def test_multilayer_perceptron():
def plot(fig, data):
ax = fig.add_subplot(111)
ax.plot([x[0] for x in data], [x[1] for x in data])
def scat(fig, liner_data, marker='o', color='g'):
ax = fig.add_subplot(111)
ax.scatter([x[0] for x in liner_data], [x[1] for x in liner_data], marker=marker, color=color, s=10)
def get_predict_list(x_range, y_range, nn, split=10):
data = []
xspan = float(x_range[1] - x_range[0]) / split
yspan = float(y_range[1] - y_range[0]) / split
for x_value in [ float(i)*xspan+x_range[0] for i in range(split)]:
predict_list = []
for y_value in [ float(j) * yspan + y_range[0] for j in range(split)]:
if nn.predict([x_value,y_value])[0] >= 0.5:
data.append((x_value, y_value))
break
return data
import matplotlib.pyplot as plt
# backpropのときは, mini_batch = 10
# rpropのときは, mini_batch = 100
mlnn = MultiLayerNeuralNetwork( [2, 5, 1],
threshold=0.1,
start_learning_coef=0.2,
sigmoid_alpha=10,
mini_batch=100,
epoch_limit=100,
layer_type=[LinearLayer, SigmoidLayer, LinearLayer],
rprop=True
)
x_range = [0,1]
y_range = [0,1]
#liner_data = liner_training_data(x_range, y_range)
liner_data = quadratic_function_data(x_range, y_range, split=20)
#liner_data = sin_function_data(x_range, y_range, split=20)
train_data_input, train_data_output = change_format(liner_data)
fig = plt.figure()
scat(fig, [key for key, value in liner_data.items() if value == 0], color='g' )
scat(fig, [key for key, value in liner_data.items() if value == 1], color='b' )
# 学習
error_hist, _ = mlnn.train_multi(train_data_input, train_data_output)
# xに対応するyを算出, 学習後分離線書く
data = get_predict_list(x_range,y_range, mlnn, split=20)
plot(fig, data)
# エラー表示
fig2 = plt.figure()
plot(fig2, error_hist)
# 表示
plt.show()
def test_multilayer_perceptron():
def plot(fig, data):
ax = fig.add_subplot(111)
ax.plot([x[0] for x in data], [x[1] for x in data])
def scat(fig, liner_data, marker='o', color='g'):
ax = fig.add_subplot(111)
ax.scatter([x[0] for x in liner_data], [x[1] for x in liner_data],
marker=marker,
color=color,
s=10)
def get_predict_list(x_range, y_range, nn, split=10):
data = []
xspan = float(x_range[1] - x_range[0]) / split
yspan = float(y_range[1] - y_range[0]) / split
for x_value in [float(i) * xspan + x_range[0] for i in range(split)]:
predict_list = []
for y_value in [
float(j) * yspan + y_range[0] for j in range(split)
]:
#if nn.predict([x_value,y_value])[0] >= 0.5:
if nn.activate([x_value, y_value])[0] >= 0.5:
data.append((x_value, y_value))
break
return data
import matplotlib.pyplot as plt
""" トレーニングデータ取得
"""
x_range = [0, 1]
y_range = [0, 1]
#liner_data = liner_training_data(x_range, y_range)
liner_data = quadratic_function_data(x_range, y_range, split=20)
#liner_data = sin_function_data(x_range, y_range, 20)
train_data_input, train_data_output = change_format(liner_data)
fig = plt.figure()
scat(fig, [key for key, value in liner_data.items() if value == 0],
color='g')
scat(fig, [key for key, value in liner_data.items() if value == 1],
color='b')
""" NN構築
"""
network = build_network()
# mlnn = MultiLayerNeuralNetwork( [2, 5, 1],
# threshold=0.1,
# start_learning_coef=0.2,
# sigmoid_alpha=10,
# mini_batch=100,
# layer_type=[LinearLayer, SigmoidLayer, SigmoidLayer],
# rprop=True
# )
""" 学習
"""
#error_hist = mlnn.train_multi(train_data_input, train_data_output)
supervised = get_supervised(network, train_data_input, train_data_output)
trainer = RPropMinusTrainer(network,
dataset=supervised,
batchlearning=True,
verbose=True)
trainer.trainUntilConvergence(maxEpochs=100)
# xに対応するyを算出, 学習後分離線書く
data = get_predict_list(x_range, y_range, network, split=20)
plot(fig, data)
# # エラー表示
# fig2 = plt.figure()
# plot(fig2, error_hist)
# 表示
plt.show()
def test_multilayer_perceptron():
def plot(fig, data):
ax = fig.add_subplot(111)
ax.plot([x[0] for x in data], [x[1] for x in data])
def scat(fig, liner_data, marker='o', color='g'):
ax = fig.add_subplot(111)
ax.scatter([x[0] for x in liner_data], [x[1] for x in liner_data],
marker=marker,
color=color,
s=10)
def get_predict_list(x_range, y_range, nn, split=10):
data = []
xspan = float(x_range[1] - x_range[0]) / split
yspan = float(y_range[1] - y_range[0]) / split
for x_value in [float(i) * xspan + x_range[0] for i in range(split)]:
predict_list = []
for y_value in [
float(j) * yspan + y_range[0] for j in range(split)
]:
if nn.predict([x_value, y_value])[0] >= 0.5:
data.append((x_value, y_value))
break
return data
import matplotlib.pyplot as plt
# backpropのときは, mini_batch = 10
# rpropのときは, mini_batch = 100
mlnn = MultiLayerNeuralNetwork(
[2, 5, 1],
threshold=0.1,
start_learning_coef=0.2,
sigmoid_alpha=10,
mini_batch=100,
epoch_limit=100,
layer_type=[LinearLayer, SigmoidLayer, LinearLayer],
rprop=True)
x_range = [0, 1]
y_range = [0, 1]
#liner_data = liner_training_data(x_range, y_range)
liner_data = quadratic_function_data(x_range, y_range, split=20)
#liner_data = sin_function_data(x_range, y_range, split=20)
train_data_input, train_data_output = change_format(liner_data)
fig = plt.figure()
scat(fig, [key for key, value in liner_data.items() if value == 0],
color='g')
scat(fig, [key for key, value in liner_data.items() if value == 1],
color='b')
# 学習
error_hist, _ = mlnn.train_multi(train_data_input, train_data_output)
# xに対応するyを算出, 学習後分離線書く
data = get_predict_list(x_range, y_range, mlnn, split=20)
plot(fig, data)
# エラー表示
fig2 = plt.figure()
plot(fig2, error_hist)
# 表示
plt.show()
def test_multilayer_perceptron():
def plot(fig, data):
ax = fig.add_subplot(111)
ax.plot([x[0] for x in data], [x[1] for x in data])
def scat(fig, liner_data, marker='o', color='g'):
ax = fig.add_subplot(111)
ax.scatter([x[0] for x in liner_data], [x[1] for x in liner_data], marker=marker, color=color, s=10)
def get_predict_list(x_range, y_range, nn, split=10):
data = []
xspan = float(x_range[1] - x_range[0]) / split
yspan = float(y_range[1] - y_range[0]) / split
for x_value in [ float(i)*xspan+x_range[0] for i in range(split)]:
predict_list = []
for y_value in [ float(j) * yspan + y_range[0] for j in range(split)]:
#if nn.predict([x_value,y_value])[0] >= 0.5:
if nn.activate([x_value,y_value])[0] >= 0.5:
data.append((x_value, y_value))
break
return data
import matplotlib.pyplot as plt
""" トレーニングデータ取得
"""
x_range = [0,1]
y_range = [0,1]
#liner_data = liner_training_data(x_range, y_range)
liner_data = quadratic_function_data(x_range, y_range, split=20)
#liner_data = sin_function_data(x_range, y_range, 20)
train_data_input, train_data_output = change_format(liner_data)
fig = plt.figure()
scat(fig, [key for key, value in liner_data.items() if value == 0], color='g' )
scat(fig, [key for key, value in liner_data.items() if value == 1], color='b' )
""" NN構築
"""
network = build_network()
# mlnn = MultiLayerNeuralNetwork( [2, 5, 1],
# threshold=0.1,
# start_learning_coef=0.2,
# sigmoid_alpha=10,
# mini_batch=100,
# layer_type=[LinearLayer, SigmoidLayer, SigmoidLayer],
# rprop=True
# )
""" 学習
"""
#error_hist = mlnn.train_multi(train_data_input, train_data_output)
supervised = get_supervised(network, train_data_input, train_data_output)
trainer = RPropMinusTrainer(network, dataset=supervised, batchlearning=True, verbose=True)
trainer.trainUntilConvergence(maxEpochs=100)
# xに対応するyを算出, 学習後分離線書く
data = get_predict_list(x_range,y_range, network, split=20)
plot(fig, data)
# # エラー表示
# fig2 = plt.figure()
# plot(fig2, error_hist)
# 表示
plt.show()