forked from eyalsty/ML--First-Neural-Network
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crossValidation.py
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crossValidation.py
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import NeuralNet
from NeuralNet import NeuralNetwork
import numpy as np
import Norm as norm
import timeit
def cross_validate(network_shape, epochs_num, learn_rate, _groups_x, _groups_y):
k = _groups_x.shape[0]
_sum = 0
results = np.zeros(k)
for i in range(k):
train_x = None
train_y = None
valid_x = np.copy(_groups_x[i]) # the validation set for th i'th iteration.
valid_y = np.copy(_groups_y[i])
net = NeuralNetwork(network_shape, epochs_num, learn_rate)
for j in range(k):
if j != i:
# arrange the train set for the i'th iteration.
if train_x is None:
train_x = np.copy(_groups_x[j])
train_y = np.copy(_groups_y[j])
else:
train_x = np.concatenate((train_x, _groups_x[j]), axis=0)
train_y = np.concatenate((train_y, _groups_y[j]), axis=0)
old_mins, denoms = norm.minmax_params(train_x)
train_x = norm.minmax(train_x, 0, 1)
valid_x = norm.minmax(valid_x, 0, 1, old_mins, denoms)
net.train(train_x, train_y)
results[i] = net.accuracy(valid_x, valid_y)
old_mins, denoms = norm.minmax_params(train_x)
train_x = norm.minmax(train_x, 0, 1)
valid_x = norm.minmax(valid_x, 0, 1, old_mins, denoms)
print(results)
return np.average(results)
def plot_epochs(max_epochs, l_rate, net_shape):
for epochs_num in range(max_epochs):
start = timeit.default_timer()
print("results of {} epochs:".format(epochs_num))
average = cross_validate(net_shape, epochs_num, l_rate, _groups_x, _groups_y)
print("Average Of:{}".format(average))
stop = timeit.default_timer()
print('Time Took: {} seconds '.format(stop - start))
samples = NeuralNet.load_samples("train_x")
labels = NeuralNet.load_labels("train_y")
n_groups = 5
max_epochs = 6
l_rate = 0.1
net_shape = [784, 24, 10]
_groups_x = np.copy(np.array_split(samples, n_groups))
_groups_y = np.copy(np.array_split(labels, n_groups))
plot_epochs(max_epochs, l_rate, net_shape)
net_shape = [784, 100, 10]
max_epochs = 15
l_rate = 0.111
plot_epochs(max_epochs, l_rate, net_shape)