def fnn(hidden_neurons, epochs, mini_batch_size, learning_rate,
training_feedback, training_dataset_type: Training_Dataset):
"""Feedforward neural network using stochastic gradient descent."""
# Loads traninig data
training_data, validation_data, test_data = mnist_loader.load_data()
# Select the training dataset
if (training_dataset_type == Training_Dataset.Validation_Data):
training_dataset = validation_data
elif (training_dataset_type == Training_Dataset.Test_Data):
training_dataset = test_data
# Basic three-layers based neural network with 748 neurons on input layer,'hidden_neurons'
# value as neurons on hidden layer, 10 neurons on output layer.
net = neural_network.Network([784, hidden_neurons, 10])
# Train the network with 'epochs' value epochs, 'mini_batch_size' digits as mini-batch size,
# 'learning_rate' as learning rate and test feedback if 'training_feedback' is true.
print_information("FNN - Quadratic Cost", hidden_neurons, epochs,
mini_batch_size, learning_rate)
net.stochastic_gradient_descent(
training_data,
epochs,
mini_batch_size,
learning_rate,
test_data=training_dataset if training_feedback else None)
def average_validation_data(): training_data, validation_data, test_data = np.asarray(mnist_loader.load_data()) d1 = defaultdict(int) d2 = defaultdict(float) # Creating two dictionaries for the average of grayscale values and the frequncies of the digits for image_data, digit_frequency_data in zip(training_data[0], training_data[1]): d1[digit_frequency_data] += 1 d2[digit_frequency_data] += image_data.mean(axis=0) print(d1) print(d2) # average = defaultdict(float) for key, value_frequency in d1.iteritems(): average[key] = round((d2[key]/value_frequency),4) print(average) # Testing above algorithm on test_data count = 0 for test_image_data, test_label_data in zip(test_data[0], test_data[1]): average_grayscale_test_value = round(test_image_data.mean(axis=0),4) tolerance = abs(average_grayscale_test_value - average[test_label_data]) #print(tolerance) if (tolerance <=0.0005) : #print(tolerance) #print(average[test_label_data]) count += 1 print(count)
def raport4():
print("raport4")
training_data, validation_data, test_data = ml.load_data() # inputs - 784, ouptuts - 10
neural_network = cnv.Conv(784, 1, 10, alpha=0.005, max_epochs=20, acc_freeze=14,
default_hlayer_neuron_numbers=50, batch_size=100, winit=cnv.XAVIER, activation_function=cnv.SIG, optimalization=cnv.ADAM)
start = datetime.now()
start_time = start.strftime("%H:%M:%S")
t0 = time.clock()
print("start_time")
print(start_time)
training_data = training_data[0][0: 1000], training_data[1][0:1000]
validation_data = validation_data[0][0: 100], validation_data[1][0:100]
training_errors, val_errors, val_accuracy, stop_reason = neural_network.train(training_data[0], training_data[1], validation_data[0], validation_data[1])
end = datetime.now()
end_time = end.strftime("%H:%M:%S")
t1 = time.clock()
print("end_time")
print(end_time)
elapsed = t1 - t0
print("elapsed time")
print(str(timedelta(seconds=elapsed)))
print("celnosc: ")
# accuracy = neural_network.accuracy(test_data[0], test_data[1])
accuracy = 0
print(accuracy)
printresults2("conv-test", training_errors, val_errors, val_accuracy, stop_reason, accuracy)
def main2():
dnn = DNN(input=28 * 28,
layers=[DropoutLayer(160, LQ),
Layer(10, LCE)],
eta=0.05,
lmbda=1) # 98%
dnn.initialize_rand()
train, test, vadilation = load_mnist_simple()
f_names = [f'mnist_expaned_k0{i}.pkl.gz' for i in range(50)]
shuffle(f_names)
for f_name in f_names:
print(f_name)
with timing("load"):
raw_data = load_data(f_name)
with timing("shuffle"):
shuffle(raw_data)
with timing("reshape"):
data = [(x.reshape((784, 1)), y)
for x, y in islice(raw_data, 100000)]
del raw_data
with timing("learn"):
dnn.learn(data)
del data
print('TEST:', dnn.test(test))
def raport2_1():
repeat_times = 3
training_data, validation_data, test_data = ml.load_data() # inputs - 784, ouptuts - 10
start = datetime.now()
start_time = start.strftime("%H:%M:%S")
t0 = time.clock()
print("start_time")
print(start_time)
neural_network = mlp.Mlperceptron(784, 1, 10, alpha=0.1, weight_random=0.3, max_epochs=50, acc_freeze=14,
default_hlayer_neuron_numbers=300, batch_size=100, default_act=mlp.SIG)
hidden_layer_weights_r, bias_layer_r = neural_network.get_weights() # zapamiętaj najlepsze wagi w tym przypadku poczatkowo wylosowane
hidden_layer_weights = copy.deepcopy(hidden_layer_weights_r)
bias_layer = copy.deepcopy(bias_layer_r)
# pierwszy parametr
for i in range(repeat_times):
neural_network = mlp.Mlperceptron(784, 1, 10, alpha=0.1, weight_random=0.3, max_epochs=50, acc_freeze=14,
default_hlayer_neuron_numbers=1200, batch_size=100, default_act=mlp.SIG)
neural_network.set_weights(copy.deepcopy(hidden_layer_weights_r), copy.deepcopy(bias_layer_r))
training_errors, val_errors, val_accuracy, stop_reason = neural_network.train(training_data[0], training_data[1], validation_data[0], validation_data[1])
accuracy = neural_network.accuracy(test_data[0], test_data[1])
tname = "300neurons-" + str(i+1)
printresults2(tname, training_errors, val_errors, val_accuracy, stop_reason, accuracy)
end = datetime.now()
end_time = end.strftime("%H:%M:%S")
t1 = time.clock()
print("end_time")
print(end_time)
elapsed = t1 - t0
print("elapsed time")
print(str(timedelta(seconds=elapsed)))
def __init__(self):
self.training_data, self.validation_data, self.test_data = mnist_loader.load_data(
)
#print training_data[0][0].shape, training_data[0][0].shape
# train
self.clf = svm.SVC()
self.clf.fit(self.training_data[0][:1000],
self.training_data[1][:1000])
# test
self.predictions = [
int(a) for a in self.clf.predict(self.test_data[0])
]
self.true_positives = [
(self.test_data[0][idx], p[0], p[1])
for idx, p in enumerate(zip(self.predictions, self.test_data[1]))
if p[0] == p[1]
]
num_correct = sum(
int(a == y) for a, y in zip(self.predictions, self.test_data[1]))
#print 'simple black box classifier using an SVM'
logger.info('black box svm accuray: %f' %
(float(num_correct) / float(len(self.test_data[1]))))
joblib.dump(
self.clf,
'/home/ubuntu/machinelearning/src/nicefolk/simple_svm.pkl')
def main():
training_set, validation_set, test_set = mnist_loader.load_data()
images = get_images(test_set)
# plot_rotated_image(images[0])
image_number = 1503
plot_mnist_digit(images[image_number])
plot_images_together(images[101:200])
def mnist_load_data(dirn='../data'):
'''
Load MNIST data from files
'''
X_train, y_train = load_data(dirn, subset='train')
X_test, y_test = load_data(dirn, subset='test')
# reshape image data to flat
X_train = X_train.reshape([-1, 28*28])
X_test = X_test.reshape([-1, 28*28])
# scaling image data from [0 .. 255] to [0.0 .. 1.0]
X_train = X_train / 255.
X_test = X_test / 255.
return X_train, X_test, y_train, y_test
def svm_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
clf = svm.SVC()
clf.fit(training_data[0], training_data[1])
predictions = [int(a) for a in clf.predict(test_data[0])]
num_correct = sum(int(a == y) for a, y in zip(predictions, test_data[1]))
print "Baseline classifier using an SVM."
print "%s of %s values correct." % (num_correct, len(test_data[1]))
def main():
training_data, validation_data, test_data = mnist_loader.load_data()
avgs = avg_darknesses(training_data)
num_correct = sum(
int(guess_digit(image, avgs) == digit)
for image, digit in zip(test_data[0], test_data[1]))
print("Baseline classifier using average darkness of images.")
print("{0} of {1} values correct.".format(num_correct, len(test_data[1])))
def main():
training_data, validation_data, test_data = mnist_loader.load_data()
# training phase: compute the average darknesses for each digit, based on the training data
avgs = avg_darknesses(training_data)
# testing phase: see how many of the test images are classified correctly
num_correct = sum(
int(guess_digit(image, avgs) == digit)
for image, digit in zip(test_data[0], test_data[1]))
print("Baseline classifier using average darkness of image.")
print("{0} of {1} values correct.".format(num_correct, len(test_data[1])))
def svm_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
# train
clf = svm.SVC()
clf.fit(training_data[0], training_data[1])
# test
predictions = [int(a) for a in clf.predict(test_data[0])]
num_correct = sum(int(a == y) for a, y in zip(predictions, test_data[1]))
print('The evaluaion results:')
print('%s of %s values correct.' % (num_correct, len(test_data[1])))
def svm_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
# train
clf = svm.SVC()
clf.fit(training_data[0], training_data[1])
# test
predictions = [int(a) for a in clf.predict(test_data[0])]
num_correct = sum(int(a == y) for a, y in zip(predictions, test_data[1]))
print "Baseline classifier using an SVM."
print "%s of %s values correct." % (num_correct, len(test_data[1]))
def main():
"""The main script to model and train the model"""
# load the data
training_data, validation_data, test_data = mnist_loader.load_data()
# training phase: compute the average darkness of each digit
avgs = avg_darknesses(training_data)
# testing phase: evaluate the model
num_correct = sum(int(guess_digit(image, avgs) == digit)
for image, digit in zip(test_data[0], test_data[1]))
print('The evaluation results:')
print('%s of %s values correct.' % (num_correct, len(test_data[1])))
def svm_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
# train
clf = svm.SVC()
# training_data[0] is 50000 images, training_data[1] is 50000 labels
clf.fit(training_data[0], training_data[1])
# test
predictions = [int(a) for a in clf.predict(test_data[0])]
num_correct = sum(int(a == y) for a, y in zip(predictions, test_data[1]))
print("Baseline classifier using an SVM.")
print(str(num_correct) + " of " + str(len(test_data[1])) + " values correct.")
def main():
training_data, validation_data, test_data = mnist_loader.load_data()
# training phase: compute the average darknesses for each digit,
# based on the training data
avgs = avg_darknesses(training_data)
# testing phase: see how many of the test images are classified
# correctly
num_correct = sum(int(guess_digit(image, avgs) == digit)
for image, digit in zip(test_data[0], test_data[1]))
print("Baseline classifier using average darkness of image.")
print("{0} of {1} values correct.".format(num_correct, len(test_data[1])))
def svm_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
#print training_data[0][0].shape, training_data[0][0].shape
# train
clf = svm.SVC()
clf.fit(training_data[0], training_data[1])
# test
predictions = [int(a) for a in clf.predict(test_data[0])]
num_correct = sum(int(a == y) for a, y in zip(predictions, test_data[1]))
print "Baseline classifier using an SVM."
print "%s of %s values correct." % (num_correct, len(test_data[1]))
joblib.dump(clf, '/home/ubuntu/machinelearning/src/nicefolk/simple_svm.pkl')
def main():
training_data, validation_data, test_data = mnist_loader.load_data(
) # 这里的每行数据是个元组(x数据,y标签)
# training phase: compute the average darknesses for each digit,
# based on the training data
avgs = avg_darknesses(training_data)
# testing phase: see how many of the test images are classified
# correctly
num_correct = sum(
int(guess_digit(image, avgs) == digit)
for image, digit in zip(test_data[0], test_data[1]))
print "Baseline classifier using average darkness of image."
print "%s of %s values correct." % (num_correct, len(test_data[1]))
def main(k): training_data, validation_data, test_data = mnist_loader.load_data() k = int(k[0]) predictions=[] for i in range( len(test_data[0])): neighbors=getNeighbors(training_data[0],test_data[0][i],training_data[1],k) result=getResponse(neighbors) predictions.append(result) confusionMatrix = CreateConfusionMatrix(predictions,test_data[1]) avgPrecision, avgRecall, avgSpecificity, precision, recall, specificity = CalculatePrecisionAndRecall(confusionMatrix, 10, len(test_data[1])) PrintResults(confusionMatrix, avgPrecision, avgRecall, avgSpecificity, precision, recall, specificity) accuracy = GetAccuracy(test_data[1], predictions) print "Accuracy :", accuracy
def deep_net_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
X = training_data[0]
Y = training_data[1]
# one_hot_encoding and reshape both (Transpose - flip rows/columns, so m=50.000=columns)
X_reshaped = X.reshape(784, 50000)
Y_reshaped = one_hot(Y, 10).reshape(10, 50000)
# train
layer_dims = [784, 20, 25, 15, 10] # 5-layer model
parameters = network.L_layer_model(X_reshaped, Y_reshaped, layer_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False)
def run_svms():
svm_training_data, svm_validation_data, svm_test_data = mnist_loader.load_data()
accuracies = []
for size in SIZES:
print("\n\nTraining SVM with data set size %s" %size)
clf = svm.SVC()
clf.fit(svm_training_data[0][:size],svm_training_data[1][:size])
predictions = [int(a) for a in clf.predict(svm_validation_data[0])]
accuracy = sum(int(a==y) for a,y in zip(predictions, svm_validation_data[1])) / 100.0
print("Accuracy was %s percent" %accuracy)
accuracies.append(accuracy)
f = open("more_data_svm.json","w")
json.dump(accuracies,f)
f.close()
def load_data_wrapper():
"""wrapping data for network input"""
tr_d, va_d, te_d = load_data()
tr_d = (shift_data(tr_d[0]), tr_d[1])
va_d = (shift_data(va_d[0]), va_d[1])
te_d = (shift_data(te_d[0]), te_d[1])
training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]]
training_results = [vectorized_result(y) for y in tr_d[1]]
training_data = list(zip(training_inputs, training_results))
validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]]
validation_data = list(zip(validation_inputs, va_d[1]))
test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]]
test_data = list(zip(test_inputs, te_d[1]))
return (training_data, validation_data, test_data)
def svm_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
# train
clf = svm.SVC()
clf.fit(training_data[0], training_data[1])
# test
predictions = [int(a) for a in clf.predict(test_data[0])]
num_correct = sum(int(a == y) for a, y in zip(predictions, test_data[1]))
confusionMatrix = CreateConfusionMatrix(predictions, test_data[1])
avgPrecision, avgRecall, avgSpecificity, precision, recall, specificity = CalculatePrecisionAndRecall(confusionMatrix, 10, len(test_data[1]))
PrintResults(confusionMatrix, avgPrecision, avgRecall, avgSpecificity, precision, recall, specificity)
print "Baseline classifier using an SVM."
print "%s of %s values correct." % (num_correct, len(test_data[1]))
def svm_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
# train
#http://peekaboo-vision.blogspot.de/2010/09/mnist-for-ever.html
gamma = 0.00728932024638 #kernel coefficient
C = 2.82842712475 #penalty parameter of error term
clf = svm.SVC(C=C,gamma=gamma)
clf.fit(training_data[0], training_data[1])
# test
predictions = [int(a) for a in clf.predict(test_data[0])]
num_correct = sum(int(a == y) for a, y in zip(predictions, test_data[1]))
print "Baseline classifier using an SVM."
print "%s of %s values correct." % (num_correct, len(test_data[1]))
def run_svms():
svm_training_data, svm_validation_data, svm_test_data = mnist_loader.load_data()
accuracies = []
for size in SIZES:
print "\n\nTraining SVM with data set size %s" % size
clf = svm.SVC()
clf.fit(svm_training_data[0][:size], svm_training_data[1][:size])
predictions = [int(a) for a in clf.predict(svm_validation_data[0])]
accuracy = sum(int(a == y) for a, y in zip(predictions, svm_validation_data[1])) / 100.0
print "Accuracy was %s percent" % accuracy
accuracies.append(accuracy)
f = open("more_data_svm.json", "w")
json.dump(accuracies, f)
f.close()
def svm_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
# train
clf = svm.SVC()
trainingSize = 2000
clf.fit(training_data[0][:trainingSize], training_data[1][:trainingSize])
# test
predictions = [int(a) for a in clf.predict(test_data[0][:predictionSize])]
num_correct = sum(int(a == y) for a, y in zip(predictions[:predictionSize], test_data[1][:predictionSize]))
print "%s of %s values correct." % (num_correct, predictionSize)
fig = plt.figure()
pageIndex = 0
drawFigure(fig, test_data, predictions, pageIndex)
plt.show()
def svm_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
# train the model
print('Training SVM classifier on the mnist dataset.')
clf = svm.SVC()
clf.fit(training_data[0], training_data[1])
# test te model
predictions = [int(a) for a in clf.predict(test_data[0])]
num_correct = sum(int(a == y) for a, y in zip(predictions, test_data[1]))
print('Results:')
print('Accuracy: ', num_correct / len(test_data[1]))
print(f'%d out of %d classified correctly' %
(num_correct, len(test_data[1])))
def svm_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
# Initialize a support vector classifier
clf = svm.SVC()
# Train the classifier
clf.fit(X=training_data[0], y=training_data[1])
# Get predictions
predictions = [int(a) for a in clf.predict(X=test_data[0])]
num_correct = sum(a == y for a, y in zip(predictions, test_data[1]))
print("Baseline classifier using an SVM")
print("%s of %s values correct" % (num_correct, len(test_data[1])))
def main(k):
training_data, validation_data, test_data = mnist_loader.load_data()
k = int(k[0])
predictions = []
for i in range(len(test_data[0])):
neighbors = getNeighbors(training_data[0], test_data[0][i],
training_data[1], k)
result = getResponse(neighbors)
predictions.append(result)
confusionMatrix = CreateConfusionMatrix(predictions, test_data[1])
avgPrecision, avgRecall, avgSpecificity, precision, recall, specificity = CalculatePrecisionAndRecall(
confusionMatrix, 10, len(test_data[1]))
PrintResults(confusionMatrix, avgPrecision, avgRecall, avgSpecificity,
precision, recall, specificity)
accuracy = GetAccuracy(test_data[1], predictions)
print "Accuracy :", accuracy
def svm_baseline():
print('LOADING DATA...')
training_data, validation_data, test_data = mnist_loader.load_data(
'G:\Pycharmworkspace\MachineLearning_python\MaiZi-Course\data\mnist.pkl.gz'
)
print(training_data[1])
print('DONE.')
# train
print('TRAINING...')
clf = svm.SVC()
clf.fit(training_data[0], training_data[1])
# test
print('TESTING...')
predictions = [int(a) for a in clf.predict(test_data[0])]
num_correct = sum(int(a == y) for a, y in zip(predictions, test_data[1]))
print("Baseline classifier using an SVM.")
print("%s of %s values correct." % (num_correct, len(test_data[1])))
def svm_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
# train
clf = svm.SVC()
trainingSize = 2000
clf.fit(training_data[0][:trainingSize], training_data[1][:trainingSize])
# test
predictions = [int(a) for a in clf.predict(test_data[0][:predictionSize])]
num_correct = sum(
int(a == y) for a, y in zip(predictions[:predictionSize], test_data[1]
[:predictionSize]))
print "%s of %s values correct." % (num_correct, predictionSize)
fig = plt.figure()
pageIndex = 0
drawFigure(fig, test_data, predictions, pageIndex)
plt.show()
def svm_baseline():
training_data, validation_data, test_data = mnist.load_data()
# train
t0 = time.time()
clf = svm.SVC(kernel='rbf', degree=4)
from sklearn import preprocessing
X_train = preprocessing.scale(training_data[0])
scaler = preprocessing.StandardScaler().fit(training_data[0])
clf.fit(X_train, training_data[1])
print("Training time: ", time.time() - t0)
# test
t0 = time.time()
X_test = scaler.transform(test_data[0])
predictions = [int(a) for a in clf.predict(X_test)]
print("testing time:", time.time() - t0)
num_correct = sum(int(a == y) for a, y in zip(predictions, test_data[1]))
print("Baseline classifier using an SVM.")
print("%s of %s values correct." % (num_correct, len(test_data[1])))
def raport4_2():
print("raport4_2")
repeat_times = 3
training_data, validation_data, test_data = ml.load_data() # inputs - 784, ouptuts - 10
neural_network = mlp.Mlperceptron(784, 1, 10, alpha=0.005, max_epochs=20, acc_freeze=14,
default_hlayer_neuron_numbers=50, batch_size=100, winit=mlp.XAVIER,
activation_function=mlp.SIG, optimalization=mlp.ADAM)
hidden_layer_weights_r, bias_layer_r = neural_network.get_weights() # zapamiętaj najlepsze wagi w tym przypadku poczatkowo wylosowane
hidden_layer_weights = copy.deepcopy(hidden_layer_weights_r)
bias_layer = copy.deepcopy(bias_layer_r)
start = datetime.now()
start_time = start.strftime("%H:%M:%S")
t0 = time.clock()
print("start_time")
print(start_time)
training_data = training_data[0][0: 1000], training_data[1][0:1000]
validation_data = validation_data[0][0: 100], validation_data[1][0:100]
for i in range(repeat_times):
neural_network = mlp.Mlperceptron(784, 1, 10, alpha=0.005, max_epochs=20, acc_freeze=14,
default_hlayer_neuron_numbers=50, batch_size=100, winit=mlp.XAVIER,
activation_function=mlp.SIG, optimalization=mlp.ADAM)
neural_network.set_weights(copy.deepcopy(hidden_layer_weights_r), copy.deepcopy(bias_layer_r))
training_errors, val_errors, val_accuracy, stop_reason = neural_network.train(training_data[0],
training_data[1],
validation_data[0],
validation_data[1])
# accuracy = neural_network.accuracy(test_data[0], test_data[1])
accuracy = 0
tname = "conv-mlp-" + str(i + 1)
printresults2(tname, training_errors, val_errors, val_accuracy, stop_reason, accuracy)
end = datetime.now()
end_time = end.strftime("%H:%M:%S")
t1 = time.clock()
print("end_time")
print(end_time)
elapsed = t1 - t0
print("elapsed time")
print(str(timedelta(seconds=elapsed)))
def hyper_parameters():
"""Defining hyper-parameters can be a hard task. To do so, there are some heuristics that can help."""
# Load Traning datasets
training_data, validation_data, test_data = mnist_loader.load_data()
training_data = list(training_data)
validation_data = list(validation_data)
test_data = list(test_data)
# Board Strategy
# board_strategy(training_data, validation_data, test_data)
# Learning Rate Strategy
# learning_rate_strategy(training_data, validation_data, test_data)
# Early Stopping Strategy
#early_stopping_strategy(training_data, validation_data, test_data)
# Learning Rate Scheduler Strategy
learning_rate_scheduler_strategy(training_data, validation_data, test_data)
def main():
import mnist_loader
training_data, validation_data, test_data = mnist_loader.load_data()
import network
import utils
net = network.Network([784, 30, 30, 30, 10],
init=utils.NormalWeightInitializer,
cost=utils.CrossEntropyCost,
norm=utils.L2Regularizer(lmbda=0.0001))
_, evaluation_accuracy, _, _ = net.SGD(
30,
10,
.14,
training_data,
test_data,
# early_stop=utils.NoImprovementInN(10),
# learning_rate_adjustment=utils.NoImprovementInN(10),
monitor_evaluation_accuracy=True)
from fig import plot
plot(evaluation_accuracy)
def singletest():
print("single_test")
training_data, validation_data, test_data = ml.load_data() # inputs - 784, ouptuts - 10
neural_network = mlp.Mlperceptron(784, 1, 10, alpha=0.1, max_epochs=50, acc_freeze=9, default_hlayer_neuron_numbers=50, batch_size=100, optimalization=mlp.ADAM, opteta=0.9, default_act=mlp.SIG, winit=mlp.XAVIER)
# neural_network.train(training_data[0], training_data[1], validation_data[0], validation_data[1])
start = datetime.now()
start_time = start.strftime("%H:%M:%S")
t0 = time.clock()
print("start_time")
print(start_time)
neural_network.train(training_data[0], training_data[1], validation_data[0], validation_data[1])
end = datetime.now()
end_time = end.strftime("%H:%M:%S")
t1 = time.clock()
print("end_time")
print(end_time)
elapsed = t1 - t0
print("elapsed time")
print(str(timedelta(seconds=elapsed)))
print("celnosc: ")
print(neural_network.accuracy(test_data[0], test_data[1]))
def svm_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
# train
clf = svm.SVC()
clf.fit(training_data[0], training_data[1])
# test
predictions = [int(a) for a in clf.predict(test_data[0])]
num_correct = sum(int(a == y) for a, y in zip(predictions, test_data[1]))
print "Baseline classifier using an SVM."
print "%s of %s values correct." % (num_correct, len(test_data[1]))
# finally, plot the first ten images where the classifier fails
failure_indices = [j for (j, z) in enumerate(zip(predictions, test_data[1]))
if z[0] != z[1]]
failed_images = [np.reshape(test_data[0][failure_indices[j]], (-1, 28))
for j in xrange(10)]
fig = plt.figure()
for j in xrange(1, 11):
ax = fig.add_subplot(1, 10, j)
ax.matshow(failed_images[j-1], cmap = matplotlib.cm.binary)
plt.xticks(np.array([]))
plt.yticks(np.array([]))
plt.show()
from tempfile import TemporaryFile
import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import mnist_loader
training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
tr_d, va_d,te_d = mnist_loader.load_data()
import network
net =network.Network([784,64,32,10]) #network architecture design
np.savez('weightsinitial',weights=net.weights)
net.SGD(training_data, 1,10,.1 )
np.savez('weightsafter1',weights=net.weights)
net.SGD(training_data, 10,10,0.1 )
np.savez('weightsafter10',weights=net.weights)
net.SGD(training_data, 10,10,0.1 )
np.savez('weightsafter20',weights=net.weights)
net.SGD(training_data, 10,10,0.1 )
np.savez('weightsafter30',weights=net.weights)
net.SGD(training_data, 10,10,0.1 )
np.savez('weightsafter40',weights=net.weights)
net.SGD(training_data, 10,10,0.1 )
np.savez('weightsafter50',weights=net.weights)
net.SGD(training_data, 10,10,0.1 )
np.savez('weightsafter60',weights=net.weights)
net.SGD(training_data, 10,10,0.1 )
np.savez('weightsafter70',weights=net.weights)
net.SGD(training_data, 10,10,0.1 )
np.savez('weightsafter80',weights=net.weights)
net.SGD(training_data, 10,10,0.1 )
np.savez('weightsafter90',weights=net.weights)
net.SGD(training_data, 10,10,0.1 )
set affects the classification accuracy of an SVM and a neural network
classifier. The training and test data is drawn from the MNIST data
set.
"""
#### Libraries
# My libraries
import mnist_nn
import mnist_loader
# Third-party libraries
import matplotlib
import matplotlib.pyplot as plt
from sklearn import svm
svm_training_data, _, svm_test_data = mnist_loader.load_data()
nn_training_data, nn_test_inputs, nn_actual_test_results = mnist_nn.load_data()
sizes = [100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000]
svm_results, nn_results = [], []
for size in sizes:
print "\nData set size: %s" % size
# SVM results
clf = svm.SVC()
clf.fit(svm_training_data[0][:size], svm_training_data[1][:size])
predictions = [int(a) for a in clf.predict(svm_test_data[0])]
svm_results.append(
sum(int(a == y) for a, y in zip(predictions, svm_test_data[1])))
print "SVM result: %s / 10000" % svm_results[-1]
# Neural network results
net = mnist_nn.Network([784, 20, 10])
epochs = 1500000/size
Input:
- veclist: a list of Vecs
Output:
- a Vec, the centroid of veclist
Example:
>>> from vecutil import list2vec
>>> vs = [list2vec(l) for l in [[1,2,3],[2,3,4],[9,10,11]]]
>>> find_centroid(vs)
Vec({0, 1, 2},{0: 4.0, 1: 5.0, 2: 6.0})
'''
pass
# Load training and testing data
n_train, n_test = 3000, 100
images, labels = load_data(n_train+n_test)
train_images, test_images = images[:n_train], images[n_train:]
train_labels, test_labels = labels[:n_train], labels[n_train:]
## 6: Raw Error Rate
raw_nn_error_rate = ...
## 7: Training Centroid
centroid = ...
def main():
training_set, validation_set, test_set = mnist_loader.load_data()
images = get_images(training_set)
plot_rotated_image(images[0])
import matplotlib.pyplot as plt import numpy as np import math, random import threading from multiprocessing import Process, Queue import time from sklearn import svm numRepetitions = 12 # Repeat experiment for the sake of precision predictionSize = 150. numProcessors = 4 plotInterval = 1 # Putting data sets in memory training_data, validation_data, test_data = mnist_loader.load_data() # Arrays containing results of proportion correct vs sample size given q = Queue() q.put([[],[]]) def iterate(times): for t in xrange(0,times): a = 10 b = .01 c = 0 exp = lambda x: int(a ** ((x * b) + c)) log = lambda x: int(math.log(x - c, a) / b) # sizes = map(exp, xrange(log(5), log(len(training_data[0]) - 1),20)) sizes = map(exp, xrange(log(5), log(5000),plotInterval)) for trainingSize in sizes: clf = svm.SVC() # Array to contain sampled images and answers