x = np.zeros((nc*3, 1))
for i in range(nc*3):
x[i][0] = random.choice([0, 1])
y = np.zeros((1, 1))
y[0][0] = f(x)
data.append((x, y))
return data
nhidden = 5 # number of neurons in the hidden layer
ntrain = 20 # number of train data samples
ntest = 2 # number of test data samples
nc = 10 # number of clauses: (x0 or x1 or x2) and ...
epochs = 2 # number of iterations for the stochastic gradient descent
batch_size = 10 # size of each batch for the stochastic gradient descent
learn_rate = 1.0 # learn rate
lmbda = 0.1 # regularization factor
# generate data
train_data = gen_data(ntrain, nc)
test_data = gen_data(ntest, nc)
# create and train the neural network
nn = NeuralNetwork([nc*3, nhidden, 1], cost.Quadratic, activation.Sigmoid)
test_func = lambda o, y: abs(o[0][0] - y[0][0]) <= 0.5
nn.learn(train_data, epochs, batch_size, learn_rate, lmbda,
test_data=test_data, test_func=test_func)
return data
def load_train_data():
train_data = read_idx_gzfile('data/mnist/train-images-idx3-ubyte.gz')
labels = read_idx_gzfile('data/mnist/train-labels-idx1-ubyte.gz')
return zip(train_data, labels)
def load_test_data():
test_data = read_idx_gzfile('data/mnist/t10k-images-idx3-ubyte.gz')
labels = read_idx_gzfile('data/mnist/t10k-labels-idx1-ubyte.gz')
return list(zip(test_data, labels))
data = load_test_data()
train_data = data[:9000]
test_data = data[9000:]
epochs = 1000 # number of iterations for the stochastic gradient descent
batch_size = 10 # size of each batch for the stochastic gradient descent
learn_rate = 3.0 # learn rate
lmbda = 0.0 # regularization factor
# create and train the neural network
nn = NeuralNetwork([784, 30, 10], cost.CrossEntropy, activation.Sigmoid)
test_func = lambda o, y: o.argmax() == y.argmax()
nn.learn(train_data, epochs, batch_size, learn_rate, lmbda,
test_data=test_data, test_func=test_func, report_freq=10)
