def start(self):
# testing()
dataset = self.dataset
print (dataset)
image_x, image_y = get_image_size(dataset)
amount_classes = get_amount_of_classes(dataset)
# Pooling size
poolsize_x = self.pooling_x
poolsize_y = self.pooling_y
# Learning rate
# Epochs to be trained and batch size
user_learning_rate = self.learn_r
user_nepochs = self.epochs
user_batch = self.batch
# Size of the convolution filter windows
user_filter_x = self.filter_x
user_filter_y = self.filter_y
# Treshhold for model training
user_treshhold = 0.995
#amount of layers given by user
n=self.layers
print ('Please, wait until all iterations are completed.')
evaluate_lenet5(learning_rate=self.learn_r, n_epochs=self.epochs,
dataset=self.dataset,
nkerns=[20, 50], batch_size=self.batch)
def test_convolutional_mlp():
convolutional_mlp.evaluate_lenet5(n_epochs=1, nkerns=[5, 5])
dataset[n, N] = files_data[n][1]
return dataset
dataset = createDataSet()
# outputFile = "c:\\temp\\foo.csv"
# np.savetxt(outputFile, np.asarray(dataset), delimiter=",", fmt='%d')
def getFormattedDataSet(dataset):
train_set = (dataset[0:10, :-1], dataset[0:10, -1])
valid_set = (dataset[10:14, :-1], dataset[10:14, -1])
test_set = (dataset[14:19, :-1], dataset[14:19, -1])
train_set_x, train_set_y = shared_dataset(train_set)
valid_set_x, valid_set_y = shared_dataset(valid_set)
test_set_x, test_set_y = shared_dataset(test_set)
rval = [(train_set_x, train_set_y), (valid_set_x, valid_set_y), (test_set_x, test_set_y)]
return rval
evaluate_lenet5(getFormattedDataSet(dataset), learning_rate=0.01)
# plt.imshow(bla2, cmap=plt.cm.gray)
# l = misc.lena()
# plt.show()
def test_convolutional_mlp():
t0=time.time()
convolutional_mlp.evaluate_lenet5(n_epochs=5,nkerns=[5,5])
print >> sys.stderr, "test_convolutional_mlp took %.3fs expected 168s in our buildbot"%(time.time()-t0)
logistic_cg.cg_optimization_mnist(mnist_pkl_gz=c10)
sys.stdout = open('results/cifar-10_results/lsgd.out', 'w')
logistic_sgd.sgd_optimization_mnist(dataset=c10)
sys.stdout = open('results/cifar-10_results/lsgd_gau.out', 'w')
logistic_sgd_gaussian.sgd_optimization_mnist(dataset=c10)
sys.stdout = open('results/cifar-10_results/lsgd_bin.out', 'w')
logistic_sgd_binomial.sgd_optimization_mnist(dataset=c10)
sys.stdout = open('results/cifar-10_results/mlp.out', 'w')
mlp.test_mlp(dataset=c10)
sys.stdout = open('results/cifar-10_results/mlpO.out', 'w')
# mlp_dropOut.test_mlp(p=0.8, n_hidden = 100)
mlp_dropOut.test_mlp(dataset=c10)
sys.stdout = open('results/cifar-10_results/mlpC.out', 'w')
mlp_dropConnect.test_mlp(dataset=c10)
sys.stdout = open('results/cifar-10_results/convo.out', 'w')
convolutional_mlp.evaluate_lenet5(dataset=c10)
sys.stdout = open('results/cifar-10_results/convoC.out', 'w')
# con_mlp_dropConnect.evaluate_lenet5(p=0.8)
con_mlp_dropConnect.evaluate_lenet5(dataset=c10)
sys.stdout = open('results/cifar-10_results/convoO.out', 'w')
con_mlp_dropOut.evaluate_lenet5(dataset=c10)
def test_convolutional_mlp():
convolutional_mlp.evaluate_lenet5(n_epochs=1, nkerns=[5, 5])
def conv_mlp_train_and_predict(train_set_x,train_set_y,valid_set_x,valid_set_y,test_set_x,test_set_y,rng,isrbg=False):
PRED = evaluate_lenet5(np.copy(train_set_x),np.copy(train_set_y),np.copy(valid_set_x),np.copy(valid_set_y),np.copy(test_set_x),np.copy(test_set_y),rng,isrbg=isrbg)
return PRED
