Python* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

fullycon_layer_dense_batch.py

1 # file: fullycon_layer_dense_batch.py
2 #===============================================================================
3 # Copyright 2014-2019 Intel Corporation.
4 #
5 # This software and the related documents are Intel copyrighted materials, and
6 # your use of them is governed by the express license under which they were
7 # provided to you (License). Unless the License provides otherwise, you may not
8 # use, modify, copy, publish, distribute, disclose or transmit this software or
9 # the related documents without Intel's prior written permission.
10 #
11 # This software and the related documents are provided as is, with no express
12 # or implied warranties, other than those that are expressly stated in the
13 # License.
14 #===============================================================================
15 
16 #
17 # ! Content:
18 # ! Python example of forward and backward fully-connected layer usage
19 # !
20 # !*****************************************************************************
21 
22 #
23 
24 
25 #
26 
27 import os
28 import sys
29 
30 from daal.algorithms.neural_networks import layers
31 from daal.data_management import HomogenTensor, TensorIface
32 
33 utils_folder = os.path.realpath(os.path.abspath(os.path.dirname(os.path.dirname(__file__))))
34 if utils_folder not in sys.path:
35  sys.path.insert(0, utils_folder)
36 from utils import printTensor, readTensorFromCSV
37 
38 # Input data set parameters
39 datasetName = os.path.join("..", "data", "batch", "layer.csv")
40 
41 if __name__ == "__main__":
42 
43  k = 0
44  m = 5
45  # Read datasetFileName from a file and create a tensor to store input data
46  tensorData = readTensorFromCSV(datasetName)
47 
48  # Create an algorithm to compute forward fully-connected layer results using default method
49  fullyconnectedLayerForward = layers.fullyconnected.forward.Batch(m)
50  fullyconnectedLayerForward.parameter.dim = k
51 
52  # Set input objects for the forward fully-connected layer
53  fullyconnectedLayerForward.input.setInput(layers.forward.data, tensorData)
54 
55  # Compute forward fully-connected layer results
56  forwardResult = fullyconnectedLayerForward.compute()
57 
58  # Print the results of the forward fully-connected layer
59  printTensor(forwardResult.getResult(layers.forward.value),
60  "Forward fully-connected layer result (first 5 rows):", 5)
61  printTensor(forwardResult.getLayerData(layers.fullyconnected.auxWeights),
62  "Forward fully-connected layer weights (first 5 rows):", 5)
63 
64  # Get the size of forward fully-connected layer output
65  gDims = forwardResult.getResult(layers.forward.value).getDimensions()
66  tensorDataBack = HomogenTensor(gDims, TensorIface.doAllocate, 0.01)
67 
68  # Create an algorithm to compute backward fully-connected layer results using default method
69  fullyconnectedLayerBackward = layers.fullyconnected.backward.Batch(m)
70 
71  # Set input objects for the backward fully-connected layer
72  fullyconnectedLayerBackward.input.setInput(layers.backward.inputGradient, tensorDataBack)
73  fullyconnectedLayerBackward.input.setInputLayerData(layers.backward.inputFromForward, forwardResult.getResultLayerData(layers.forward.resultForBackward))
74 
75  # Compute backward fully-connected layer results
76  backwardResult = fullyconnectedLayerBackward.compute()
77 
78  # Print the results of the backward fully-connected layer
79  printTensor(backwardResult.getResult(layers.backward.gradient),
80  "Backward fully-connected layer gradient result (first 5 rows):", 5)
81  printTensor(backwardResult.getResult(layers.backward.weightDerivatives),
82  "Backward fully-connected layer weightDerivative result (first 5 rows):", 5)
83  printTensor(backwardResult.getResult(layers.backward.biasDerivatives),
84  "Backward fully-connected layer biasDerivative result (first 5 rows):", 5)

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