def mnist(parameters, loss_function):
dataset = 0
digits = []
for p in parameters:
if (p == "testing"):
dataset = 1
if (p.isdigit()):
digits.append(int(p))
output_size = 10
if (len(digits) == 0):
images, labels = mb.load_mnist(
dataset=("training" if not dataset else "testing"))
# If specified which images to get: (i.e. [1, 4, 6])
else:
images, labels = mb.load_mnist(
dataset=("training" if not dataset else "testing"), digits=digits)
# Creating [input, output] - cases, with normalized, flattened images, and int label vectors as output (sparse need integers, not vectors):
cases = [[
mb.flatten_image(i) / la.norm(i),
TFT.int_to_one_hot(int(l[0]), output_size)
] for (i, l) in zip(images, labels)]
print("Total cases collected: ", len(cases))
return cases
def run(self, delta, epochs):
trX, trY = mnist_b.load_mnist()
trX = self.floatX(trX)
trX = trX/255.
trX = trX.reshape((60000,28*28)).astype(float)
trY = one_hot(trY, 10)
teX, teY = mnist_b.load_mnist("testing")
teX = self.floatX(teX)
teX = teX/255.
teX = teX.reshape((10000,28*28)).astype(float)
teY = one_hot(teY, 10)
result_list = [self.neuronsInHiddenLayers, self.listOfFunctions, delta, epochs,[]]
print ("Starting...")
self.printSetUp()
for i in range(epochs):
for start, end in zip(range(0, len(trX), delta), range(delta, len(trX), delta)):
self.cost = self.train(trX[start:end], trY[start:end])
predicted = np.mean(np.argmax(teY, axis=1) == self.predict(teX)) * 100
result_list[4].append(predicted)
print ("epoch: " + str(i + 1))
print ("Epoch number " + str(i + 1) + " predicted : " + str(predicted) + str(" % correct"))
print (result_list)
plt.plot(result_list[4])
plt.ylabel('correctness rate')
plt.xlabel('epochs')
plt.show()
def main(): images, labels = MNIST.gen_flat_cases(digits=np.arange(10),type='training',cases=(MNIST.load_mnist(dataset="training", digits=np.arange(10), path="datasets/"))) images = np.divide(images,255) ann = ANN(images, [10, 10], 0.1) ann.do_training(10, test_interval=10) return ann.do_testing()
def mnist(self):
data_set = MNIST.load_mnist()
flat_set = MNIST.gen_flat_cases(cases = data_set)
return_set = []
for i in range(len(flat_set[0])):
return_set.append([flat_set[0][i], TFT.int_to_one_hot(flat_set[1][i], 10)])
return return_set
def main():
images, labels = MNIST.gen_flat_cases(digits=np.arange(10),
type='training',
cases=(MNIST.load_mnist(
dataset="training",
digits=np.arange(10),
path="datasets/")))
images = np.divide(images, 255)
ann = ANN(images, [10, 10], 0.1)
ann.do_training(10, test_interval=10)
return ann.do_testing()
def run(self, delta, epochs):
trX, trY = mnist_b.load_mnist()
trX = self.floatX(trX)
trX = trX / 255.
trX = trX.reshape((60000, 28 * 28)).astype(float)
trY = one_hot(trY, 10)
teX, teY = mnist_b.load_mnist("testing")
teX = self.floatX(teX)
teX = teX / 255.
teX = teX.reshape((10000, 28 * 28)).astype(float)
teY = one_hot(teY, 10)
result_list = [
self.neuronsInHiddenLayers, self.listOfFunctions, delta, epochs,
[]
]
print("Starting...")
self.printSetUp()
for i in range(epochs):
for start, end in zip(range(0, len(trX), delta),
range(delta, len(trX), delta)):
self.cost = self.train(trX[start:end], trY[start:end])
predicted = np.mean(
np.argmax(teY, axis=1) == self.predict(teX)) * 100
result_list[4].append(predicted)
print("epoch: " + str(i + 1))
print("Epoch number " + str(i + 1) + " predicted : " +
str(predicted) + str(" % correct"))
print(result_list)
plt.plot(result_list[4])
plt.ylabel('correctness rate')
plt.xlabel('epochs')
plt.show()
