diff --git a/src/TensorFlowNET.Core/Keras/Layers/Conv.cs b/src/TensorFlowNET.Core/Keras/Layers/Conv.cs
index efa79e00..aeb37128 100644
--- a/src/TensorFlowNET.Core/Keras/Layers/Conv.cs
+++ b/src/TensorFlowNET.Core/Keras/Layers/Conv.cs
@@ -56,7 +56,9 @@ namespace Tensorflow.Keras.Layers
protected override void build(TensorShape input_shape)
{
int channel_axis = data_format == "channels_first" ? 1 : -1;
- int input_dim = input_shape.Dimensions[input_shape.NDim - 1];
+ int input_dim = channel_axis < 0 ?
+ input_shape.Dimensions[input_shape.NDim + channel_axis] :
+ input_shape.Dimensions[channel_axis];
var kernel_shape = new int[] { kernel_size[0], kernel_size[1], input_dim, filters };
kernel = add_weight(name: "kernel",
shape: kernel_shape,
@@ -102,7 +104,7 @@ namespace Tensorflow.Keras.Layers
}
else
{
- outputs = nn_ops.bias_add(outputs, bias._AsTensor(), data_format: "NHWC");
+ outputs = nn_ops.bias_add(outputs, bias, data_format: "NHWC");
}
}
diff --git a/src/TensorFlowNET.Core/Keras/Layers/Layer.cs b/src/TensorFlowNET.Core/Keras/Layers/Layer.cs
index db089959..a0a151ef 100644
--- a/src/TensorFlowNET.Core/Keras/Layers/Layer.cs
+++ b/src/TensorFlowNET.Core/Keras/Layers/Layer.cs
@@ -206,12 +206,14 @@ namespace Tensorflow.Keras.Layers
_updates.AddRange(updates_op);
}
+ // Determine layer name (non-unique).
protected virtual void _init_set_name(string name, bool zero_based = true)
{
+ var base_name = name;
+ _name = name;
if (name == null)
- _name = base_layer_utils.unique_layer_name(generic_utils.to_snake_case(this.GetType().Name), zero_based: zero_based);
- else
- _name = name;
+ (_name, base_name) = _make_unique_name();
+ _base_name = base_name;
}
protected virtual (string, string) _make_unique_name()
diff --git a/src/TensorFlowNET.Core/Layers/Layer.cs b/src/TensorFlowNET.Core/Layers/Layer.cs
index e8844d46..ff0fd28e 100644
--- a/src/TensorFlowNET.Core/Layers/Layer.cs
+++ b/src/TensorFlowNET.Core/Layers/Layer.cs
@@ -67,13 +67,6 @@ namespace Tensorflow.Layers
return outputs;
}
- protected override void _init_set_name(string name, bool zero_based = true)
- {
- // Determine layer name (non-unique).
- base._init_set_name(name, zero_based: zero_based);
- _base_name = this.name;
- }
-
protected virtual void _add_elements_to_collection(Operation[] elements, string[] collection_list)
{
foreach(var name in collection_list)
diff --git a/src/TensorFlowNET.Core/Operations/NnOps/Conv2dParams.cs b/src/TensorFlowNET.Core/Operations/NnOps/Conv2dParams.cs
new file mode 100644
index 00000000..de480692
--- /dev/null
+++ b/src/TensorFlowNET.Core/Operations/NnOps/Conv2dParams.cs
@@ -0,0 +1,53 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Operations
+{
+ public class Conv2dParams
+ {
+ public string Name { get; set; }
+
+ ///
+ /// An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`.
+ /// 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].
+ ///
+ public string DataFormat { get; set; } = "NHWC";
+
+ ///
+ /// Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`.
+ /// A 4-D tensor. The dimension order is interpreted according to the value
+ ///
+ public Tensor Input { get; set; }
+
+ ///
+ /// A 4-D tensor of shape
+ ///
+ public Tensor Filter { get; set; }
+
+ ///
+ /// The stride of the sliding window for each
+ /// dimension of `input`. The dimension order is determined by the value of
+ /// `data_format`, see below for details.
+ ///
+ public int[] Strides { get; set; }
+
+ ///
+ /// A `string` from: `"SAME", "VALID", "EXPLICIT"`.
+ ///
+ public string Padding { get; set; }
+
+ public int[] ExplicitPaddings { get; set; } = new int[0];
+
+ public bool UseCudnnOnGpu { get; set; } = true;
+
+ public int[] Dilations { get; set; } = new [] { 1, 1, 1, 1 };
+
+ public Conv2dParams()
+ {
+
+ }
+ }
+}
diff --git a/src/TensorFlowNET.Core/Operations/NnOps/_NonAtrousConvolution.cs b/src/TensorFlowNET.Core/Operations/NnOps/_NonAtrousConvolution.cs
index e6a3958a..c742884a 100644
--- a/src/TensorFlowNET.Core/Operations/NnOps/_NonAtrousConvolution.cs
+++ b/src/TensorFlowNET.Core/Operations/NnOps/_NonAtrousConvolution.cs
@@ -11,7 +11,7 @@ namespace Tensorflow.Operations
public string name;
public int[] strides;
public string data_format;
- private Func conv_op;
+ private Func conv_op;
public _NonAtrousConvolution(TensorShape input_shape,
TensorShape filter_shape,
@@ -55,14 +55,14 @@ namespace Tensorflow.Operations
public Tensor __call__(Tensor inp, RefVariable filter)
{
- return conv_op(new
+ return conv_op(new Conv2dParams
{
- input = inp,
- filter,
- strides,
- padding,
- data_format,
- name
+ Input = inp,
+ Filter = filter,
+ Strides = strides,
+ Padding = padding,
+ DataFormat = data_format,
+ Name = name
});
}
}
diff --git a/src/TensorFlowNET.Core/Operations/NnOps/gen_nn_ops.cs b/src/TensorFlowNET.Core/Operations/NnOps/gen_nn_ops.cs
index 9dfc882e..7e985a01 100644
--- a/src/TensorFlowNET.Core/Operations/NnOps/gen_nn_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/NnOps/gen_nn_ops.cs
@@ -10,28 +10,36 @@ namespace Tensorflow.Operations
{
public static OpDefLibrary _op_def_lib = new OpDefLibrary();
- public static Tensor conv2d(object parameters)
+ ///
+ /// Computes a 2-D 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, out_channels]`, this op
+ /// performs the following:
+ ///
+ /// 1. Flattens the filter to a 2-D matrix with shape
+ /// `[filter_height * filter_width * in_channels, output_channels]`.
+ /// 2. Extracts image patches from the input tensor to form a *virtual*
+ /// tensor of shape `[batch, out_height, out_width,
+ /// filter_height * filter_width * in_channels]`.
+ /// 3. For each patch, right-multiplies the filter matrix and the image patch
+ /// vector.
+ ///
+ ///
+ ///
+ public static Tensor conv2d(Conv2dParams parameters)
{
- var args = Python.ConvertToDict(parameters);
-
- var input = args["input"];
- var filter = args["filter"];
- var strides = args["strides"];
- var padding = args["padding"];
- var name = args["name"];
- var data_format = args.ContainsKey("data_format") ? args["data_format"] : "NHWC";
- var use_cudnn_on_gpu = args.ContainsKey("use_cudnn_on_gpu") ? args["use_cudnn_on_gpu"] : true;
- var dilations = args.ContainsKey("dilations") ? args["dilations"] : new int[] { 1, 1, 1, 1 };
-
- var _op = _op_def_lib._apply_op_helper("Conv2D", name: name?.ToString(), args: new
+ var _op = _op_def_lib._apply_op_helper("Conv2D", name: parameters.Name, args: new
{
- input,
- filter,
- strides,
- padding,
- use_cudnn_on_gpu,
- data_format,
- dilations
+ input = parameters.Input,
+ filter = parameters.Filter,
+ strides = parameters.Strides,
+ padding = parameters.Padding,
+ use_cudnn_on_gpu = parameters.UseCudnnOnGpu,
+ explicit_paddings = parameters.ExplicitPaddings,
+ data_format = parameters.DataFormat,
+ dilations = parameters.Dilations
});
return _op.outputs[0];
diff --git a/src/TensorFlowNET.Core/Operations/nn_ops.cs b/src/TensorFlowNET.Core/Operations/nn_ops.cs
index b778bfae..69d5f166 100644
--- a/src/TensorFlowNET.Core/Operations/nn_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/nn_ops.cs
@@ -37,6 +37,7 @@ namespace Tensorflow
{
return Python.with(ops.name_scope(name, "BiasAdd", new { value, bias }), scope =>
{
+ name = scope;
value = ops.convert_to_tensor(value, name: "input");
var bias_tensor = ops.convert_to_tensor(bias, dtype: value.dtype, name: "bias");
return gen_nn_ops.bias_add(value, bias_tensor, data_format: data_format, name: name);
diff --git a/src/TensorFlowNET.Core/Variables/RefVariable.cs b/src/TensorFlowNET.Core/Variables/RefVariable.cs
index 3d586f4b..7b1de63c 100644
--- a/src/TensorFlowNET.Core/Variables/RefVariable.cs
+++ b/src/TensorFlowNET.Core/Variables/RefVariable.cs
@@ -188,7 +188,7 @@ namespace Tensorflow
public Tensor _as_graph_element() => _variable;
- public Tensor _TensorConversionFunction(bool as_ref = false)
+ public Tensor _TensorConversionFunction(TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool as_ref = false)
{
if (as_ref)
return _ref();
diff --git a/src/TensorFlowNET.Core/Variables/VariableScope.cs b/src/TensorFlowNET.Core/Variables/VariableScope.cs
index fe6f973e..60cd7777 100644
--- a/src/TensorFlowNET.Core/Variables/VariableScope.cs
+++ b/src/TensorFlowNET.Core/Variables/VariableScope.cs
@@ -40,7 +40,7 @@ namespace Tensorflow
VariableSynchronization synchronization = VariableSynchronization.Auto,
VariableAggregation aggregation= VariableAggregation.None)
{
- string full_name = !string.IsNullOrEmpty(this._name) ? this._name + "/" + name : name;
+ string full_name = !string.IsNullOrEmpty(this.name) ? this.name + "/" + name : name;
return with(ops.name_scope(null), scope =>
{
if (dtype == TF_DataType.DtInvalid)
diff --git a/src/TensorFlowNET.Core/ops.py.cs b/src/TensorFlowNET.Core/ops.py.cs
index 20dbf668..48c7909c 100644
--- a/src/TensorFlowNET.Core/ops.py.cs
+++ b/src/TensorFlowNET.Core/ops.py.cs
@@ -473,7 +473,7 @@ namespace Tensorflow
case Tensor[] tensors:
return array_ops._autopacking_helper(tensors, dtype, name == null ? "packed" : name);
case RefVariable varVal:
- return varVal._TensorConversionFunction(as_ref: as_ref);
+ return varVal._TensorConversionFunction(dtype: dtype, name: name, as_ref: as_ref);
case ResourceVariable varVal:
return null;
case object[] objects: