Python Supervised Exemples

Exemple #1

Fichier : eval_cifar_supervised_train.py Projet : slundqui/TFSparseCode

    'VStrideY':        2,
    'VStrideX':        2,
    'patchSizeX':      12,
    'patchSizeY':      12,
    'numV':            128,
    #####New encode parapms#####
    'maxPool':         True, #Controls max or avg pool
}

print "Done init"
est = np.zeros((testDataObj.numImages))
gt = np.zeros((testDataObj.numImages))

#Allocate tensorflow object
#This will build the graph
tfObj = Supervised(params, trainDataObj.inputShape)

assert(testDataObj.numImages % params["batchSize"] == 0)

for i in range(testDataObj.numImages/params["batchSize"]):
    print i*params["batchSize"], "out of", testDataObj.numImages
    (inImage, inGt) = testDataObj.getData(params["batchSize"])
    outVals = tfObj.evalModel(inImage, inGt = inGt, plot=False)
    tfObj.timestep += 1
    v = np.argmax(outVals, axis=1)

    startIdx = i*batch
    endIdx = startIdx + params["batchSize"]
    est[startIdx:endIdx] = v
    gt[startIdx:endIdx] = inGt

Exemple #2

Fichier : cifar_supervised_train_limited.py Projet : xueyangfu/TFSparseCode

    'displayPeriod': 100,
    #Batch size
    'batchSize': 4,
    #Learning rate for optimizer
    'learningRate': 1e-4,
    'numClasses': 10,
    'epsilon': 1e-8,
    'regularizer': 'none',
    'regWeight': .3,

    #####ISTA PARAMS######
    'VStrideY': 2,
    'VStrideX': 2,
    'patchSizeX': 12,
    'patchSizeY': 12,
    'numV': 256,
    #####New encode parapms#####
    'maxPool': True,  #Controls max or avg pool
    'preTrain': False,
}

#Allocate tensorflow object
#This will build the graph
tfObj = Supervised(params, trainDataObj)

print("Done init")
tfObj.runModel(trainDataObj, testDataObj=testDataObj, numTest=256)
print("Done run")

tfObj.closeSess()

Exemple #3

    'outerSteps': 500,  #1000000,
    'innerSteps': 100,  #300,
    #Batch size
    'batchSize': 128,
    #Learning rate for optimizer
    'learningRate': 1e-4,
    'numClasses': 10,
    'epsilon': 1e-8,
    'regularizer': 'none',
    'regWeight': .3,

    #####ISTA PARAMS######
    'VStrideY': 2,
    'VStrideX': 2,
    'patchSizeX': 12,
    'patchSizeY': 12,
    'numV': 128,
    #####New encode parapms#####
    'maxPool': True,  #Controls max or avg pool
}

#Allocate tensorflow object
#This will build the graph
tfObj = Supervised(params, trainDataObj.inputShape)

print("Done init")
tfObj.runModel(trainDataObj, testDataObj=testDataObj)
print("Done run")

tfObj.closeSess()

Exemple #4

Fichier : cifar_supervised_train.py Projet : slundqui/TFSparseCode

    'batchSize':       128,
    #Learning rate for optimizer
    'learningRate':    1e-4,
    'numClasses':      10,

    'epsilon': 1e-8,

    'regularizer': 'none',
    'regWeight': .3,

    #####ISTA PARAMS######
    'VStrideY':        2,
    'VStrideX':        2,
    'patchSizeX':      12,
    'patchSizeY':      12,
    'numV':            128,
    #####New encode parapms#####
    'maxPool':         True, #Controls max or avg pool
}

#Allocate tensorflow object
#This will build the graph
tfObj = Supervised(params, trainDataObj.inputShape)

print "Done init"
tfObj.runModel(trainDataObj, testDataObj = testDataObj)
print "Done run"

tfObj.closeSess()

Exemple #5

    'VStrideY': 2,
    'VStrideX': 2,
    'patchSizeX': 12,
    'patchSizeY': 12,
    'numV': 128,
    #####New encode parapms#####
    'maxPool': True,  #Controls max or avg pool
}

print "Done init"
est = np.zeros((testDataObj.numImages))
gt = np.zeros((testDataObj.numImages))

#Allocate tensorflow object
#This will build the graph
tfObj = Supervised(params, trainDataObj.inputShape)

assert (testDataObj.numImages % params["batchSize"] == 0)

for i in range(testDataObj.numImages / params["batchSize"]):
    print i * params["batchSize"], "out of", testDataObj.numImages
    (inImage, inGt) = testDataObj.getData(params["batchSize"])
    outVals = tfObj.evalModel(inImage, inGt=inGt, plot=False)
    tfObj.timestep += 1
    v = np.argmax(outVals, axis=1)

    startIdx = i * batch
    endIdx = startIdx + params["batchSize"]
    est[startIdx:endIdx] = v
    gt[startIdx:endIdx] = inGt

Exemple #6

Fichier : cifar_supervised_train_limited.py Projet : slundqui/TFSparseCode

    'learningRate':    1e-4,
    'numClasses':      10,

    'epsilon': 1e-8,

    'regularizer': 'none',
    'regWeight': .3,

    #####ISTA PARAMS######
    'VStrideY':        2,
    'VStrideX':        2,
    'patchSizeX':      12,
    'patchSizeY':      12,
    'numV':            256,
    #####New encode parapms#####
    'maxPool':         True, #Controls max or avg pool

    'preTrain': False,
}

#Allocate tensorflow object
#This will build the graph
tfObj = Supervised(params, trainDataObj)

print("Done init")
tfObj.runModel(trainDataObj, testDataObj = testDataObj, numTest=256)
print("Done run")

tfObj.closeSess()