# file: neural_net_dense_batch.py
#===============================================================================
# Copyright 2014-2018 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
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# License.
#===============================================================================
#
# ! Content:
# ! Python example of neural network training and scoring
# !*****************************************************************************
#
## <a name="DAAL-EXAMPLE-PY-NEURAL_NET_DENSE_BATCH"></a>
## example neural_net_dense_batch.py
#
import os
import sys
import numpy as np
from daal.algorithms.neural_networks import initializers
from daal.algorithms.neural_networks import layers
from daal.algorithms import optimization_solver
from daal.algorithms.neural_networks import training, prediction
from daal.data_management import NumericTable, HomogenNumericTable
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 printTensors, readTensorFromCSV
# Input data set parameters
trainDatasetFile = os.path.join("..", "data", "batch", "neural_network_train.csv")
trainGroundTruthFile = os.path.join("..", "data", "batch", "neural_network_train_ground_truth.csv")
testDatasetFile = os.path.join("..", "data", "batch", "neural_network_test.csv")
testGroundTruthFile = os.path.join("..", "data", "batch", "neural_network_test_ground_truth.csv")
fc1 = 0
fc2 = 1
sm1 = 2
batchSize = 10
def configureNet():
# Create layers of the neural network
# Create fully-connected layer and initialize layer parameters
fullyConnectedLayer1 = layers.fullyconnected.Batch(5)
fullyConnectedLayer1.parameter.weightsInitializer = initializers.uniform.Batch(-0.001, 0.001)
fullyConnectedLayer1.parameter.biasesInitializer = initializers.uniform.Batch(0, 0.5)
# Create fully-connected layer and initialize layer parameters
fullyConnectedLayer2 = layers.fullyconnected.Batch(2)
fullyConnectedLayer2.parameter.weightsInitializer = initializers.uniform.Batch(0.5, 1)
fullyConnectedLayer2.parameter.biasesInitializer = initializers.uniform.Batch(0.5, 1)
# Create softmax layer and initialize layer parameters
softmaxCrossEntropyLayer = layers.loss.softmax_cross.Batch()
# Create configuration of the neural network with layers
topology = training.Topology()
# Add layers to the topology of the neural network
topology.push_back(fullyConnectedLayer1)
topology.push_back(fullyConnectedLayer2)
topology.push_back(softmaxCrossEntropyLayer)
topology.get(fc1).addNext(fc2)
topology.get(fc2).addNext(sm1)
return topology
def trainModel():
# Read training data set from a .csv file and create a tensor to store input data
trainingData = readTensorFromCSV(trainDatasetFile)
trainingGroundTruth = readTensorFromCSV(trainGroundTruthFile, True)
sgdAlgorithm = optimization_solver.sgd.Batch(fptype=np.float32)
# Set learning rate for the optimization solver used in the neural network
learningRate = 0.001
sgdAlgorithm.parameter.learningRateSequence = HomogenNumericTable(1, 1, NumericTable.doAllocate, learningRate)
# Set the batch size for the neural network training
sgdAlgorithm.parameter.batchSize = batchSize
sgdAlgorithm.parameter.nIterations = int(trainingData.getDimensionSize(0) / sgdAlgorithm.parameter.batchSize)
# Create an algorithm to train neural network
net = training.Batch(sgdAlgorithm)
sampleSize = trainingData.getDimensions()
sampleSize[0] = batchSize
# Configure the neural network
topology = configureNet()
net.initialize(sampleSize, topology)
# Pass a training data set and dependent values to the algorithm
net.input.setInput(training.data, trainingData)
net.input.setInput(training.groundTruth, trainingGroundTruth)
# Run the neural network training and retrieve training model
trainingModel = net.compute().get(training.model)
# return prediction model
return trainingModel.getPredictionModel_Float32()
def testModel(predictionModel):
# Read testing data set from a .csv file and create a tensor to store input data
predictionData = readTensorFromCSV(testDatasetFile)
# Create an algorithm to compute the neural network predictions
net = prediction.Batch()
net.parameter.batchSize = predictionData.getDimensionSize(0)
# Set input objects for the prediction neural network
net.input.setModelInput(prediction.model, predictionModel)
net.input.setTensorInput(prediction.data, predictionData)
# Run the neural network prediction
# and return results of the neural network prediction
return net.compute()
def printResults(predictionResult):
# Read testing ground truth from a .csv file and create a tensor to store the data
predictionGroundTruth = readTensorFromCSV(testGroundTruthFile)
printTensors(predictionGroundTruth, predictionResult.getResult(prediction.prediction),
"Ground truth", "Neural network predictions: each class probability",
"Neural network classification results (first 20 observations):", 20)
topology = ""
if __name__ == "__main__":
predictionModel = trainModel()
predictionResult = testModel(predictionModel)
printResults(predictionResult)
目前支持的