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Quick Example on Intel CPU and GPU

Installation

Code

Use TensorFlow to compute graph: Conv -> ReLU activation -> Bias

quick_example.py

importnumpyasnpimportsysimporttensorflowastf# Conv + ReLU activation + BiasN=1num_channel=3input_width, input_height= (5, 5) filter_width, filter_height= (2, 2) x=np.random.rand(N, input_width, input_height, num_channel).astype(np.float32) weight=np.random.rand(filter_width, filter_height, num_channel, num_channel).astype(np.float32) bias=np.random.rand(num_channel).astype(np.float32) conv=tf.nn.conv2d(x, weight, strides=[1, 1, 1, 1], padding='SAME') activation=tf.nn.relu(conv) result=tf.nn.bias_add(activation, bias) print(result) print('Finished')
  • Execute the Code
python quick_example.py

Example Output

With successful execution, it will print out the following results:

... tf.Tensor( [[[[3.479142 2.7296917 4.6456823 ] [4.077278 3.9259825 5.3000765 ] [3.3999124 3.0527704 4.0656753 ] [2.85485 2.7297122 3.9373732 ] [2.4818356 2.1455178 2.4929404 ]] [[3.6422923 2.718459 4.7090344 ] [3.988714 3.3391027 4.875052 ] [3.6461415 2.9349675 4.327398 ] [3.298973 2.3905785 4.1704025 ] [1.9154005 1.6926193 1.9677248 ]] [[3.481086 2.9746864 3.8941312 ] [3.3221133 2.5479512 4.197306 ] [3.305706 2.9873173 4.5597944 ] [3.250221 3.118212 3.8672705 ] [1.949225 1.2636094 1.5300783 ]] [[3.1403804 2.1729176 3.6628485 ] [3.2607155 2.6342418 3.9381838 ] [2.6761076 2.5063303 3.4718971 ] [2.8880196 2.1658201 3.3787665 ] [2.1193419 1.42261 2.318963 ]] [[1.8809638 1.6514435 2.3549364 ] [1.8598063 1.517385 1.9702091 ] [1.9260886 1.3804817 2.381424 ] [1.6027272 1.7787259 1.9631021 ] [0.93901324 1.2134862 0.89942324]]]], shape=(1, 5, 5, 3), dtype=float32) Finished

Notes

  1. In this example, it is not necessary to import intel_extension_for_tensorflow, and no need to call any of its APIs.
    If installed as the intel-extension-for-tensorflow[cpu], then the script will choose CPU as the backend and be executed on the CPU automatically; while if installed as intel-extension-for-tensorflow[xpu], then the default backend will be GPU and the script will be executed on the GPU.
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