spat_stoch_pool2d_layer_dense_batch.py

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 # file: spat_stoch_pool2d_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.
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 # 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 two-dimensional spatial pyramid stochastic pooling layers usage
 # !
 # !*****************************************************************************
 
 #
 ## <a name="DAAL-EXAMPLE-PY-SPAT_STOCH_POOL2D_LAYER_DENSE_BATCH"></a>
 ## \example spat_stoch_pool2d_layer_dense_batch.py
 #
 
 import os
 import sys
 
 import numpy as np
 
 from daal.algorithms.neural_networks import layers
 from daal.algorithms.neural_networks.layers import spatial_stochastic_pooling2d
 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 printTensor
 
 nDim = 4
 dims = [2, 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]]],
                                          [[[10,  20,  30,  40],
                                            [50,  60,  70,  80]],
                                           [[90, 100, 110, 120],
                                            [130, 140, 150, 160]],
                                           [[170, 180, 190, 200],
                                            [210, 220, 230, 240]]]],
                      dtype=np.float64)
 
 if __name__ == "__main__":
     data = HomogenTensor(dataArray)
 
     printTensor(data, "Forward two-dimensional spatial pyramid stochastic pooling layer input (first 10 rows):", 10)
 
     # Create an algorithm to compute forward two-dimensional spatial pyramid stochastic pooling layer results using default method
     forwardLayer = spatial_stochastic_pooling2d.forward.Batch(2, nDim)
     forwardLayer.input.setInput(layers.forward.data, data)
 
     # Compute forward two-dimensional spatial pyramid stochastic pooling layer results
     forwardLayer.compute()
 
     # Get the computed forward two-dimensional spatial pyramid stochastic pooling layer results
     forwardResult = forwardLayer.getResult()
 
     printTensor(forwardResult.getResult(layers.forward.value), "Forward two-dimensional spatial pyramid stochastic pooling layer result (first 5 rows):", 5)
     printTensor(forwardResult.getLayerData(layers.spatial_stochastic_pooling2d.auxSelectedIndices),
                 "Forward two-dimensional spatial pyramid stochastic pooling layer selected indices (first 10 rows):", 10)
 
     # Create an algorithm to compute backward two-dimensional spatial pyramid stochastic pooling layer results using default method
     backwardLayer = layers.spatial_stochastic_pooling2d.backward.Batch(2, 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 two-dimensional spatial pyramid stochastic pooling layer results
     backwardLayer.compute()
 
     # Get the computed backward two-dimensional spatial pyramid stochastic pooling layer results
     backwardResult = backwardLayer.getResult()
 
     printTensor(backwardResult.getResult(layers.backward.gradient),
                 "Backward two-dimensional spatial pyramid stochastic pooling layer result (first 10 rows):", 10)
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