__author__ = 'JoJo' # Import statements required to build, train, and test the networks from pybrain.tools.shortcuts import buildNetwork from pybrain.tools.datasets import mnist from pybrain.structure.modules import LSTMLayer from pybrain.supervised.trainers import BackpropTrainer from pybrain.tools.validation import ModuleValidator # Import statement to keep track of time elapsed import time # Import statement to plot data import matplotlib.pyplot as plt path = "/Users/JoJo/Desktop/Macalester-Sophomore/COMP221/Final/MNIST" (train, test) = mnist.makeMnistDataSets(path) # Build the networks with increasing complexity in number of nodes in order to test if number of nodes affects error rate # Trainers are created for each network and each network is trained once # Then, I test the network against the actual dataset and get the mean squared error and see the connection between nodes and MSE inputNode = 28*28 baselineNumberOfNodes = 10 numOutputNodes = 10 LSTMNetworkNumber = 1 LSTMNetworks = [] multipliers = [1,2,5,10] overallError = [] testComparison = [] timeSpentBuilding = []
start = clock() mse = ModuleValidator.MSE(n,ts) end = clock() duration = end - start writeFile(base + str(i) + "/evaluationTime.txt","The network tested in " + str(duration) + " seconds with an MSE of " + str(mse))#record the MSE if __name__ == "__main__": #define constants rootPath = "/Users/hherman1/ConvolutionalNeuralNetwork/" resDir = rootPath + "results/" convDir = resDir + "convolutional/" lstmDir = resDir + "lstm/" ffDir = resDir + "feedforward/" MNISTDIR = "MNIST/" #build training and testing data sets (train,test) = makeMnistDataSets(rootPath + MNISTDIR) #designate testing values convolutionalTestValues = [] lstmTestValues = [1,2,5,10] ffTestValues = [1,2,3,4,5,6] #build folder structure buildStructure(rootPath) #evaluate convolutional networks #RECCOMENDATION: Use the tensorflow implementation, also provided, or run no tests on the convolutional network, since these are exceptionally slow in pybrain. nets = buildConvolutionalNetworks(convDir,convolutionalTestValues) nets = trainNetworks(convDir,nets,train) testAccuracy(convDir,nets,test)
# Import statements required to build, train, and test the networks from pybrain.tools.shortcuts import buildNetwork from pybrain.tools.datasets import mnist from pybrain.structure.modules import LSTMLayer from pybrain.supervised.trainers import BackpropTrainer from pybrain.tools.validation import ModuleValidator # Import statement to keep track of time elapsed import time # Import statement to plot data import matplotlib.pyplot as plt path = "MNIST/" (train, test) = mnist.makeMnistDataSets(path) # Build the networks with increasing complexity in number of nodes in order to test if number of nodes affects error rate # Trainers are created for each network and each network is trained once # Then, I test the network against the actual dataset and get the mean squared error and see the connection between nodes and MSE inputNode = 28 * 28 baselineNumberOfNodes = 10 numOutputNodes = 10 LSTMNetworkNumber = 1 LSTMNetworks = [] multipliers = [1, 2, 5, 10] overallError = [] testComparison = [] timeSpentBuilding = [] timeSpentTraining = [] timeSpentTesting = []
from pybrain.tools.shortcuts import buildNetwork
from pybrain.tools.validation import ModuleValidator
import matplotlib.pyplot as plt
import time
FirstStartTime = time.time()
print("Making Networks 1-6")
net1 = buildNetwork(28 * 28, 28, 10)
net2 = buildNetwork(28 * 28, 28 * 2, 10)
net3 = buildNetwork(28 * 28, 28 * 3, 10)
net4 = buildNetwork(28 * 28, 28 * 4, 10)
net5 = buildNetwork(28 * 28, 28 * 5, 10)
net6 = buildNetwork(28 * 28, 28 * 6, 10)
#Gets the training and test sets for MNIST stuff
(train, test) = mnist.makeMnistDataSets(
"/Users/talhaAhsan/PycharmProjects/PythonCrashCourse/MNIST_data")
print("Training and Testing sets have been formed")
trainerrorlist = []
print("Starting backprop training")
timeTaken = []
starttime = time.time()
print("Training Network 1")
trainer1 = backprop.BackpropTrainer(net1, dataset=train)
score1 = trainer1.train()
trainerrorlist.append(score1)
timeTaken.append(time.time() - starttime)
starttime = time.time()
print("Training Network 2")
trainer2 = backprop.BackpropTrainer(net2, dataset=train)
from pybrain.tools.shortcuts import buildNetwork
from pybrain.tools.validation import ModuleValidator
import matplotlib.pyplot as plt
import time
FirstStartTime = time.time()
print("Making Networks 1-6")
net1 = buildNetwork(28*28, 28, 10)
net2 = buildNetwork(28*28, 28*2, 10)
net3 = buildNetwork(28*28, 28*3, 10)
net4 = buildNetwork(28*28, 28*4, 10)
net5 = buildNetwork(28*28, 28*5, 10)
net6 = buildNetwork(28*28, 28*6, 10)
#Gets the training and test sets for MNIST stuff
(train, test) = mnist.makeMnistDataSets("C:\\Users\\Talha Ahsan\\PycharmProjects\\PythonCrashCourse\\MNIST_data")
print("Training and Testing sets have been formed")
trainerrorlist = []
print("Starting backprop training")
timeTaken = []
starttime = time.time()
print("Training Network 1")
trainer1 = backprop.BackpropTrainer(net1, dataset= train)
score1 = trainer1.train()
trainerrorlist.append(score1)
timeTaken.append(time.time() - starttime)
starttime = time.time()
print("Training Network 2")
trainer2 = backprop.BackpropTrainer(net2, dataset= train)