Pcs

README.rst

@@ -42,7 +42,7 @@ It can also be used with a python environment in the following manner:
     from keras2c import k2c
     k2c(model, function_name, malloc=False, num_tests=10, verbose=True)
 
-For more information, see `Installation <https://f0uriest.github.io/keras2c/installation.html>`_ and  `Usage <https://f0uriest.github.io/keras2c/usage.html>`_
+For more information, see `Installation <https://plasmacontrol.github.io/keras2c/installation.html>`_ and  `Usage <https://plasmacontrol.github.io/keras2c/usage.html>`_
 
 
 Supported Layers
@@ -73,24 +73,24 @@ ToDo
 Contribute
 **********
 
-- Documentation: `<https://f0uriest.github.io/keras2c/>`_
-- Issue Tracker: `<https://github.com/f0uriest/keras2c/issues>`_
-- Source Code: `<https://github.com/f0uriest/keras2c/>`_
+- Documentation: `<https://plasmacontrol.github.io/keras2c/>`_
+- Issue Tracker: `<https://github.com/plasmacontrol/keras2c/issues>`_
+- Source Code: `<https://github.com/plasmacontrol/keras2c/>`_
 
 License
 *******
 
 The project is licensed under the LGPLv3 license.
 
 
-.. |Build-Status| image:: https://travis-ci.org/f0uriest/keras2c.svg?branch=master
-    :target: https://travis-ci.org/f0uriest/keras2c
+.. |Build-Status| image:: https://travis-ci.org/plasmacontrol/keras2c.svg?branch=master
+    :target: https://travis-ci.org/plasmacontrol/keras2c
     :alt: Build Status
-.. |Codecov| image:: https://codecov.io/gh/f0uriest/keras2c/branch/master/graph/badge.svg
-    :target: https://codecov.io/gh/f0uriest/keras2c
+.. |Codecov| image:: https://codecov.io/gh/plasmacontrol/keras2c/branch/master/graph/badge.svg
+    :target: https://codecov.io/gh/plasmacontrol/keras2c
     :alt: Code Coverage
-.. |License| image:: https://img.shields.io/github/license/f0uriest/keras2c
-    :target: https://github.com/f0uriest/keras2c/blob/master/LICENSE
+.. |License| image:: https://img.shields.io/github/license/plasmacontrol/keras2c
+    :target: https://github.com/plasmacontrol/keras2c/blob/master/LICENSE
     :alt: License: LGPLv3
 .. |DOI| image:: https://zenodo.org/badge/193152058.svg
     :target: https://zenodo.org/badge/latestdoi/193152058

include/k2c_activations.c

@@ -116,6 +116,24 @@ void k2c_sigmoid_func(float * x, const size_t size) {
 }
 k2c_activationType * k2c_sigmoid = k2c_sigmoid_func;
 
+/**
+ * swish activation function.
+ *   y = x * (1/(1+exp(-x)))
+ *
+ * :param x: array of input values. Gets overwritten by output.
+ * :param size: length of input array.
+ */
+void k2c_swish_func(float * x, const size_t size) {
+
+    for (size_t i = 0; i < size; ++i) {
+        float xv = x[i];
+        float v = xv;
+        if (v < -30.0f) v = -30.0f; // Clamp to avoid overflow
+        x[i] = xv / (1.0f + expf(-v));
+    }
+}
+k2c_activationType * k2c_swish = k2c_swish_func;
+
 
 /**
  * Soft max activation function.

include/k2c_conv_transpose_layer.c

@@ -0,0 +1,73 @@
+#include <string.h>
+#include "k2c_include.h"
+
+/**
+ * 1D (temporal) Convolution.
+ * Assumes a "channels last" structure.
+ *
+ * :param output: output tensor.
+ * :param input: input tensor.
+ * :param kernel: kernel tensor.
+ * :param bias: bias tensor.
+ * :param stride: stride length of the convolution.
+ * :param dilation: dilation rate to use for dilated convolution.
+ * :param activation: activation function to apply to output.
+ */
+void k2c_conv1d_transpose(k2c_tensor *output, const k2c_tensor *input,
+                          const k2c_tensor *kernel, const k2c_tensor *bias,
+                          const size_t stride, const size_t start_crop,
+                          k2c_activationType *activation)
+{
+    memset(output->array, 0, output->numel * sizeof(output->array[0]));
+
+    const size_t n_height = input->shape[0];
+    const size_t n_channels = input->shape[1];
+    const size_t k_size = kernel->shape[0];
+    const size_t n_filters = kernel->shape[1];
+    const size_t out_height = output->shape[0];
+
+    const size_t ker_dim12 = n_channels * n_filters;
+
+    size_t cs = 0;
+    size_t ce = 0;
+    size_t ts = 0;
+    size_t ks = 0;
+
+    for (size_t f = 0; f < n_filters; ++f)
+    {
+        for (size_t ch = 0; ch < n_channels; ++ch)
+        {
+            for (size_t t = 0; t < n_height; ++t)
+            {
+                ts = t * stride;
+                if (ts > start_crop)
+                {
+                    cs = ts - start_crop;
+                }
+                else
+                {
+                    cs = 0;
+                }
+                if (ts + k_size - start_crop > out_height)
+                {
+                    ce = out_height;
+                }
+                else
+                {
+                    ce = ts + k_size - start_crop;
+                }
+                ks = cs - (ts - start_crop);
+                for (size_t i = 0; i < ce - cs; ++i)
+                {
+                    output->array[(i + cs) * n_filters + f] +=
+                        kernel->array[(i + ks) * ker_dim12 + f * n_channels + ch] *
+                        input->array[t * n_channels + ch];
+                }
+            }
+        }
+    }
+    // }
+
+    k2c_bias_add(output, bias);
+    activation(output->array, output->numel);
+}