def test_log_scale(self):
original_image_name = format_image_name("log_scale.png")
original_image = os.path.join(IMGDIR, original_image_name)
with image_comparison(original_image) as fig:
ax = fig.add_subplot(1, 1, 1)
network = reproducible_network_train(step=0.3)
plots.error_plot(network, logx=True, ax=ax, show=False)
def test_log_scale(self):
original_image_name = format_image_name("log_scale.png")
original_image = os.path.join(IMGDIR, original_image_name)
with image_comparison(original_image) as fig:
ax = fig.add_subplot(1, 1, 1)
network = reproducible_network_train(step=0.3)
plots.error_plot(network, logx=True, ax=ax, show=False)
def test_error_plot_and_validation_error_warnings(self):
with catch_stdout() as out:
network = algorithms.GradientDescent((2, 3, 1), verbose=True)
network.errors = ErrorHistoryList([1, 2])
network.validation_errors = ErrorHistoryList([None])
plots.error_plot(network, ax=None, show=False)
terminal_output = out.getvalue()
self.assertIn("error will be ignored", terminal_output)
def test_error_plot_and_validation_error_warnings(self):
with catch_stdout() as out:
network = algorithms.GradientDescent((2, 3, 1), verbose=True)
network.errors = ErrorHistoryList([1, 2])
network.validation_errors = ErrorHistoryList([None])
plots.error_plot(network, ax=None, show=False)
terminal_output = out.getvalue()
self.assertIn("error will be ignored", terminal_output)
def test_plot_with_validation_dataset(self):
original_image_name = format_image_name("with_validation.png")
original_image = os.path.join(IMGDIR, original_image_name)
with image_comparison(original_image) as fig:
ax = fig.add_subplot(1, 1, 1)
x_train, x_test, y_train, y_test = simple_classification()
gdnet = algorithms.GradientDescent((10, 12, 1), step=0.25)
gdnet.train(x_train, y_train, x_test, y_test, epochs=100)
plots.error_plot(gdnet, ax=ax, show=False)
def test_plot_with_validation_dataset(self):
original_image_name = format_image_name("with_validation.png")
original_image = os.path.join(IMGDIR, original_image_name)
with image_comparison(original_image) as fig:
ax = fig.add_subplot(1, 1, 1)
x_train, x_test, y_train, y_test = simple_classification()
gdnet = algorithms.GradientDescent((10, 12, 1), step=0.25)
gdnet.train(x_train, y_train, x_test, y_test, epochs=100)
plots.error_plot(gdnet, ax=ax, show=False)
def test_error_plot_show_image(self):
def mock_plt_show():
pass
# Test suppose not to fail
real_plt_show = plt.show
plt.show = mock_plt_show
network = reproducible_network_train(step=0.3)
plots.error_plot(network, show=True)
plt.show = real_plt_show
def test_error_plot_show_image(self):
def mock_plt_show():
pass
# Test suppose not to fail
real_plt_show = plt.show
plt.show = mock_plt_show
network = reproducible_network_train(step=0.3)
plots.error_plot(network, show=True)
plt.show = real_plt_show
def test_error_plot_ax_none(self):
ax = plt.gca()
network = algorithms.GradientDescent((2, 3, 1))
ax_returned = plots.error_plot(network, ax=None, show=False)
self.assertIs(ax_returned, ax)
def test_error_plot_ax_none(self):
ax = plt.gca()
network = algorithms.GradientDescent((2, 3, 1))
ax_returned = plots.error_plot(network, ax=None, show=False)
self.assertIs(ax_returned, ax)
def run_neural_net():
import_modules()
dataset = datasets.load_boston()
data, target = dataset.data, dataset.target
data_scalar = preprocessing.MinMaxScaler()
target_scalar = preprocessing.MinMaxScaler()
data = data_scalar.fit_transform(data)
target = target_scalar.fit_transform(target.reshape(-1, 1))
environment.reproducible()
x_train, x_test, y_train, y_test = train_test_split(data,
target,
train_size=0.85)
cgnet = algorithms.ConjugateGradient(
connection=[
layers.Input(13),
layers.Sigmoid(75),
layers.Sigmoid(25),
layers.Sigmoid(1),
],
search_method='golden',
show_epoch=1,
verbose=True,
addons=[algorithms.LinearSearch],
)
cgnet.train(x_train, y_train, x_test, y_test, epochs=30)
plots.error_plot(cgnet)
y_predict = cgnet.predict(x_test).round(1)
error = rmsle(target_scalar.invers_transform(y_test), \
target_scalar.invers_transform(y_predict))
return (error)
def go(self):
raw = self.datafile.read().splitlines()
data = self._prepare_data(raw[::2])
target = self._prepare_target(raw[1::2])
print(len(data))
print(len(target))
environment.reproducible()
x_train, x_test, y_train, y_test = train_test_split(data,
target,
train_size=0.85)
print(x_train[0])
connections = [
layers.Input(100),
layers.Linear(200),
layers.Sigmoid(150),
layers.Sigmoid(5),
]
cgnet = algorithms.ConjugateGradient(
connection=connections,
search_method='golden',
show_epoch=25,
verbose=True,
addons=[algorithms.LinearSearch],
)
cgnet.train(x_train, y_train, x_test, y_test, epochs=100)
plots.error_plot(cgnet)
y_predict = cgnet.predict(x_test).round(1)
error = rmsle(y_test, y_predict)
print(error)
with open('lib/net/base_searcher.pickle', 'wb') as f:
pickle.dump(cgnet, f)
layers.Relu(100),
layers.Relu(1),
],
search_method='golden',
show_epoch=1,
verbose=True,
addons=[algorithms.LinearSearch],
step=0.01,
epoch_end_signal=on_epoch_end,
error='rmse',
)
cgnet.train(training_X, training_Y, testing_X, testing_Y, epochs=1000)
from neupy import plots
plots.error_plot(cgnet)
y_predicted = cgnet.predict(testing_X)
print("RMSE = " + str(estimators.rmse(y_predicted, testing_Y.ravel())))
print("MAE = " + str(estimators.mae(y_predicted, testing_Y.ravel())))
actual_mae = y_data_scaler.inverse_transform(
estimators.mae(y_predicted, testing_Y))
print("MAE (no. of shares) = " + str(actual_mae.squeeze()))
# # SOM try out
# from neupy import algorithms
#
# num_epochs = 100
# num_clusters = 3
# num_features = training_X.shape[1]
#
# sofm = algorithms.SOFM(n_inputs=num_features, n_outputs=num_clusters, step=0.1, learning_radius=0, verbose=True,
import numpy as np from neupy import algorithms, plots x_train = np.array([[1, 2], [3, 4]]) y_train = np.array([[1], [0]]) lmnet = algorithms.LevenbergMarquardt((2, 3, 1)) lmnet.train(x_train, y_train) plots.error_plot(lmnet)
],
batch_size=128,
step=0.1,
# Using Mean Squared Error as the Loss Function
error='mse',
# Learning Rate
#step=1.0,
# Display network data in console
verbose=True,
# shuffle training data random before each epoch
shuffle_data=True,
show_epoch=1)
# Show network architecture in console
network.architecture()
network.train(x_train, y_train, x_test, y_test, epochs=70)
plots.error_plot(network)
# Making test filters
MIN_POWER = np.min(x_train)
MAX_POWER = np.max(x_train)
FrequencyTestArray = np.empty([500, 500], dtype='float32')
FrequencyTestArray[:, :] = MAX_POWER
np.fill_diagonal(FrequencyTestArray[0:500, :], MIN_POWER)
DoubleTestArray = np.empty([250, 500], dtype='float32')
DoubleTestArray[:, :] = MAX_POWER
for i in range(0, 250):
DoubleTestArray[i, i] = MIN_POWER
DoubleTestArray[i, -(i + 1)] = MIN_POWER
def show_plow(self): plots.error_plot(self.network)
#se crea la red neuronal con la arquitectura 57 -7 -1
rpropnet = algorithms.RPROP(
[
layers.Input(57),
layers.Sigmoid(7),
layers.Sigmoid(1),
],
error='mse',
verbose=True,
shuffle_data=True,
maxstep=1,
minstep=1e-7,
)
#se realiza el entrenamiento de la red
rpropnet.train(input_train=x_train,target_train=y_train,epochs=200)
#se muestra un grafico de los errores cometidos en el entrenamiento
plots.error_plot(rpropnet)
y_train_predicted = rpropnet.predict(x_train).round()
y_test_predicted = rpropnet.predict(x_test).round()
# se muestran las predicciones
print(metrics.classification_report(y_train_predicted, y_train))
print(metrics.confusion_matrix(y_train_predicted, y_train))
print()
print(metrics.classification_report(y_test_predicted, y_test))
print(metrics.confusion_matrix(y_test_predicted, y_test))