Update README.md

README.md

@@ -20,7 +20,7 @@ Self-Created Tools to convert ONNX files (NCHW) to TensorFlow/TFLite/Keras forma
 - [x] All OPs are decomposed into primitive operations as much as possible. This is beneficial for lateral deployment of models to frameworks other than TFLite. Therefore, OPs belonging to `tf.keras.layers` are almost never used, and the tool consists only of `tf.xxx`. (except for a very few OPs)
 - [x] Not only does it handle conversions of 4-dimensional inputs, such as `NCHW` to `NHWC`, but also the number of input dimensions in 3, 5, or even more dimensions. For example, `NCDHW` to `NDHWC`, etc. However, since 1-D, 2-D, 3-D and 6-D input may produce patterns that are mechanically difficult to convert, it should be possible to give parameters to externally modify the tool's behavior. See [Parameter replacement](#parameter-replacement)
 - [x] If there are undefined dimensions in the input OP, the model structure is not fully optimized and conversion errors are very likely to occur.
-- [x] Immediately following a `Reshape` OP with dimensional compression and dimensional decompression, there is a 95% probability that the model transformation operation will be disrupted and errors will occur. For example, patterns such as `[1,200,200,5]` -> `[1,200,-1]` or `[10,20,30,40,50]` -> `[10,2,10,30,10,4,50]` or `Flatten`. See [#8 Not able to reshape input in replace.json](https://github.com/PINTO0309/onnx2tf/issues/8)
+- [x] Immediately following a `Reshape` OP with dimensional compression and dimensional decompression, there is a 95% probability that the model transformation operation will be disrupted and errors will occur. For example, patterns such as `[1,200,200,5]` -> `[1,200,-1]` or `[10,20,30,40,50]` -> `[10,2,10,30,10,4,50]` or `Flatten`. See [#8 Not able to reshape input in replace.json](https://github.com/PINTO0309/onnx2tf/issues/8), or [#15 Conv layer shape wrong](https://github.com/PINTO0309/onnx2tf/issues/15)
 - [x] TensorFlow's Convolution does not have an equivalent operation to ONNX's Padding operation. Therefore, a `Pad` OP is inserted immediately before a Convolution with Padding of size greater than 1.
 - [x] Support conversion to TensorFlow saved model and TFLite (Float32/Float16/INT8).
 - [x] Files exceeding the Protocol Buffers file size limit of 2GB are not supported. Therefore, the external format is not supported at the initial stage of tool creation.