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<a href="_tensor_flow_support_8md.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span>&#160;# TensorFlow operators that the Arm NN SDK supports</div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span>&#160;</div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span>&#160;This reference guide provides a list of TensorFlow operators the Arm NN SDK currently supports.</div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span>&#160;</div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span>&#160;The Arm NN SDK TensorFlow parser currently only supports fp32 operators.</div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span>&#160;</div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span>&#160;## Fully supported</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span>&#160;</div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span>&#160;**avg_pool**</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span>&#160;</div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span>&#160;See the TensorFlow [avg_pool documentation](https://www.tensorflow.org/api_docs/python/tf/nn/avg_pool) for more information.</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span>&#160;</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>&#160;**bias_add**</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>&#160;</div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>&#160;See the TensorFlow [bias_add documentation](https://www.tensorflow.org/api_docs/python/tf/nn/bias_add) for more information.</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>&#160;</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>&#160;**conv2d**</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>&#160;</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>&#160;See the TensorFlow [conv2d documentation](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d) for more information.</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>&#160;</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>&#160;**expand_dims**</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>&#160;</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>&#160;See the TensorFlow [expand_dims documentation](https://www.tensorflow.org/api_docs/python/tf/expand_dims) for more information.</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>&#160;</div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>&#160;**gather**</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>&#160;</div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>&#160;See the TensorFlow [gather documentation](https://www.tensorflow.org/api_docs/python/tf/gather) for more information.</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>&#160;</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span>&#160;**identity**</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>&#160;</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>&#160;See the TensorFlow [identity documentation](https://www.tensorflow.org/api_docs/python/tf/identity) for more information.</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>&#160;</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>&#160;**local_response_normalization**</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>&#160;</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>&#160;See the TensorFlow [local_response_normalization documentation](https://www.tensorflow.org/api_docs/python/tf/nn/local_response_normalization) for more information.</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>&#160;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>&#160;**max_pool**</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>&#160;</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>&#160;See the TensorFlow [max_pool documentation](https://www.tensorflow.org/api_docs/python/tf/nn/max_pool) for more information.</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>&#160;</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>&#160;**placeholder**</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>&#160;</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>&#160;See the TensorFlow [placeholder documentation](https://www.tensorflow.org/api_docs/python/tf/placeholder) for more information.</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>&#160;</div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>&#160;**reduce_mean**</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>&#160;</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>&#160;See the TensorFlow [reduce_mean documentation](https://www.tensorflow.org/api_docs/python/tf/reduce_mean) for more information.</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>&#160;</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span>&#160;**relu**</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>&#160;</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>&#160;See the TensorFlow [relu documentation](https://www.tensorflow.org/api_docs/python/tf/nn/relu) for more information.</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>&#160;</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>&#160;**relu6**</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>&#160;</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>&#160;See the TensorFlow [relu6 documentation](https://www.tensorflow.org/api_docs/python/tf/nn/relu6) for more information.</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>&#160;</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>&#160;**rsqrt**</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>&#160;</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>&#160;See the TensorFlow [rsqrt documentation](https://www.tensorflow.org/api_docs/python/tf/math/rsqrt) for more information.</div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>&#160;</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>&#160;**shape**</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>&#160;</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>&#160;See the TensorFlow [shape documentation](https://www.tensorflow.org/api_docs/python/tf/shape) for more information.</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>&#160;</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>&#160;**sigmoid**</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>&#160;</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>&#160;See the TensorFlow [sigmoid documentation](https://www.tensorflow.org/api_docs/python/tf/sigmoid) for more information.</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>&#160;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>&#160;**softplus**</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>&#160;</div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>&#160;See the TensorFlow [softplus documentation](https://www.tensorflow.org/api_docs/python/tf/nn/softplus) for more information.</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>&#160;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>&#160;**squeeze**</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>&#160;</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>&#160;See the TensorFlow [squeeze documentation](https://www.tensorflow.org/api_docs/python/tf/squeeze) for more information.</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>&#160;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>&#160;**tanh**</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>&#160;</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>&#160;See the TensorFlow [tanh documentation](https://www.tensorflow.org/api_docs/python/tf/tanh) for more information.</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>&#160;</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>&#160;## Partially supported</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>&#160;</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>&#160;**add**</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>&#160;</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>&#160;The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of scalars and 1D tensors. See the TensorFlow [add operator documentation](https://www.tensorflow.org/api_docs/python/tf/add) for more information.</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>&#160;</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>&#160;**add_n**</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>&#160;</div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>&#160;The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of scalars and 1D tensors. See the TensorFlow [add operator documentation](https://www.tensorflow.org/api_docs/python/tf/add_n) for more information.</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>&#160;</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>&#160;**concat**</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>&#160;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>&#160;Arm NN supports concatenation along the channel dimension for data formats NHWC and NCHW.</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>&#160;</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>&#160;**constant**</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>&#160;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>&#160;The parser does not support the optional `shape` argument. It always infers the shape of the output tensor from `value`. See the TensorFlow [constant documentation](https://www.tensorflow.org/api_docs/python/tf/constant) for further information.</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>&#160;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>&#160;**depthwise_conv2d_native**</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>&#160;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>&#160;The parser only supports a dilation rate of (1,1,1,1). See the TensorFlow [depthwise_conv2d_native documentation](https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d_native) for more information.</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>&#160;</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>&#160;**equal**</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>&#160;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>&#160;The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of 4D and 1D tensors. See the TensorFlow [equal operator documentation](https://www.tensorflow.org/api_docs/python/tf/math/equal) for more information.</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>&#160;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>&#160;**fused_batch_norm**</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>&#160;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>&#160;The parser does not support training outputs. See the TensorFlow [fused_batch_norm documentation](https://www.tensorflow.org/api_docs/python/tf/nn/fused_batch_norm) for more information.</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>&#160;</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>&#160;**greater**</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>&#160;</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>&#160;The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of 4D and 1D tensors. See the TensorFlow [greater operator documentation](https://www.tensorflow.org/api_docs/python/tf/math/greater) for more information.</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>&#160;</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>&#160;**matmul**</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>&#160;</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>&#160;The parser only supports constant weights in a fully connected layer. See the TensorFlow [matmul documentation](https://www.tensorflow.org/api_docs/python/tf/matmul) for more information.</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>&#160;</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>&#160;**maximum**</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>&#160;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>&#160;where maximum is used in one of the following ways</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>&#160;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>&#160;* max(mul(a, x), x)</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>&#160;* max(mul(x, a), x)</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>&#160;* max(x, mul(a, x))</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>&#160;* max(x, mul(x, a)</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span>&#160;</div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>&#160;This is interpreted as a ActivationLayer with a LeakyRelu activation function. Any other usage of max will result in the insertion of a simple maximum layer. The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting). See the TensorFlow [maximum documentation](https://www.tensorflow.org/api_docs/python/tf/maximum) for more information.</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>&#160;</div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>&#160;**minimum**</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>&#160;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>&#160;The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of 4D and 1D tensors. See the TensorFlow [minimum operator documentation](https://www.tensorflow.org/api_docs/python/tf/math/minimum) for more information.</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>&#160;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>&#160;**multiply**</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>&#160;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>&#160;The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of scalars and 1D tensors. See the TensorFlow [multiply documentation](https://www.tensorflow.org/api_docs/python/tf/multiply) for more information.</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>&#160;</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>&#160;**pad**</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>&#160;</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>&#160;Only supports tf.pad function with mode = &#39;CONSTANT&#39; and constant_values = 0. See the TensorFlow [pad documentation](https://www.tensorflow.org/api_docs/python/tf/pad) for more information.</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>&#160;</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>&#160;**realdiv**</div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>&#160;</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>&#160;The parser does not support all forms of [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of scalars and 1D tensors. See the TensorFlow [realdiv documentation](https://www.tensorflow.org/api_docs/python/tf/realdiv) for more information.</div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>&#160;</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>&#160;**reshape**</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>&#160;</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>&#160;The parser does not support reshaping to or from 4D. See the TensorFlow [reshape documentation](https://www.tensorflow.org/api_docs/python/tf/reshape) for more information.</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>&#160;</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>&#160;**resize_images**</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>&#160;</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span>&#160;The parser only supports `ResizeMethod.BILINEAR` with `align_corners=False`. See the TensorFlow [resize_images documentation](https://www.tensorflow.org/api_docs/python/tf/image/resize_images) for more information.</div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>&#160;</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>&#160;**softmax**</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>&#160;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>&#160;The parser only supports 2D inputs and does not support selecting the `softmax` dimension. See the TensorFlow [softmax documentation](https://www.tensorflow.org/api_docs/python/tf/nn/softmax) for more information.</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span>&#160;</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span>&#160;**split**</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>&#160;</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>&#160;Arm NN supports split along the channel dimension for data formats NHWC and NCHW.</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>&#160;</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>&#160;**subtract**</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>&#160;</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>&#160;The parser does not support all forms of broadcasting [broadcast composition](https://www.tensorflow.org/performance/xla/broadcasting), only broadcasting of scalars and 1D tensors. See the TensorFlow [subtract documentation](https://www.tensorflow.org/api_docs/python/tf/math/subtract) for more information.</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>&#160;</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>&#160;**pack/stack**</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>&#160;</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>&#160;See the TensorFlow [stack documentation](https://www.tensorflow.org/api_docs/python/tf/stack) for more information.</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>&#160;</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>&#160;**strided_slice**</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>&#160;</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>&#160;See the TensorFlow [strided_slice documentation](https://www.tensorflow.org/api_docs/python/tf/strided_slice) for more information.</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>&#160;</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>&#160;## Tested networks</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>&#160;</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>&#160;Arm tests these operators with the following TensorFlow fp32 neural networks:</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>&#160;</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>&#160;* Cifar10</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>&#160;</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>&#160;* Lenet</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>&#160;</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>&#160;* Simple MNIST. For more information check out the [tutorial](https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/deploying-a-tensorflow-mnist-model-on-arm-nn) on the Arm Developer portal.</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>&#160;</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>&#160;* mobilenet_v1_1.0_224. The Arm NN SDK only supports the non-quantized version of the network. See the [MobileNet_v1 documentation](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md) for more information on quantized networks.</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>&#160;</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>&#160;* inception_v3. The Arm NN SDK only supports the official inception_v3 transformed model. See the TensorFlow documentation on [preparing models for mobile deployment](https://www.tensorflow.org/mobile/prepare_models) for more information on how to transform the inception_v3 network.</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>&#160;</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>&#160;More machine learning operators will be supported in future releases.</div></div><!-- fragment --></div><!-- contents -->
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