max_pool3d_layer_dense_batch.py

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 # file: max_pool3d_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 neural network forward and backward three-dimensional maximum pooling layers usage
 # !
 # !*****************************************************************************
 
 #
 ## <a name="DAAL-EXAMPLE-PY-MAXIMUM_POOLING3D_LAYER_BATCH"></a>
 ## \example max_pool3d_layer_dense_batch.py
 #
 
 import os
 import sys
 
 import numpy as np
 
 from daal.algorithms.neural_networks import layers
 from daal.data_management import HomogenTensor
 
 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 printTensor3d
 
 nDim = 3
 dims = [3, 2, 4]
 dataArray = np.array([[[1,  2,  3,  4],
                        [5,  6,  7,  8]],
                       [[9, 10, 11, 12],
                        [13, 14, 15, 16]],
                       [[17, 18, 19, 20],
                        [21, 22, 23, 24]]],
                      dtype=np.float64)
 
 if __name__ == "__main__":
 
     dataTensor = HomogenTensor(dataArray)
 
     printTensor3d(dataTensor, "Forward maximum pooling layer input:")
 
     # Create an algorithm to compute forward pooling layer results using maximum method
     forwardLayer = layers.maximum_pooling3d.forward.Batch(nDim)
     forwardLayer.input.setInput(layers.forward.data, dataTensor)
 
     # Compute forward pooling layer results
     forwardResult = forwardLayer.compute()
 
     printTensor3d(forwardResult.getResult(layers.forward.value), "Forward maximum pooling layer result:")
     printTensor3d(forwardResult.getLayerData(layers.maximum_pooling3d.auxSelectedIndices),
                   "Forward maximum pooling layer selected indices:")
 
     # Create an algorithm to compute backward pooling layer results using maximum method
     backwardLayer = layers.maximum_pooling3d.backward.Batch(nDim)
     backwardLayer.input.setInput(layers.backward.inputGradient, forwardResult.getResult(layers.forward.value))
     backwardLayer.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
 
     # Compute backward pooling layer results
     backwardResult = backwardLayer.compute()
 
     # Print the computed backward pooling layer results
     printTensor3d(backwardResult.getResult(layers.backward.gradient), "Backward maximum pooling layer result:")
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