Intel DAAL AI加速——神经网络

# file: neural_net_dense_batch.py
#===============================================================================
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#===============================================================================
 
#
# !  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Layers：

• Common Parameters
• Fully Connected Forward Layer
• Fully Connected Backward Layer
• Absolute Value ForwardLayer
• Absolute Value Backward Layer
• Logistic ForwardLayer
• Logistic BackwardLayer
• pReLU ForwardLayer
• pReLU BackwardLayer
• ReLU Forward Layer
• ReLU BackwardLayer
• SmoothReLU ForwardLayer
• SmoothReLU BackwardLayer
• Hyperbolic Tangent Forward Layer
• Hyperbolic Tangent Backward Layer
• Batch Normalization Forward Layer
• Batch Normalization Backward Layer
• Local-Response Normalization ForwardLayer
• Local-Response Normalization Backward Layer
• Local-Contrast Normalization ForwardLayer
• Local-Contrast Normalization Backward Layer
• Dropout ForwardLayer
• Dropout BackwardLayer
• 1D Max Pooling Forward Layer
• 1D Max Pooling Backward Layer
• 2D Max Pooling Forward Layer
• 2D Max Pooling Backward Layer
• 3D Max Pooling Forward Layer
• 3D Max Pooling Backward Layer
• 1D Average Pooling Forward Layer
• 1D Average Pooling Backward Layer
• 2D Average Pooling Forward Layer
• 2D Average Pooling Backward Layer
• 3D Average Pooling Forward Layer
• 3D Average Pooling Backward Layer
• 2D Stochastic Pooling Forward Layer
• 2D Stochastic Pooling Backward Layer
• 2D Spatial Pyramid Pooling ForwardLayer
• 2D Spatial Pyramid Pooling BackwardLayer
• 2D Convolution Forward Layer
• 2D Convolution Backward Layer
• 2D Transposed Convolution ForwardLayer
• 2D Transposed Convolution BackwardLayer
• 2D Locally-connected Forward Layer
• 2D Locally-connected Backward Layer
• Reshape ForwardLayer
• Reshape BackwardLayer
• Concat ForwardLayer
• Concat BackwardLayer
• Split Forward Layer
• Split Backward Layer
• Softmax ForwardLayer
• Softmax BackwardLayer
• Loss Forward Layer
• Loss Backward Layer
• Loss Softmax Cross-entropy ForwardLayer
• Loss Softmax Cross-entropy BackwardLayer
• Loss Logistic Cross-entropy ForwardLayer
• Loss Logistic Cross-entropy BackwardLayer
• Exponential Linear Unit Forward Layer
• Exponential Linear Unit Backward Layer