diff --git a/.gitignore b/.gitignore
index 9fd6144c..6cfc7c16 100644
--- a/.gitignore
+++ b/.gitignore
@@ -310,3 +310,4 @@ examples/TensorFlow/Keras/keras_to_MDF
/checkout_pngs.sh
/examples/TensorFlow/Keras/IRIS/keras_model_on_iris.keras
/examples/TensorFlow/Keras/MNIST/kr_N_model.keras
+/checkoutpngs.sh
diff --git a/docs/MDF_function_specifications.json b/docs/MDF_function_specifications.json
index ddab64cc..cde46305 100644
--- a/docs/MDF_function_specifications.json
+++ b/docs/MDF_function_specifications.json
@@ -14,6 +14,30 @@
],
"expression_string": "A * (A > 0)"
},
+ "arccos": {
+ "description": "Inverse cosine function",
+ "arguments": [
+ "variable0",
+ "scale"
+ ],
+ "expression_string": "scale * arccos(variable0)"
+ },
+ "arcsin": {
+ "description": "Inverse sine function",
+ "arguments": [
+ "variable0",
+ "scale"
+ ],
+ "expression_string": "scale * arcsin(variable0)"
+ },
+ "arctan": {
+ "description": "Inverse tangent function",
+ "arguments": [
+ "variable0",
+ "scale"
+ ],
+ "expression_string": "scale * arctan(variable0)"
+ },
"change_goal": {
"description": "Modifies the current goal buffer using the given pattern.",
"arguments": [
@@ -687,7 +711,7 @@
"expression_string": "onnx_ops.max(data_0)"
},
"onnx::MaxPool": {
- "description": "\n MaxPool consumes an input tensor X and applies max pooling across\n the tensor according to kernel sizes, stride sizes, and pad lengths.\n max pooling consisting of computing the max on all values of a\n subset of the input tensor according to the kernel size and downsampling the\n data into the output tensor Y for further processing. The output spatial shape is calculated differently\n depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized.\n With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d):\n ```\n output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n or\n ```\n output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`.\n\n `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:\n ```\n VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])\n ```\n or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D):\n ```\n VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1\n ```\n And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:\n ```\n pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]\n ```\n The output of each pooling window is maximum number of elements exclude pad. \n ",
+ "description": "\n MaxPool consumes an input tensor X and applies max pooling across\n the tensor according to kernel sizes, stride sizes, and pad lengths.\n max pooling consisting of computing the max on all values of a\n subset of the input tensor according to the kernel size and downsampling the\n data into the output tensor Y for further processing. The output spatial shape is calculated differently\n depending on whether explicit padding is used, where pads is employed, or auto padding is used, where auto_pad is utilized.\n With explicit padding (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html?highlight=maxpool#torch.nn.MaxPool2d):\n ```\n output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n or\n ```\n output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)\n ```\n if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored.\n\n `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:\n ```\n VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i])\n ```\n or when ceil_mode is disabled (https://www.tensorflow.org/api_docs/python/tf/keras/layers/AveragePooling2D):\n ```\n VALID: output_spatial_shape[i] = floor((input_spatial_shape[i] - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1)) / strides_spatial_shape[i]) + 1\n SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = floor((input_spatial_shape[i] - 1) / strides_spatial_shape[i]) + 1\n ```\n And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:\n ```\n pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) - input_spatial_shape[i]\n ```\n The output of each pooling window is maximum number of elements exclude pad. \n ",
"arguments": [
"X"
],
@@ -1335,7 +1359,7 @@
"expression_string": "onnx_ops.tile(input, repeats)"
},
"onnx::TopK": {
- "description": "\nRetrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of\nshape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:\n\n* Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]\n which contains the values of the top k elements along the specified axis\n* Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which\n contains the indices of the top k elements (original indices from the input\n tensor).\n\n* If \"largest\" is 1 (the default value) then the k largest elements are returned.\n* If \"sorted\" is 1 (the default value) then the resulting k elements will be sorted.\n* If \"sorted\" is 0, order of returned 'Values' and 'Indices' are undefined.\n\nGiven two equivalent values, this operator uses the indices along the axis as\na tiebreaker. That is, the element with the lower index will appear first.\n",
+ "description": "\nRetrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of\nshape [a_0, a_1, ..., a_{n-1}] and integer argument k, return two outputs:\n\n* Value tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}]\n which contains the values of the top k elements along the specified axis\n* Index tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which\n contains the indices of the top k elements (original indices from the input\n tensor).\n\n* If \"largest\" is 1 (the default value) then the k largest elements are returned.\n* If \"sorted\" is 1 (the default value) then the resulting k elements will be sorted.\n* If \"sorted\" is 0, order of returned 'Values' and 'Indices' are undefined.\n\nGiven two equivalent values, this operator uses the indices along the axis as\na tiebreaker. That is, the element with the lower index will appear first.\n",
"arguments": [
"X",
"K"
diff --git a/docs/MDF_function_specifications.md b/docs/MDF_function_specifications.md
index 69848e9c..e8d6d894 100644
--- a/docs/MDF_function_specifications.md
+++ b/docs/MDF_function_specifications.md
@@ -5,6 +5,9 @@ These functions are defined in Python API module MatMul
- Relu
+- arccos
+- arcsin
+- arctan
- change_goal
- check_termination
- chunk_to_string
@@ -211,6 +214,30 @@ Python version: `A @ B`
Python version: `A * (A > 0)`
+
+
+## arccos
+
Inverse cosine function
+arccos(variable0, scale) = scale * arccos(variable0)
+
+Python version: `scale * numpy.arccos(variable0)`
+
+
+
+## arcsin
+ Inverse sine function
+arcsin(variable0, scale) = scale * arcsin(variable0)
+
+Python version: `scale * numpy.arcsin(variable0)`
+
+
+
+## arctan
+ Inverse tangent function
+arctan(variable0, scale) = scale * arctan(variable0)
+
+Python version: `scale * numpy.arctan(variable0)`
+
## change_goal
@@ -320,7 +347,7 @@ Python version: `onnx_ops.abs(X)`
Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise.
-Python version: `onnx_ops.anumpy.cos(input)`
+Python version: `onnx_ops.acos(input)`
ONNX Documentation
@@ -330,7 +357,7 @@ Python version: `onnx_ops.anumpy.cos(input)`
Calculates the hyperbolic arccosine of the given input tensor element-wise.
-Python version: `onnx_ops.anumpy.cosh(input)`
+Python version: `onnx_ops.acosh(input)`
ONNX Documentation
@@ -397,7 +424,7 @@ Python version: `onnx_ops.argmin(data, axis, keepdims, select_last_index)`
Calculates the arcsine (inverse of sine) of the given input tensor, element-wise.
-Python version: `onnx_ops.anumpy.sin(input)`
+Python version: `onnx_ops.asin(input)`
ONNX Documentation
@@ -407,7 +434,7 @@ Python version: `onnx_ops.anumpy.sin(input)`
Calculates the hyperbolic arcsine of the given input tensor element-wise.
-Python version: `onnx_ops.anumpy.sinh(input)`
+Python version: `onnx_ops.asinh(input)`
ONNX Documentation
@@ -417,7 +444,7 @@ Python version: `onnx_ops.anumpy.sinh(input)`
Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise.
-Python version: `onnx_ops.anumpy.tan(input)`
+Python version: `onnx_ops.atan(input)`
ONNX Documentation
@@ -427,7 +454,7 @@ Python version: `onnx_ops.anumpy.tan(input)`
Calculates the hyperbolic arctangent of the given input tensor element-wise.
-Python version: `onnx_ops.anumpy.tanh(input)`
+Python version: `onnx_ops.atanh(input)`
ONNX Documentation
@@ -1705,7 +1732,7 @@ Python version: `onnx_ops.max(data_0)`
```
output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)
```
- if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`.
+ if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored.
`auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:
```
@@ -2464,7 +2491,7 @@ valid. Reduction over an empty set of values yields minus infinity (if supported
The above behavior is similar to numpy, with the exception that numpy defaults `keepdims`
to `False` instead of `True`.
-Python version: `onnx_ops.reducelogsumnumpy.exp(data, axes, keepdims)`
+Python version: `onnx_ops.reducelogsumexp(data, axes, keepdims)`
ONNX Documentation
@@ -3431,11 +3458,11 @@ Python version: `onnx_ops.tile(input, repeats)`
## TopK
Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of
-shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:
+shape [a_0, a_1, ..., a_{n-1}] and integer argument k, return two outputs:
-* Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
+* Value tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}]
which contains the values of the top k elements along the specified axis
-* Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which
+* Index tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which
contains the indices of the top k elements (original indices from the input
tensor).
diff --git a/docs/MDF_function_specifications.yaml b/docs/MDF_function_specifications.yaml
index 6bd5aa99..af73c748 100644
--- a/docs/MDF_function_specifications.yaml
+++ b/docs/MDF_function_specifications.yaml
@@ -9,6 +9,24 @@ Relu:
arguments:
- A
expression_string: A * (A > 0)
+arccos:
+ description: Inverse cosine function
+ arguments:
+ - variable0
+ - scale
+ expression_string: scale * arccos(variable0)
+arcsin:
+ description: Inverse sine function
+ arguments:
+ - variable0
+ - scale
+ expression_string: scale * arcsin(variable0)
+arctan:
+ description: Inverse tangent function
+ arguments:
+ - variable0
+ - scale
+ expression_string: scale * arctan(variable0)
change_goal:
description: Modifies the current goal buffer using the given pattern.
arguments:
@@ -1650,7 +1668,8 @@ onnx::MaxPool:
\ 1)\n ```\n or\n ```\n output_spatial_shape[i] = ceil((input_spatial_shape[i]\
\ + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i]\
\ + 1)\n ```\n if ceil_mode is enabled. `pad_shape[i]` is the sum of pads\
- \ along axis `i`.\n\n `auto_pad` is a DEPRECATED attribute. If you are using\
+ \ along axis `i`. Sliding windows that would start in the right padded region\
+ \ are ignored.\n\n `auto_pad` is a DEPRECATED attribute. If you are using\
\ them currently, the output spatial shape will be following when ceil_mode\
\ is enabled:\n ```\n VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i]\
\ - ((kernel_spatial_shape[i] - 1) * dilations[i] + 1) + 1) / strides_spatial_shape[i])\n\
@@ -3461,18 +3480,18 @@ onnx::Tile:
expression_string: onnx_ops.tile(input, repeats)
onnx::TopK:
description: "\nRetrieve the top-K largest or smallest elements along a specified\
- \ axis. Given an input tensor of\nshape [a_1, a_2, ..., a_n, r] and integer\
- \ argument k, return two outputs:\n\n* Value tensor of shape [a_1, a_2, ...,\
- \ a_{axis-1}, k, a_{axis+1}, ... a_n]\n which contains the values of the\
- \ top k elements along the specified axis\n* Index tensor of shape [a_1, a_2,\
- \ ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which\n contains the indices of\
- \ the top k elements (original indices from the input\n tensor).\n\n* If\
- \ \"largest\" is 1 (the default value) then the k largest elements are returned.\n\
- * If \"sorted\" is 1 (the default value) then the resulting k elements will\
- \ be sorted.\n* If \"sorted\" is 0, order of returned 'Values' and 'Indices'\
- \ are undefined.\n\nGiven two equivalent values, this operator uses the indices\
- \ along the axis as\na tiebreaker. That is, the element with the lower index\
- \ will appear first.\n"
+ \ axis. Given an input tensor of\nshape [a_0, a_1, ..., a_{n-1}] and integer\
+ \ argument k, return two outputs:\n\n* Value tensor of shape [a_0, a_1, ...,\
+ \ a_{axis-1}, k, a_{axis+1}, ... a_{n-1}]\n which contains the values of\
+ \ the top k elements along the specified axis\n* Index tensor of shape [a_0,\
+ \ a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which\n contains the\
+ \ indices of the top k elements (original indices from the input\n tensor).\n\
+ \n* If \"largest\" is 1 (the default value) then the k largest elements are\
+ \ returned.\n* If \"sorted\" is 1 (the default value) then the resulting k\
+ \ elements will be sorted.\n* If \"sorted\" is 0, order of returned 'Values'\
+ \ and 'Indices' are undefined.\n\nGiven two equivalent values, this operator\
+ \ uses the indices along the axis as\na tiebreaker. That is, the element with\
+ \ the lower index will appear first.\n"
arguments:
- X
- K
diff --git a/docs/MDF_specification.json b/docs/MDF_specification.json
index 85f47e1b..202a92c7 100644
--- a/docs/MDF_specification.json
+++ b/docs/MDF_specification.json
@@ -166,7 +166,7 @@
},
"function": {
"type": "Union[str, NoneType]",
- "description": "Which of the in-build MDF functions (linear etc.) this uses, See"
+ "description": "Which of the in-build MDF functions (linear etc.) this uses, See\nhttps://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html"
},
"args": {
"type": "Union[Any, NoneType]",
diff --git a/docs/MDF_specification.yaml b/docs/MDF_specification.yaml
index ccdcd2a7..aa520e8d 100644
--- a/docs/MDF_specification.yaml
+++ b/docs/MDF_specification.yaml
@@ -168,8 +168,10 @@ specification:
of time are seconds.
function:
type: Union[str, NoneType]
- description: Which of the in-build MDF functions (linear etc.) this
+ description: 'Which of the in-build MDF functions (linear etc.) this
uses, See
+
+ https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html'
args:
type: Union[Any, NoneType]
description: 'Dictionary of values for each of the arguments for the
diff --git a/docs/README.md b/docs/README.md
index d51c6d16..0db2109c 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -286,7 +286,8 @@ inbuilt function with args; 4) or change from a default_initial
function |
Union[str, NoneType] |
- Which of the in-build MDF functions (linear etc.) this uses, See |
+ Which of the in-build MDF functions (linear etc.) this uses, See
+https://mdf.readthedocs.io/en/latest/api/MDFfunctionspecifications.html |
diff --git a/docs/sphinx/source/api/MDF_function_specifications.md b/docs/sphinx/source/api/MDF_function_specifications.md
index 69848e9c..e8d6d894 100644
--- a/docs/sphinx/source/api/MDF_function_specifications.md
+++ b/docs/sphinx/source/api/MDF_function_specifications.md
@@ -5,6 +5,9 @@ These functions are defined in Python API module MatMul
- Relu
+- arccos
+- arcsin
+- arctan
- change_goal
- check_termination
- chunk_to_string
@@ -211,6 +214,30 @@ Python version: `A @ B`
Python version: `A * (A > 0)`
+
+
+## arccos
+
Inverse cosine function
+arccos(variable0, scale) = scale * arccos(variable0)
+
+Python version: `scale * numpy.arccos(variable0)`
+
+
+
+## arcsin
+ Inverse sine function
+arcsin(variable0, scale) = scale * arcsin(variable0)
+
+Python version: `scale * numpy.arcsin(variable0)`
+
+
+
+## arctan
+ Inverse tangent function
+arctan(variable0, scale) = scale * arctan(variable0)
+
+Python version: `scale * numpy.arctan(variable0)`
+
## change_goal
@@ -320,7 +347,7 @@ Python version: `onnx_ops.abs(X)`
Calculates the arccosine (inverse of cosine) of the given input tensor, element-wise.
-Python version: `onnx_ops.anumpy.cos(input)`
+Python version: `onnx_ops.acos(input)`
ONNX Documentation
@@ -330,7 +357,7 @@ Python version: `onnx_ops.anumpy.cos(input)`
Calculates the hyperbolic arccosine of the given input tensor element-wise.
-Python version: `onnx_ops.anumpy.cosh(input)`
+Python version: `onnx_ops.acosh(input)`
ONNX Documentation
@@ -397,7 +424,7 @@ Python version: `onnx_ops.argmin(data, axis, keepdims, select_last_index)`
Calculates the arcsine (inverse of sine) of the given input tensor, element-wise.
-Python version: `onnx_ops.anumpy.sin(input)`
+Python version: `onnx_ops.asin(input)`
ONNX Documentation
@@ -407,7 +434,7 @@ Python version: `onnx_ops.anumpy.sin(input)`
Calculates the hyperbolic arcsine of the given input tensor element-wise.
-Python version: `onnx_ops.anumpy.sinh(input)`
+Python version: `onnx_ops.asinh(input)`
ONNX Documentation
@@ -417,7 +444,7 @@ Python version: `onnx_ops.anumpy.sinh(input)`
Calculates the arctangent (inverse of tangent) of the given input tensor, element-wise.
-Python version: `onnx_ops.anumpy.tan(input)`
+Python version: `onnx_ops.atan(input)`
ONNX Documentation
@@ -427,7 +454,7 @@ Python version: `onnx_ops.anumpy.tan(input)`
Calculates the hyperbolic arctangent of the given input tensor element-wise.
-Python version: `onnx_ops.anumpy.tanh(input)`
+Python version: `onnx_ops.atanh(input)`
ONNX Documentation
@@ -1705,7 +1732,7 @@ Python version: `onnx_ops.max(data_0)`
```
output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)
```
- if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`.
+ if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored.
`auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:
```
@@ -2464,7 +2491,7 @@ valid. Reduction over an empty set of values yields minus infinity (if supported
The above behavior is similar to numpy, with the exception that numpy defaults `keepdims`
to `False` instead of `True`.
-Python version: `onnx_ops.reducelogsumnumpy.exp(data, axes, keepdims)`
+Python version: `onnx_ops.reducelogsumexp(data, axes, keepdims)`
ONNX Documentation
@@ -3431,11 +3458,11 @@ Python version: `onnx_ops.tile(input, repeats)`
## TopK
Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of
-shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:
+shape [a_0, a_1, ..., a_{n-1}] and integer argument k, return two outputs:
-* Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
+* Value tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}]
which contains the values of the top k elements along the specified axis
-* Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which
+* Index tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1}] which
contains the indices of the top k elements (original indices from the input
tensor).
diff --git a/docs/sphinx/source/api/Specification.rst b/docs/sphinx/source/api/Specification.rst
index 9926de03..18761b00 100644
--- a/docs/sphinx/source/api/Specification.rst
+++ b/docs/sphinx/source/api/Specification.rst
@@ -135,6 +135,7 @@ Allowed field Data Type
**default_initial_value** Union[EvaluableExpression, List, Dict, ndarray, int, float, str, NoneType] The initial value of the parameter, only used when parameter is stateful.
**time_derivative** Union[str, NoneType] How the parameter changes with time, i.e. ds/dt. Units of time are seconds.
**function** Union[str, NoneType] Which of the in-build MDF functions (linear etc.) this uses, See
+ https://mdf.readthedocs.io/en/latest/api/MDF_function_specifications.html
**args** Union[Any, NoneType] Dictionary of values for each of the arguments for the function of the parameter,
e.g. if the in-build function is **linear(slope)**, the args here could be **{"slope": 3}** or
**{"slope": "input_port_0 + 2"}**
diff --git a/examples/ACT-R/addition.json b/examples/ACT-R/addition.json
index 8423d002..822c40b1 100644
--- a/examples/ACT-R/addition.json
+++ b/examples/ACT-R/addition.json
@@ -1,7 +1,7 @@
{
"addition": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"addition_graph": {
"nodes": {
@@ -485,16 +485,6 @@
"dependencies": "conflict_resolution"
}
}
- },
- "termination": {
- "check_term_true": {
- "type": "JustRan",
- "kwargs": {
- "dependencies": [
- "check_termination"
- ]
- }
- }
}
}
}
diff --git a/examples/ACT-R/addition.yaml b/examples/ACT-R/addition.yaml
index df10d236..fbb416ee 100644
--- a/examples/ACT-R/addition.yaml
+++ b/examples/ACT-R/addition.yaml
@@ -1,6 +1,6 @@
addition:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
addition_graph:
nodes:
@@ -319,9 +319,3 @@ addition:
type: JustRan
kwargs:
dependencies: conflict_resolution
- termination:
- check_term_true:
- type: JustRan
- kwargs:
- dependencies:
- - check_termination
diff --git a/examples/ACT-R/count.json b/examples/ACT-R/count.json
index 7c13a54d..8dfa41fd 100644
--- a/examples/ACT-R/count.json
+++ b/examples/ACT-R/count.json
@@ -1,7 +1,7 @@
{
"count": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"count_graph": {
"nodes": {
@@ -409,16 +409,6 @@
"dependencies": "conflict_resolution"
}
}
- },
- "termination": {
- "check_term_true": {
- "type": "JustRan",
- "kwargs": {
- "dependencies": [
- "check_termination"
- ]
- }
- }
}
}
}
diff --git a/examples/ACT-R/count.yaml b/examples/ACT-R/count.yaml
index 8c94ea64..2776344d 100644
--- a/examples/ACT-R/count.yaml
+++ b/examples/ACT-R/count.yaml
@@ -1,6 +1,6 @@
count:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
count_graph:
nodes:
@@ -267,9 +267,3 @@ count:
type: JustRan
kwargs:
dependencies: conflict_resolution
- termination:
- check_term_true:
- type: JustRan
- kwargs:
- dependencies:
- - check_termination
diff --git a/examples/MDF/ABCD.json b/examples/MDF/ABCD.json
index c553bde5..89b88a7a 100644
--- a/examples/MDF/ABCD.json
+++ b/examples/MDF/ABCD.json
@@ -1,7 +1,7 @@
{
"ABCD": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"abcd_example": {
"nodes": {
diff --git a/examples/MDF/ABCD.yaml b/examples/MDF/ABCD.yaml
index c87d7bec..cd614f7d 100644
--- a/examples/MDF/ABCD.yaml
+++ b/examples/MDF/ABCD.yaml
@@ -1,6 +1,6 @@
ABCD:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
abcd_example:
nodes:
diff --git a/examples/MDF/Arrays.json b/examples/MDF/Arrays.json
index fa108749..8ca82454 100644
--- a/examples/MDF/Arrays.json
+++ b/examples/MDF/Arrays.json
@@ -1,7 +1,7 @@
{
"Arrays": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"array_example": {
"nodes": {
diff --git a/examples/MDF/Arrays.yaml b/examples/MDF/Arrays.yaml
index 3825866f..cc585cae 100644
--- a/examples/MDF/Arrays.yaml
+++ b/examples/MDF/Arrays.yaml
@@ -1,6 +1,6 @@
Arrays:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
array_example:
nodes:
diff --git a/examples/MDF/ParametersFunctions.json b/examples/MDF/ParametersFunctions.json
index b97f9a78..10e1b13a 100644
--- a/examples/MDF/ParametersFunctions.json
+++ b/examples/MDF/ParametersFunctions.json
@@ -1,7 +1,7 @@
{
"ParametersFunctions": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"params_funcs_example": {
"nodes": {
diff --git a/examples/MDF/ParametersFunctions.yaml b/examples/MDF/ParametersFunctions.yaml
index 1e115511..2207e5c0 100644
--- a/examples/MDF/ParametersFunctions.yaml
+++ b/examples/MDF/ParametersFunctions.yaml
@@ -1,6 +1,6 @@
ParametersFunctions:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
params_funcs_example:
nodes:
diff --git a/examples/MDF/RNN/IAF_net.json b/examples/MDF/RNN/IAF_net.json
index ae47caa4..a005bcfb 100644
--- a/examples/MDF/RNN/IAF_net.json
+++ b/examples/MDF/RNN/IAF_net.json
@@ -1,7 +1,7 @@
{
"IAF_net": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"iaf_example": {
"nodes": {
diff --git a/examples/MDF/RNN/IAF_net.yaml b/examples/MDF/RNN/IAF_net.yaml
index b6af0d91..3704a1bb 100644
--- a/examples/MDF/RNN/IAF_net.yaml
+++ b/examples/MDF/RNN/IAF_net.yaml
@@ -1,6 +1,6 @@
IAF_net:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
iaf_example:
nodes:
diff --git a/examples/MDF/RNN/IAF_net2.json b/examples/MDF/RNN/IAF_net2.json
index 7bbc1f25..b816852b 100644
--- a/examples/MDF/RNN/IAF_net2.json
+++ b/examples/MDF/RNN/IAF_net2.json
@@ -1,7 +1,7 @@
{
"IAF_net2": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"iaf_example": {
"nodes": {
diff --git a/examples/MDF/RNN/IAF_net2.yaml b/examples/MDF/RNN/IAF_net2.yaml
index 769202ec..db46f9c5 100644
--- a/examples/MDF/RNN/IAF_net2.yaml
+++ b/examples/MDF/RNN/IAF_net2.yaml
@@ -1,6 +1,6 @@
IAF_net2:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
iaf_example:
nodes:
diff --git a/examples/MDF/RNN/IAFs.json b/examples/MDF/RNN/IAFs.json
index 6868698f..d75f9e5a 100644
--- a/examples/MDF/RNN/IAFs.json
+++ b/examples/MDF/RNN/IAFs.json
@@ -1,7 +1,7 @@
{
"IAFs": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"iaf_example": {
"nodes": {
diff --git a/examples/MDF/RNN/IAFs.yaml b/examples/MDF/RNN/IAFs.yaml
index 2d7cee1e..84e4c4bd 100644
--- a/examples/MDF/RNN/IAFs.yaml
+++ b/examples/MDF/RNN/IAFs.yaml
@@ -1,6 +1,6 @@
IAFs:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
iaf_example:
nodes:
diff --git a/examples/MDF/RNN/RNNs.json b/examples/MDF/RNN/RNNs.json
index 46822464..6028d355 100644
--- a/examples/MDF/RNN/RNNs.json
+++ b/examples/MDF/RNN/RNNs.json
@@ -1,7 +1,7 @@
{
"RNNs": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"rnn_example": {
"nodes": {
diff --git a/examples/MDF/RNN/RNNs.yaml b/examples/MDF/RNN/RNNs.yaml
index 502355c5..8e2f517b 100644
--- a/examples/MDF/RNN/RNNs.yaml
+++ b/examples/MDF/RNN/RNNs.yaml
@@ -1,6 +1,6 @@
RNNs:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
rnn_example:
nodes:
diff --git a/examples/MDF/Simple.bson b/examples/MDF/Simple.bson
index e65b3ee9..dac44ab0 100644
Binary files a/examples/MDF/Simple.bson and b/examples/MDF/Simple.bson differ
diff --git a/examples/MDF/Simple.json b/examples/MDF/Simple.json
index fdede522..9f14ee1e 100644
--- a/examples/MDF/Simple.json
+++ b/examples/MDF/Simple.json
@@ -1,7 +1,7 @@
{
"Simple": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"simple_example": {
"nodes": {
diff --git a/examples/MDF/Simple.yaml b/examples/MDF/Simple.yaml
index eb7cf673..e0f33ea9 100644
--- a/examples/MDF/Simple.yaml
+++ b/examples/MDF/Simple.yaml
@@ -1,6 +1,6 @@
Simple:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
simple_example:
nodes:
diff --git a/examples/MDF/States.json b/examples/MDF/States.json
index fafde25a..9f9eecef 100644
--- a/examples/MDF/States.json
+++ b/examples/MDF/States.json
@@ -1,7 +1,7 @@
{
"States": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"state_example": {
"nodes": {
diff --git a/examples/MDF/States.yaml b/examples/MDF/States.yaml
index cc526161..241a1a8d 100644
--- a/examples/MDF/States.yaml
+++ b/examples/MDF/States.yaml
@@ -1,6 +1,6 @@
States:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
state_example:
nodes:
diff --git a/examples/MDF/abc_conditions.json b/examples/MDF/abc_conditions.json
index 4628ce1c..dfee679b 100644
--- a/examples/MDF/abc_conditions.json
+++ b/examples/MDF/abc_conditions.json
@@ -1,7 +1,7 @@
{
"abc_conditions": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"abc_conditions_example": {
"nodes": {
diff --git a/examples/MDF/abc_conditions.yaml b/examples/MDF/abc_conditions.yaml
index c6c1abb7..4d9df027 100644
--- a/examples/MDF/abc_conditions.yaml
+++ b/examples/MDF/abc_conditions.yaml
@@ -1,6 +1,6 @@
abc_conditions:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
abc_conditions_example:
nodes:
diff --git a/examples/MDF/conditions/Composite_mdf_condition.json b/examples/MDF/conditions/Composite_mdf_condition.json
index 5c7ba98e..66e05b34 100644
--- a/examples/MDF/conditions/Composite_mdf_condition.json
+++ b/examples/MDF/conditions/Composite_mdf_condition.json
@@ -1,7 +1,7 @@
{
"Composite_mdf_condition": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"Composite_mdf_condition_example": {
"nodes": {
diff --git a/examples/MDF/conditions/Composite_mdf_condition.yaml b/examples/MDF/conditions/Composite_mdf_condition.yaml
index ef8c5ea9..02375bda 100644
--- a/examples/MDF/conditions/Composite_mdf_condition.yaml
+++ b/examples/MDF/conditions/Composite_mdf_condition.yaml
@@ -1,6 +1,6 @@
Composite_mdf_condition:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
Composite_mdf_condition_example:
nodes:
diff --git a/examples/MDF/conditions/everyncalls_condition.json b/examples/MDF/conditions/everyncalls_condition.json
index 33c8ea4a..1965342c 100644
--- a/examples/MDF/conditions/everyncalls_condition.json
+++ b/examples/MDF/conditions/everyncalls_condition.json
@@ -1,7 +1,7 @@
{
"everyncalls_condition": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"everyncalls_example": {
"nodes": {
diff --git a/examples/MDF/conditions/everyncalls_condition.yaml b/examples/MDF/conditions/everyncalls_condition.yaml
index 6efec591..29c9f5ae 100644
--- a/examples/MDF/conditions/everyncalls_condition.yaml
+++ b/examples/MDF/conditions/everyncalls_condition.yaml
@@ -1,6 +1,6 @@
everyncalls_condition:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
everyncalls_example:
nodes:
diff --git a/examples/MDF/conditions/threshold_condition.json b/examples/MDF/conditions/threshold_condition.json
index 191b9afd..c78655e1 100644
--- a/examples/MDF/conditions/threshold_condition.json
+++ b/examples/MDF/conditions/threshold_condition.json
@@ -1,7 +1,7 @@
{
"threshold_condition": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"threshold_example": {
"nodes": {
diff --git a/examples/MDF/conditions/threshold_condition.yaml b/examples/MDF/conditions/threshold_condition.yaml
index 87f3dcdc..2307d798 100644
--- a/examples/MDF/conditions/threshold_condition.yaml
+++ b/examples/MDF/conditions/threshold_condition.yaml
@@ -1,6 +1,6 @@
threshold_condition:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
threshold_example:
nodes:
diff --git a/examples/MDF/conditions/timeinterval_condition.json b/examples/MDF/conditions/timeinterval_condition.json
index 9962404d..84c1afb0 100644
--- a/examples/MDF/conditions/timeinterval_condition.json
+++ b/examples/MDF/conditions/timeinterval_condition.json
@@ -1,7 +1,7 @@
{
"timeinterval_condition": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"timeinterval_example": {
"nodes": {
diff --git a/examples/MDF/conditions/timeinterval_condition.yaml b/examples/MDF/conditions/timeinterval_condition.yaml
index 0c08e5c2..afb5e60d 100644
--- a/examples/MDF/conditions/timeinterval_condition.yaml
+++ b/examples/MDF/conditions/timeinterval_condition.yaml
@@ -1,6 +1,6 @@
timeinterval_condition:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
timeinterval_example:
nodes:
diff --git a/examples/NeuroML/ABCD.json b/examples/NeuroML/ABCD.json
index e9b6c3e1..c1a912c0 100644
--- a/examples/NeuroML/ABCD.json
+++ b/examples/NeuroML/ABCD.json
@@ -1,6 +1,6 @@
{
"ABCD": {
- "version": "NeuroMLlite v0.5.7",
+ "version": "NeuroMLlite v0.5.9",
"notes": "Example of a simplified network",
"parameters": {
"A_initial": 0.1,
diff --git a/examples/NeuroML/ABCD.net.nml b/examples/NeuroML/ABCD.net.nml
index bfe6acb3..8a29509f 100644
--- a/examples/NeuroML/ABCD.net.nml
+++ b/examples/NeuroML/ABCD.net.nml
@@ -1,5 +1,5 @@
- Generated by NeuroMLlite v0.5.7
+ Generated by NeuroMLlite v0.5.9
Generated network: ABCD
Generation seed: 1234
NeuroMLlite parameters:
diff --git a/examples/NeuroML/FN.json b/examples/NeuroML/FN.json
index c1235a5e..3e02690f 100644
--- a/examples/NeuroML/FN.json
+++ b/examples/NeuroML/FN.json
@@ -1,6 +1,6 @@
{
"FN": {
- "version": "NeuroMLlite v0.5.7",
+ "version": "NeuroMLlite v0.5.9",
"notes": "FitzHugh Nagumo cell model - originally specified in NeuroML/LEMS",
"parameters": {
"initial_w": 0.0,
diff --git a/examples/NeuroML/FN.net.nml b/examples/NeuroML/FN.net.nml
index c3649de6..28c0b9bc 100644
--- a/examples/NeuroML/FN.net.nml
+++ b/examples/NeuroML/FN.net.nml
@@ -1,5 +1,5 @@
- Generated by NeuroMLlite v0.5.7
+ Generated by NeuroMLlite v0.5.9
Generated network: FN
Generation seed: 1234
NeuroMLlite parameters:
diff --git a/examples/NeuroML/IzhikevichTest.net.nml b/examples/NeuroML/IzhikevichTest.net.nml
index 65b7372f..95490bdd 100644
--- a/examples/NeuroML/IzhikevichTest.net.nml
+++ b/examples/NeuroML/IzhikevichTest.net.nml
@@ -1,5 +1,5 @@
- Generated by NeuroMLlite v0.5.7
+ Generated by NeuroMLlite v0.5.9
Generated network: IzhikevichTest
Generation seed: 1234
NeuroMLlite parameters:
diff --git a/examples/NeuroML/IzhikevichTest.nmllite.yaml b/examples/NeuroML/IzhikevichTest.nmllite.yaml
index 79fe02a8..7a271c1c 100644
--- a/examples/NeuroML/IzhikevichTest.nmllite.yaml
+++ b/examples/NeuroML/IzhikevichTest.nmllite.yaml
@@ -1,5 +1,5 @@
IzhikevichTest:
- version: NeuroMLlite v0.5.7
+ version: NeuroMLlite v0.5.9
notes: Example Izhikevich
parameters:
N: 1
diff --git a/examples/NeuroML/LEMS_SimABCD.xml b/examples/NeuroML/LEMS_SimABCD.xml
index 8d6f20e1..9236e0a1 100644
--- a/examples/NeuroML/LEMS_SimABCD.xml
+++ b/examples/NeuroML/LEMS_SimABCD.xml
@@ -2,7 +2,7 @@
diff --git a/examples/NeuroML/LEMS_SimFN.xml b/examples/NeuroML/LEMS_SimFN.xml
index 30878a37..20334f61 100644
--- a/examples/NeuroML/LEMS_SimFN.xml
+++ b/examples/NeuroML/LEMS_SimFN.xml
@@ -2,7 +2,7 @@
diff --git a/examples/NeuroML/LEMS_SimIzhikevichTest.xml b/examples/NeuroML/LEMS_SimIzhikevichTest.xml
index a38e06e3..d5d1bc62 100644
--- a/examples/NeuroML/LEMS_SimIzhikevichTest.xml
+++ b/examples/NeuroML/LEMS_SimIzhikevichTest.xml
@@ -2,7 +2,7 @@
diff --git a/examples/NeuroML/PyNN/HH.net.nml b/examples/NeuroML/PyNN/HH.net.nml
index 202428b1..256e4546 100644
--- a/examples/NeuroML/PyNN/HH.net.nml
+++ b/examples/NeuroML/PyNN/HH.net.nml
@@ -1,5 +1,5 @@
- Generated by NeuroMLlite v0.5.7
+ Generated by NeuroMLlite v0.5.9
Generated network: HH
Generation seed: 1234
NeuroMLlite parameters:
diff --git a/examples/NeuroML/PyNN/InputWeights.net.nml b/examples/NeuroML/PyNN/InputWeights.net.nml
index f0bd24f1..d5ab31f8 100644
--- a/examples/NeuroML/PyNN/InputWeights.net.nml
+++ b/examples/NeuroML/PyNN/InputWeights.net.nml
@@ -1,5 +1,5 @@
- Generated by NeuroMLlite v0.5.7
+ Generated by NeuroMLlite v0.5.9
Generated network: InputWeights
Generation seed: 1234
NeuroMLlite parameters:
diff --git a/examples/NeuroML/PyNN/Net1.net.nml b/examples/NeuroML/PyNN/Net1.net.nml
index 0b173eb5..d806e868 100644
--- a/examples/NeuroML/PyNN/Net1.net.nml
+++ b/examples/NeuroML/PyNN/Net1.net.nml
@@ -1,5 +1,5 @@
- Generated by NeuroMLlite v0.5.7
+ Generated by NeuroMLlite v0.5.9
Generated network: Net1
Generation seed: 1234
NeuroMLlite parameters:
diff --git a/examples/NeuroML/PyNN/OneCell.net.nml b/examples/NeuroML/PyNN/OneCell.net.nml
index 3e894506..180cdd67 100644
--- a/examples/NeuroML/PyNN/OneCell.net.nml
+++ b/examples/NeuroML/PyNN/OneCell.net.nml
@@ -1,5 +1,5 @@
- Generated by NeuroMLlite v0.5.7
+ Generated by NeuroMLlite v0.5.9
Generated network: OneCell
Generation seed: 1234
NeuroMLlite parameters:
diff --git a/examples/NeuroML/PyNN/SimpleNet.net.nml b/examples/NeuroML/PyNN/SimpleNet.net.nml
index 3788e7fe..eedb0c5c 100644
--- a/examples/NeuroML/PyNN/SimpleNet.net.nml
+++ b/examples/NeuroML/PyNN/SimpleNet.net.nml
@@ -1,5 +1,5 @@
- Generated by NeuroMLlite v0.5.7
+ Generated by NeuroMLlite v0.5.9
Generated network: SimpleNet
Generation seed: 1234
NeuroMLlite parameters:
diff --git a/examples/NeuroML/SimABCD.json b/examples/NeuroML/SimABCD.json
index c4404a5a..695b19ad 100644
--- a/examples/NeuroML/SimABCD.json
+++ b/examples/NeuroML/SimABCD.json
@@ -1,6 +1,6 @@
{
"SimABCD": {
- "version": "NeuroMLlite v0.5.7",
+ "version": "NeuroMLlite v0.5.9",
"network": "ABCD.json",
"duration": 5000.0,
"dt": 100.0,
diff --git a/examples/NeuroML/SimFN.json b/examples/NeuroML/SimFN.json
index ba82f883..f1b68db9 100644
--- a/examples/NeuroML/SimFN.json
+++ b/examples/NeuroML/SimFN.json
@@ -1,6 +1,6 @@
{
"SimFN": {
- "version": "NeuroMLlite v0.5.7",
+ "version": "NeuroMLlite v0.5.9",
"network": "FN.json",
"duration": 100.0,
"dt": 0.05,
diff --git a/examples/NeuroML/SimIzhikevichTest.yaml b/examples/NeuroML/SimIzhikevichTest.yaml
index 6fe61ef2..040a7bc4 100644
--- a/examples/NeuroML/SimIzhikevichTest.yaml
+++ b/examples/NeuroML/SimIzhikevichTest.yaml
@@ -1,5 +1,5 @@
SimIzhikevichTest:
- version: NeuroMLlite v0.5.7
+ version: NeuroMLlite v0.5.9
network: IzhikevichTest.nmllite.yaml
duration: 700.0
dt: 0.025
diff --git a/examples/ONNX/ab.json b/examples/ONNX/ab.json
index 1e96f3cf..b267c60a 100644
--- a/examples/ONNX/ab.json
+++ b/examples/ONNX/ab.json
@@ -1,7 +1,7 @@
{
"ONNX Model": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"main_graph": {
"nodes": {
diff --git a/examples/ONNX/ab.yaml b/examples/ONNX/ab.yaml
index 270fa065..1fd617bf 100644
--- a/examples/ONNX/ab.yaml
+++ b/examples/ONNX/ab.yaml
@@ -1,6 +1,6 @@
ONNX Model:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
main_graph:
nodes:
diff --git a/examples/ONNX/abc.json b/examples/ONNX/abc.json
index b095362d..ad053f45 100644
--- a/examples/ONNX/abc.json
+++ b/examples/ONNX/abc.json
@@ -1,7 +1,7 @@
{
"ONNX Model": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"main_graph": {
"nodes": {
diff --git a/examples/ONNX/abc.yaml b/examples/ONNX/abc.yaml
index 6f1d0067..ca3a28b3 100644
--- a/examples/ONNX/abc.yaml
+++ b/examples/ONNX/abc.yaml
@@ -1,6 +1,6 @@
ONNX Model:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
main_graph:
nodes:
diff --git a/examples/ONNX/abc_basic-mdf.json b/examples/ONNX/abc_basic-mdf.json
index 29bdde9f..af718d46 100644
--- a/examples/ONNX/abc_basic-mdf.json
+++ b/examples/ONNX/abc_basic-mdf.json
@@ -1,18 +1,22 @@
{
"ONNX Model": {
- "format": "ModECI MDF v0.2",
- "generating_application": "Python modeci-mdf v0.2.1",
+ "format": "ModECI MDF v0.4",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
- "torch-jit-export": {
+ "main_graph": {
"nodes": {
- "Sin_0": {
+ "/A/Sin": {
"input_ports": {
"input": {
- "shape": [2, 3]
+ "shape": [
+ 2,
+ 3
+ ],
+ "type": "float"
}
},
"parameters": {
- "Sin_0": {
+ "/A/Sin": {
"function": "onnx::Sin",
"args": {
"input": "input"
@@ -20,64 +24,72 @@
}
},
"output_ports": {
- "_1": {
- "value": "Sin_0"
+ "_A_Sin_output_0": {
+ "value": "/A/Sin"
}
}
},
- "Sin_1": {
+ "/B/Sin": {
"input_ports": {
- "_1": {
- "shape": [2, 3]
+ "_A_Sin_output_0": {
+ "shape": [
+ 2,
+ 3
+ ],
+ "type": "float"
}
},
"parameters": {
- "Sin_1": {
+ "/B/Sin": {
"function": "onnx::Sin",
"args": {
- "input": "_1"
+ "input": "_A_Sin_output_0"
}
}
},
"output_ports": {
- "_2": {
- "value": "Sin_1"
+ "_B_Sin_output_0": {
+ "value": "/B/Sin"
}
}
},
- "Cos_2": {
+ "/C/Cos": {
"input_ports": {
- "_2": {
- "shape": [2, 3]
+ "_B_Sin_output_0": {
+ "shape": [
+ 2,
+ 3
+ ],
+ "type": "float"
}
},
"parameters": {
- "Cos_2": {
+ "/C/Cos": {
"function": "onnx::Cos",
"args": {
- "input": "_2"
+ "input": "_B_Sin_output_0"
}
}
},
"output_ports": {
"_3": {
- "value": "Cos_2"
+ "value": "/C/Cos"
}
}
}
},
"edges": {
- "Sin_0._1_Sin_1._1": {
- "sender": "Sin_0",
- "receiver": "Sin_1",
- "sender_port": "_1",
- "receiver_port": "_1"
+ "/A/Sin._A_Sin_output_0_/B/Sin._A_Sin_output_0": {
+ "sender": "/A/Sin",
+ "receiver": "/B/Sin",
+ "sender_port": "_A_Sin_output_0",
+ "receiver_port": "_A_Sin_output_0"
},
- "Sin_1._2_Cos_2._2": {
- "sender": "Sin_1",
- "receiver": "Cos_2",
- "sender_port": "_2",
- "receiver_port": "_2"
+ "/B/Sin._B_Sin_output_0_/C/Cos._B_Sin_output_0": {
+ "sender": "/B/Sin",
+ "receiver": "/C/Cos",
+ "sender_port": "_B_Sin_output_0",
+ "receiver_port": "_B_Sin_output_0"
}
}
}
diff --git a/examples/ONNX/abc_basic-mdf.yaml b/examples/ONNX/abc_basic-mdf.yaml
index e6e069e1..37fc162f 100644
--- a/examples/ONNX/abc_basic-mdf.yaml
+++ b/examples/ONNX/abc_basic-mdf.yaml
@@ -1,53 +1,62 @@
ONNX Model:
- format: ModECI MDF v0.2
- generating_application: Python modeci-mdf v0.2.1
+ format: ModECI MDF v0.4
+ generating_application: Python modeci-mdf v0.4.10
graphs:
- torch-jit-export:
+ main_graph:
nodes:
- Sin_0:
+ /A/Sin:
input_ports:
input:
- shape: (2, 3)
+ shape:
+ - 2
+ - 3
+ type: float
parameters:
- Sin_0:
+ /A/Sin:
function: onnx::Sin
args:
input: input
output_ports:
- _1:
- value: Sin_0
- Sin_1:
+ _A_Sin_output_0:
+ value: /A/Sin
+ /B/Sin:
input_ports:
- _1:
- shape: (2, 3)
+ _A_Sin_output_0:
+ shape:
+ - 2
+ - 3
+ type: float
parameters:
- Sin_1:
+ /B/Sin:
function: onnx::Sin
args:
- input: _1
+ input: _A_Sin_output_0
output_ports:
- _2:
- value: Sin_1
- Cos_2:
+ _B_Sin_output_0:
+ value: /B/Sin
+ /C/Cos:
input_ports:
- _2:
- shape: (2, 3)
+ _B_Sin_output_0:
+ shape:
+ - 2
+ - 3
+ type: float
parameters:
- Cos_2:
+ /C/Cos:
function: onnx::Cos
args:
- input: _2
+ input: _B_Sin_output_0
output_ports:
_3:
- value: Cos_2
+ value: /C/Cos
edges:
- Sin_0._1_Sin_1._1:
- sender: Sin_0
- receiver: Sin_1
- sender_port: _1
- receiver_port: _1
- Sin_1._2_Cos_2._2:
- sender: Sin_1
- receiver: Cos_2
- sender_port: _2
- receiver_port: _2
+ /A/Sin._A_Sin_output_0_/B/Sin._A_Sin_output_0:
+ sender: /A/Sin
+ receiver: /B/Sin
+ sender_port: _A_Sin_output_0
+ receiver_port: _A_Sin_output_0
+ /B/Sin._B_Sin_output_0_/C/Cos._B_Sin_output_0:
+ sender: /B/Sin
+ receiver: /C/Cos
+ sender_port: _B_Sin_output_0
+ receiver_port: _B_Sin_output_0
diff --git a/examples/ONNX/abcd.json b/examples/ONNX/abcd.json
index 8381cd2d..1f8bca22 100644
--- a/examples/ONNX/abcd.json
+++ b/examples/ONNX/abcd.json
@@ -1,7 +1,7 @@
{
"ONNX Model": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"main_graph": {
"nodes": {
diff --git a/examples/ONNX/abcd.yaml b/examples/ONNX/abcd.yaml
index f35230d3..98ad05ee 100644
--- a/examples/ONNX/abcd.yaml
+++ b/examples/ONNX/abcd.yaml
@@ -1,6 +1,6 @@
ONNX Model:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
main_graph:
nodes:
diff --git a/examples/PyTorch/MDF_PyTorch/ABCD.onnx b/examples/PyTorch/MDF_PyTorch/ABCD.onnx
index e8baabb3..b05f48a6 100644
Binary files a/examples/PyTorch/MDF_PyTorch/ABCD.onnx and b/examples/PyTorch/MDF_PyTorch/ABCD.onnx differ
diff --git a/examples/PyTorch/MDF_PyTorch/ABCD_pytorch.py b/examples/PyTorch/MDF_PyTorch/ABCD_pytorch.py
index ad29cc00..613b85fd 100644
--- a/examples/PyTorch/MDF_PyTorch/ABCD_pytorch.py
+++ b/examples/PyTorch/MDF_PyTorch/ABCD_pytorch.py
@@ -1,5 +1,5 @@
"""
-This script has been generated by modeci_mdf v0.4.9.
+This script has been generated by modeci_mdf v0.4.10.
It is an export of a MDF model (mdf.s - MDF stateful, i.e. full MDF allowing stateful parameters) to PyTorch
"""
diff --git a/examples/PyTorch/MDF_PyTorch/Arrays.onnx b/examples/PyTorch/MDF_PyTorch/Arrays.onnx
index 40649332..88ce0174 100644
Binary files a/examples/PyTorch/MDF_PyTorch/Arrays.onnx and b/examples/PyTorch/MDF_PyTorch/Arrays.onnx differ
diff --git a/examples/PyTorch/MDF_PyTorch/Arrays_pytorch.py b/examples/PyTorch/MDF_PyTorch/Arrays_pytorch.py
index ea751284..9969414e 100644
--- a/examples/PyTorch/MDF_PyTorch/Arrays_pytorch.py
+++ b/examples/PyTorch/MDF_PyTorch/Arrays_pytorch.py
@@ -1,5 +1,5 @@
"""
-This script has been generated by modeci_mdf v0.4.9.
+This script has been generated by modeci_mdf v0.4.10.
It is an export of a MDF model (mdf.s - MDF stateful, i.e. full MDF allowing stateful parameters) to PyTorch
"""
diff --git a/examples/PyTorch/MDF_PyTorch/Simple.onnx b/examples/PyTorch/MDF_PyTorch/Simple.onnx
index b2fe0223..6e9c440a 100644
Binary files a/examples/PyTorch/MDF_PyTorch/Simple.onnx and b/examples/PyTorch/MDF_PyTorch/Simple.onnx differ
diff --git a/examples/PyTorch/MDF_PyTorch/Simple_pytorch.py b/examples/PyTorch/MDF_PyTorch/Simple_pytorch.py
index 9335ca36..4a728b18 100644
--- a/examples/PyTorch/MDF_PyTorch/Simple_pytorch.py
+++ b/examples/PyTorch/MDF_PyTorch/Simple_pytorch.py
@@ -1,5 +1,5 @@
"""
-This script has been generated by modeci_mdf v0.4.9.
+This script has been generated by modeci_mdf v0.4.10.
It is an export of a MDF model (mdf.s - MDF stateful, i.e. full MDF allowing stateful parameters) to PyTorch
"""
diff --git a/examples/PyTorch/inception.json b/examples/PyTorch/inception.json
index 67db0417..5bd31bad 100644
--- a/examples/PyTorch/inception.json
+++ b/examples/PyTorch/inception.json
@@ -1,7 +1,7 @@
{
"InceptionBlocks": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"InceptionBlocksGraph": {
"nodes": {
diff --git a/examples/PyTorch/mlp_pure_mdf.json b/examples/PyTorch/mlp_pure_mdf.json
index fc1e251c..ab922c19 100644
--- a/examples/PyTorch/mlp_pure_mdf.json
+++ b/examples/PyTorch/mlp_pure_mdf.json
@@ -1,7 +1,7 @@
{
"mlp_pure_mdf": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"mlp_pure_mdf": {
"nodes": {
diff --git a/examples/PyTorch/mlp_pure_mdf.yaml b/examples/PyTorch/mlp_pure_mdf.yaml
index d412b2ab..2c031141 100644
--- a/examples/PyTorch/mlp_pure_mdf.yaml
+++ b/examples/PyTorch/mlp_pure_mdf.yaml
@@ -1,6 +1,6 @@
mlp_pure_mdf:
format: ModECI MDF v0.4
- generating_application: Python modeci-mdf v0.4.9
+ generating_application: Python modeci-mdf v0.4.10
graphs:
mlp_pure_mdf:
nodes:
diff --git a/examples/PyTorch/simple_pytorch_to_mdf.json b/examples/PyTorch/simple_pytorch_to_mdf.json
index 98cc593b..759a7c5f 100644
--- a/examples/PyTorch/simple_pytorch_to_mdf.json
+++ b/examples/PyTorch/simple_pytorch_to_mdf.json
@@ -1,7 +1,7 @@
{
"SimpleNet": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"SimpleNetGraph": {
"nodes": {
diff --git a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.json b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.json
index cefe532a..38fa64a3 100644
--- a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.json
+++ b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.json
@@ -1,7 +1,7 @@
{
"Sequential": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"Sequential_graph": {
"nodes": {
@@ -206,177 +206,177 @@
"wgts": {
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diff --git a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.png b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.png
index c6a177fe..16fefd28 100644
Binary files a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.png and b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.png differ
diff --git a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.yaml b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.yaml
index ceb3053c..f0a72479 100644
--- a/examples/TensorFlow/Keras/IRIS/keras_to_MDF.yaml
+++ b/examples/TensorFlow/Keras/IRIS/keras_to_MDF.yaml
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Sequential:
format: ModECI MDF v0.4
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+ generating_application: Python modeci-mdf v0.4.10
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linear:
value: (First_layer_in @ wgts) + bias
Output:
@@ -312,2120 +312,2120 @@ Sequential:
parameters:
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linear:
value: (Second_layer_in @ wgts) + bias
Output:
@@ -2439,453 +2439,453 @@ Sequential:
parameters:
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linear:
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linear:
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output_ports:
diff --git a/examples/TensorFlow/Keras/IRIS/model_on_iris_plot.png b/examples/TensorFlow/Keras/IRIS/model_on_iris_plot.png
index fdfed712..9b0cde3d 100644
Binary files a/examples/TensorFlow/Keras/IRIS/model_on_iris_plot.png and b/examples/TensorFlow/Keras/IRIS/model_on_iris_plot.png differ
diff --git a/examples/TensorFlow/Keras/MNIST/keras_to_MDF.json b/examples/TensorFlow/Keras/MNIST/keras_to_MDF.json
index c5eaba1d..d44cb2fa 100644
--- a/examples/TensorFlow/Keras/MNIST/keras_to_MDF.json
+++ b/examples/TensorFlow/Keras/MNIST/keras_to_MDF.json
@@ -1,7 +1,7 @@
{
"Sequential": {
"format": "ModECI MDF v0.4",
- "generating_application": "Python modeci-mdf v0.4.9",
+ "generating_application": "Python modeci-mdf v0.4.10",
"graphs": {
"Sequential_graph": {
"nodes": {
@@ -18444,523 +18444,523 @@
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