smoothrelu_layer_dense_batch.py

Deprecation Notice: With the introduction of daal4py, a package that supersedes PyDAAL, Intel is deprecating PyDAAL and will discontinue support starting with Intel® DAAL 2021 and Intel® Distribution for Python 2021. Until then Intel will continue to provide compatible pyDAAL pip and conda packages for newer releases of Intel DAAL and make it available in open source. However, Intel will not add the new features of Intel DAAL to pyDAAL. Intel recommends developers switch to and use daal4py.

Note: To find daal4py examples, refer to daal4py documentation or browse github repository.

 # file: smoothrelu_layer_dense_batch.py
 #===============================================================================
 # Copyright 2014-2019 Intel Corporation.
 #
 # This software and the related documents are Intel copyrighted  materials,  and
 # your use of  them is  governed by the  express license  under which  they were
 # provided to you (License).  Unless the License provides otherwise, you may not
 # use, modify, copy, publish, distribute,  disclose or transmit this software or
 # the related documents without Intel's prior written permission.
 #
 # This software and the related documents  are provided as  is,  with no express
 # or implied  warranties,  other  than those  that are  expressly stated  in the
 # License.
 #===============================================================================
 
 #
 # !  Content:
 # !    Python example of forward and backward smooth rectified linear unit (smooth relu) layer usage
 # !
 # !*****************************************************************************
 
 #
 ## <a name="DAAL-EXAMPLE-PY-SMOOTHRELU_LAYER_BATCH"></a>
 ## \example smoothrelu_layer_dense_batch.py
 #
 
 import os
 import sys
 
 from daal.algorithms.neural_networks import layers
 from daal.algorithms.neural_networks.layers import smoothrelu
 from daal.data_management import HomogenTensor, TensorIface
 
 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
 if utils_folder not in sys.path:
     sys.path.insert(0, utils_folder)
 from utils import printTensor, readTensorFromCSV
 
 # Input data set parameters
 datasetName = os.path.join("..", "data", "batch", "layer.csv")
 
 if __name__ == "__main__":
 
     # Read datasetFileName from a file and create a tensor to store input data
     tensorData = readTensorFromCSV(datasetName)
 
     # Create an algorithm to compute forward smooth relu layer results using default method
     smoothreluLayerForward = smoothrelu.forward.Batch()
 
     # Set input objects for the forward smooth relu layer
     smoothreluLayerForward.input.setInput(layers.forward.data, tensorData)
 
     # Compute forward smooth relu layer results
     forwardResult = smoothreluLayerForward.compute()
 
     # Print the results of the forward smooth relu layer
     printTensor(forwardResult.getResult(layers.forward.value), "Forward smooth ReLU layer result (first 5 rows):", 5)
 
     # Get the size of forward dropout smooth relu output
     gDims = forwardResult.getResult(layers.forward.value).getDimensions()
     tensorDataBack = HomogenTensor(gDims, TensorIface.doAllocate, 0.01)
 
     # Create an algorithm to compute backward smooth relu layer results using default method
     smoothreluLayerBackward = smoothrelu.backward.Batch()
 
     # Set input objects for the backward smooth relu layer
     smoothreluLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
     smoothreluLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
 
     # Compute backward smooth relu layer results
     backwardResult = smoothreluLayerBackward.compute()
 
     # Print the results of the backward smooth relu layer
     printTensor(backwardResult.getResult(layers.backward.gradient), "Backward smooth ReLU layer result (first 5 rows):", 5)
For more complete information about compiler optimizations, see our Optimization Notice.
Select sticky button color: 
Orange (only for download buttons)