from neural_network.activations import * from neural_network.data_manipulation import load_easy_data from neural_network.plots import plot_data_2d, plot_measure_results_data from neural_network.learn_network import learn_network import matplotlib.pyplot as plt # load dataset train, test = load_easy_data() # x and y - observations and values for them x = train[:, 0:2] y = train[:, 2:3] # plot data classes plt.figure(figsize=(12.8, 9.6)) plt.subplot(221) plot_data_2d(x[:, 0], x[:, 1], y[:, 0], title='True classes of points on the plane', show=False) # learn model and plot result classes plt.subplot(222) mse_linear = learn_network(x, y, [20], [sigmoid, linear], iterations=100, regression=False, plot_title='Predicted classes for linear function', plot_show=False) plt.subplot(223) mse_softmax = learn_network(x, y, [20], [sigmoid, softmax], iterations=100, regression=False, plot_title='Predicted classes for softmax function', plot_show=False) plt.subplot(224) plot_measure_results_data([mse_linear, mse_softmax], labels=['linear', 'softmax'], title_base="Accuracy", ylabel="Accuracy", title_ending=" for last layer activation function", show=False) plt.show()
from neural_network.activations import *
from neural_network.learn_network import learn_network
from neural_network.data_manipulation import load_data
from neural_network.plots import plot_data_1d_compare, plot_measure_results_data
train, test = load_data('multimodal-large')
x = train[:, 0:1]
y = train[:, 1:2]
# weights experiment
mse_zeros, zeros = learn_network(x,
y, [20], [sigmoid, linear],
initialize_weights='zeros',
iterations=100,
momentum_type='normal',
plot=False,
return_result=True)
mse_normal, normal = learn_network(x,
y, [20], [sigmoid, linear],
initialize_weights='norm',
iterations=100,
momentum_type='normal',
plot=False,
return_result=True)
mse_Xavier, Xavier = learn_network(x,
y, [20], [sigmoid, linear],
initialize_weights='Xavier',
iterations=100,
momentum_type='normal',
plot=False,
'L2, lambda: 0.0001'
]
# learn model and plot result classes
i = 2
activation = sigmoid
iterations = 10000
no_change_epochs_to_stop = 100
mse_no_reg, res_no_reg = learn_network(
x,
y,
neurons[:i], [activation] * i + [linear],
beta=0.01,
eta=0.01,
epochs=1,
iterations=iterations,
plot_title="Prediction with sigmoid activation function and " + str(i) +
" hidden layers",
plot=False,
return_result=True,
x_test=x_test,
y_test=y_test,
use_test_and_stop_learning=True,
no_change_epochs_to_stop=no_change_epochs_to_stop)
mses = [mse_no_reg]
results = [res_no_reg]
for reg_type in ["L1", "L2"]:
for reg_lambda in [0.01, 0.001, 0.0001]:
mse_reg, res_reg = learn_network(
x,
y,
neurons[:i], [activation] * i + [linear],
plot_data_2d(x[:, 0],
x[:, 1],
y[:, 0],
title='True classes of points on the plane',
show=False)
neurons = [50, 50, 50]
# learn model and plot result classes for i layer networks
for i in range(1, 4):
mse_linear = learn_network(
x,
y,
neurons[:i], [linear] * i + [softmax],
beta=0.01,
eta=0.01,
epochs=1,
iterations=1000,
regression=False,
plot_title="Prediction with linear activation function and " + str(i) +
" hidden layers",
plot=False)
mse_ReLU = learn_network(
x,
y,
neurons[:i], [ReLU] * i + [softmax],
beta=0.01,
eta=0.01,
epochs=1,
iterations=1000,
regression=False,
plot_title="Prediction with ReLU activation function and " + str(i) +
y = train[:, 1:2]
# neurons in each layer
neurons = [50, 50, 50]
# labels for plots
labels = ['linear', 'ReLU', 'sigmoid', 'tanh']
# learn model and plot result classes
plt.figure(figsize=(12.8, 19.2))
for i in range(1, 4):
mse_linear, res_linear = learn_network(
x,
y,
neurons[:i], [linear] * i + [linear],
beta=0.01,
eta=0.01,
epochs=1,
iterations=300,
plot_title="Prediction with linear activation function and " + str(i) +
" hidden layers",
plot=False,
return_result=True)
mse_ReLU, res_ReLU = learn_network(
x,
y,
neurons[:i], [ReLU] * i + [linear],
beta=0.01,
eta=0.01,
epochs=1,
iterations=300,
plot_title="Prediction with ReLU activation function and " + str(i) +
" hidden layers",
from neural_network.activations import *
from neural_network.data_manipulation import load_data
from neural_network.plots import plot_measure_results_data, plot_data_1d_compare
from neural_network.learn_network import learn_network
# load dataset
train, test = load_data('square-large')
x = train[:, 0:1]
y = train[:, 1:2]
# learn network without any momentum technique and show model
mse, base = learn_network(x,
y, [20], [sigmoid, linear],
momentum_type='normal',
eta=0.01,
epochs=1,
iterations=100,
plot=False,
return_result=True)
# learn network with momentum and show model
mse_mom, mom = learn_network(x,
y, [20], [sigmoid, linear],
momentum_type='momentum',
lambda_momentum=0.9,
eta=0.01,
epochs=1,
iterations=100,
plot=False,
return_result=True)