algorithm to use.
-func DepthwiseConv2dNative(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeAttr) (output tf.Output) {
+// Returns the created operation.
+func InitializeTableV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) {
if scope.Err() != nil {
return
}
- attrs := map[string]interface{}{"strides": strides, "padding": padding}
- for _, a := range optional {
- a(attrs)
- }
opspec := tf.OpSpec{
- Type: "DepthwiseConv2dNative",
+ Type: "InitializeTableV2",
Input: []tf.Input{
- input, filter,
+ table_handle, keys, values,
},
- Attrs: attrs,
}
- op := scope.AddOperation(opspec)
- return op.Output(0)
+ return scope.AddOperation(opspec)
}
// DataFormatDimMapAttr is an optional argument to DataFormatDimMap.
return op.Output(0)
}
+// DepthwiseConv2dNativeAttr is an optional argument to DepthwiseConv2dNative.
+type DepthwiseConv2dNativeAttr func(optionalAttr)
+
+// DepthwiseConv2dNativeDataFormat sets the optional data_format attribute to value.
+//
+// value: Specify the data format of the input and output data. With the
+// default format "NHWC", the data is stored in the order of:
+// [batch, height, width, channels].
+// Alternatively, the format could be "NCHW", the data storage order of:
+// [batch, channels, height, width].
+// If not specified, defaults to "NHWC"
+func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr {
+ return func(m optionalAttr) {
+ m["data_format"] = value
+ }
+}
+
+// DepthwiseConv2dNativeDilations sets the optional dilations attribute to value.
+//
+// value: 1-D tensor of length 4. The dilation factor for each dimension of
+// `input`. If set to k > 1, there will be k-1 skipped cells between each filter
+// element on that dimension. The dimension order is determined by the value of
+// `data_format`, see above for details. Dilations in the batch and depth
+// dimensions must be 1.
+// If not specified, defaults to <i:1 i:1 i:1 i:1 >
+func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr {
+ return func(m optionalAttr) {
+ m["dilations"] = value
+ }
+}
+
+// Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
+//
+// Given an input tensor of shape `[batch, in_height, in_width, in_channels]`
+// and a filter / kernel tensor of shape
+// `[filter_height, filter_width, in_channels, channel_multiplier]`, containing
+// `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies
+// a different filter to each input channel (expanding from 1 channel to
+// `channel_multiplier` channels for each), then concatenates the results
+// together. Thus, the output has `in_channels * channel_multiplier` channels.
+//
+// ```
+// for k in 0..in_channels-1
+// for q in 0..channel_multiplier-1
+// output[b, i, j, k * channel_multiplier + q] =
+// sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *
+// filter[di, dj, k, q]
+// ```
+//
+// Must have `strides[0] = strides[3] = 1`. For the most common case of the same
+// horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
+//
+// Arguments:
+//
+//
+// strides: 1-D of length 4. The stride of the sliding window for each dimension
+// of `input`.
+// padding: The type of padding algorithm to use.
+func DepthwiseConv2dNative(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...DepthwiseConv2dNativeAttr) (output tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{"strides": strides, "padding": padding}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "DepthwiseConv2dNative",
+ Input: []tf.Input{
+ input, filter,
+ },
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Computes the gradients of 3-D convolution with respect to the input.
//
// DEPRECATED at GraphDef version 10: Use Conv3DBackpropInputV2
return op.Output(0)
}
+// CriticalSectionOpAttr is an optional argument to CriticalSectionOp.
+type CriticalSectionOpAttr func(optionalAttr)
+
+// CriticalSectionOpContainer sets the optional container attribute to value.
+//
+// value: the container this critical section is placed in.
+// If not specified, defaults to ""
+func CriticalSectionOpContainer(value string) CriticalSectionOpAttr {
+ return func(m optionalAttr) {
+ m["container"] = value
+ }
+}
+
+// CriticalSectionOpSharedName sets the optional shared_name attribute to value.
+//
+// value: the name by which this critical section is referred to.
+// If not specified, defaults to ""
+func CriticalSectionOpSharedName(value string) CriticalSectionOpAttr {
+ return func(m optionalAttr) {
+ m["shared_name"] = value
+ }
+}
+
+// Creates a handle to a CriticalSection resource.
+func CriticalSectionOp(scope *Scope, optional ...CriticalSectionOpAttr) (resource tf.Output) {
+ if scope.Err() != nil {
+ return
+ }
+ attrs := map[string]interface{}{}
+ for _, a := range optional {
+ a(attrs)
+ }
+ opspec := tf.OpSpec{
+ Type: "CriticalSectionOp",
+
+ Attrs: attrs,
+ }
+ op := scope.AddOperation(opspec)
+ return op.Output(0)
+}
+
// Computes gradients of the maxpooling function.
//
// Arguments:
op := scope.AddOperation(opspec)
return op.Output(0)
}
-
-// Table initializer that takes two tensors for keys and values respectively.
-//
-// Arguments:
-// table_handle: Handle to a table which will be initialized.
-// keys: Keys of type Tkey.
-// values: Values of type Tval.
-//
-// Returns the created operation.
-func InitializeTableV2(scope *Scope, table_handle tf.Output, keys tf.Output, values tf.Output) (o *tf.Operation) {
- if scope.Err() != nil {
- return
- }
- opspec := tf.OpSpec{
- Type: "InitializeTableV2",
- Input: []tf.Input{
- table_handle, keys, values,
- },
- }
- return scope.AddOperation(opspec)
-}
projects / platform / upstream / tensorflow.git / commitdiff