def run_mnist(epochs, layers, neuron_count):
""" Run Mnist dataset and output a guess list on the Kaggle test_set
Parameters
----------
epochs : int
Number of iterations of the the traininng loop for the whole dataset
layers : int
Number of layers (not counting the input layer, but does count output
layer)
neuron_count : list
The number of neurons in each of the layers (in order), does not count
the bias term
Attributes
----------
target_values : list
The possible values for each training vector
"""
with open('train.csv', 'r') as f:
reader = csv.reader(f)
t = list(reader)
train = [[int(x) for x in y] for y in t[1:]]
with open('test.csv', 'r') as f:
reader = csv.reader(f)
raw_nums = list(reader)
test_set = [[int(x) for x in y] for y in raw_nums[1:]]
ans_train = [x[0] for x in train]
train_set = [x[1:] for x in train]
ans_train.pop(0)
train_set.pop(0)
train_set = utils.resample(train_set, random_state=2)
ans_train = utils.resample(ans_train, random_state=2)
network = Network(layers, neuron_count, train_set[0])
network.train(train_set, ans_train, epochs)
# For validation purposes
# guess_list = network.run_unseen(train_set[4000:4500])
# network.report_results(guess_list, ans_train[4000:4500])
# guess_list = network.run_unseen(train_set[4500:5000])
# network.report_results(guess_list, ans_train[4500:5000])
guess_list = network.run_unseen(test_set)
with open('digits.txt', 'w') as d:
for elem in guess_list:
d.write(str(elem) + '\n')
def run_mnist(run_num, epochs=0, layers=0, neuron_count=0):
""" Run Mnist dataset and output a guess list on the Kaggle test_set
Parameters
----------
epochs : int
Number of iterations of the the traininng loop for the whole dataset
layers : int
Number of layers (not counting the input layer, but does count output
layer)
neuron_count : list
The number of neurons in each of the layers (in order), does not count
the bias term
Attributes
----------
"""
ans_train = []
train_set = []
with open('train.csv', 'r') as f:
reader = csv.reader(f)
t = list(reader)
train = [[int(x) for x in y] for y in t[1:]]
ans_train = [x[0] for x in train]
train_set = [x[1:] for x in train]
ans_train.pop(0)
train_set.pop(0)
network = Network(layers, neuron_count, train_set[1])
network.train(train_set, ans_train, epochs)
guess_list = network.run_unseen(train_set[37800:])
print('Test Set')
test_report = network.report_results(guess_list, ans_train[37800:])
file_name = 'finnegan/my_net_' + str(run_num) + '.pickle'
g = open(file_name, 'wb')
pickle.dump(network, g, protocol=4)
g.close()
file_name_2 = 'finnegan/my_net_report_' + str(run_num) + '.txt'
h = open(file_name_2, 'w')
details = 'Neuron Counts: ' + str(neuron_count) + '\n'
details_2 = 'Test Report: ' + test_report + '\n'
h.write(details)
h.write(details_2)
h.close()
return None
def run_scikit_digits(epochs=0, layers=0, neuron_count=0):
""" Run Handwritten Digits dataset from Scikit-Learn. Learning set is split
into 70% for training, 15% for testing, and 15% for validation.
Parameters
----------
epochs : int
Number of iterations of the the traininng loop for the whole dataset
layers : int
Number of layers (not counting the input layer, but does count output
layer)
neuron_count : list
The number of neurons in each of the layers (in order), does not count
the bias term
Attributes
----------
target_values : list
The possible values for each training vector
"""
# Imported from linear_neuron
temp_digits = datasets.load_digits()
digits = utils.resample(temp_digits.data, random_state=3)
temp_answers = utils.resample(temp_digits.target, random_state=3)
# images = utils.resample(temp_digits.images, random_state=0)
num_of_training_vectors = 1250
answers, answers_to_test, validation_answers = temp_answers[:num_of_training_vectors], temp_answers[
num_of_training_vectors:num_of_training_vectors +
260], temp_answers[num_of_training_vectors + 260:]
training_set, testing_set, validation_set = digits[:num_of_training_vectors], digits[
num_of_training_vectors:num_of_training_vectors +
260], digits[num_of_training_vectors + 260:]
###########
# network.visualization(training_set[10], answers[10])
# network.visualization(training_set[11], answers[11])
# network.visualization(training_set[12], answers[12])
network = Network(layers, neuron_count, training_set[0])
network.train(training_set, answers, epochs)
f = open('my_net.pickle', 'wb')
# fr = open('my_net.pickle', 'rb')
dill.dump(network, f)
# network = pickle.load(fr)
# fr.close()
f.close()
# guess_list = network.run_unseen(testing_set)
return network.run_unseen(testing_set)
def run_mnist(run_num, epochs=0, layers=0, neuron_count=0):
""" Run affNIST dataset and output a guess list on test and validation
sets. Dumps a pickle of the trained network state and a results file
for choosing the best parameters.
Parameters
----------
epochs : int
Number of iterations of the the traininng loop for the whole dataset
layers : int
Number of layers (not counting the input layer, but does count output
layer)
neuron_count : list
The number of neurons in each of the layers (in order), does not count
the bias term
Attributes
----------
"""
dataset = loadmat('1.mat')
ans_train = dataset['affNISTdata']['label_int']
train_set = dataset['affNISTdata']['image'].transpose()
dataset2 = loadmat('2.mat')
ans_train2 = dataset2['affNISTdata']['label_int']
train_set2 = dataset2['affNISTdata']['image'].transpose()
ans_train = np.hstack((ans_train, ans_train2))
train_set = np.vstack((train_set, train_set2))
network = Network(layers, neuron_count, train_set[1])
network.train(train_set, ans_train, epochs)
dataset = loadmat('3.mat')
ans_train = dataset['affNISTdata']['label_int']
train_set = dataset['affNISTdata']['image'].transpose()
guess_list = network.run_unseen(train_set)
print('Test Set')
test_report = network.report_results(guess_list, ans_train)
dataset = loadmat('4.mat')
ans_train = dataset['affNISTdata']['label_int']
train_set = dataset['affNISTdata']['image'].transpose()
guess_list = network.run_unseen(train_set)
print('Validation Set')
val_report = network.report_results(guess_list, ans_train)
file_name = 'finnegan/my_net_' + str(run_num) + '.pickle'
g = open(file_name, 'wb')
pickle.dump(network, g, protocol=4)
g.close()
file_name_2 = 'finnegan/my_net_report_' + str(run_num) + '.txt'
h = open(file_name_2, 'w')
details = 'Neuron Counts: ' + str(neuron_count) + '\n'
details_2 = 'Test Report: ' + test_report + '\n'
details_3 = 'Validation Report: ' + val_report + '\n'
h.write(details)
h.write(details_2)
h.write(details_3)
h.close()
return None