def main():
trainX, trainY, testX, testY = load_mnist()
print "Shapes: ", trainX.shape, trainY.shape, testX.shape, testY.shape
print "\nDigit sample"
print_digit(trainX[1], trainY[1])
train_dense.train(trainX, trainY)
labels = train_dense.test(testX)
accuracy = np.mean((labels == testY)) * 100.0
print "\nDNN Test accuracy: %lf%%" % accuracy
def main():
print("\n\n\nNOTE")
print("***First change the Backend from Tesorflow to Theano****")
print(
"IF ANY ERRORS OCCUR PLEASE ENSURE THAT THE VERSION OF KERAS USED IS 1.2.2\n\n\n"
)
trainX, trainY, testX, testY = load_mnist()
print "Shapes: ", trainX.shape, trainY.shape, testX.shape, testY.shape
print "\nDigit sample"
print_digit(trainX[1], trainY[1])
# train_dense.train(trainX, trainY)
labels = train_dense.test(testX)
accuracy = np.mean((labels == testY)) * 100.0
print "\nDNN Test accuracy: %lf%%" % accuracy
'''# train_cnn.train(trainX, trainY)
def main():
trainX, trainY, testX, testY = load_mnist()
print "Shapes: ", trainX.shape, trainY.shape, testX.shape, testY.shape
print "\nDigit sample"
print_digit(trainX[1], trainY[1])
print 'Cloning from github'
git_repo = 'https://github.com/tushargupta14/weights.git'
call(['git', 'clone', git_repo])
#train_dense.train(trainX,trainY)
labels = train_dense.test(testX)
accuracy = np.mean((labels == testY)) * 100.0
print "\nDNN Test accuracy: %lf%%" % accuracy
#train_cnn.train(trainX, trainY)
labels = train_cnn.test(testX)
accuracy = np.mean((labels == testY)) * 100.0
print "\nCNN Test accuracy: %lf%%" % accuracy