Python Supervised.evalModel Examples

Example #1

File: eval_cifar_supervised_train.py Project: slundqui/TFSparseCode

}

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

print "Done run"

tfObj.closeSess()

numCorrect = len(np.nonzero(est == gt)[0])
print "Accuracy: ", float(numCorrect)/testDataObj.numImages

Example #2

}

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

print "Done run"

tfObj.closeSess()

numCorrect = len(np.nonzero(est == gt)[0])
print "Accuracy: ", float(numCorrect) / testDataObj.numImages