from keras.layers import Dense, Activation
from keras.models import Sequential
from ADDITIONAL.CUSTOM_KERAS import hard_lim
import numpy as np
import LABS.ZeroLab.D_DivIntoNClasses as dataset4
if __name__ == '__main__':
train_size = 100
(x_train, y_train), (x_test,
y_test) = dataset4.load_data(train_size=train_size,
show=True)
model = Sequential()
model.add(
Dense(17,
input_dim=x_train.shape[1],
activation=Activation(hard_lim),
name='1',
weights=list([
np.array([[
1.0, -1.0, 0.0, 0.0, 1.0, -1.0, 0.0, 0.0, -1.0, 0.0, 1.0,
0.0, 1.0, -1.0, 0.0, 1.0, 0.0
],
[
0.0, 0.0, 1.0, -1.0, 0.0, 0.0, 1.0, -1.0, 0.0,
-1.0, 1.0, 1.0, 0.0, -1.0, 1.0, 0.0, -1.0
]],
if __name__ == '__main__':
np.random.seed(42)
# 1,2 initializing
train_size = 16000
batch_size = 160
epochs = 1000
lr = 0.007
verbose = 1
neurons_number = [40, 35, 7]
opt_name = "Adam"
optimizer = Adam(lr=lr)
goal_loss = 0.013
(x_train, y_train), (x_test, y_test) = dataset4.load_data(train_size=train_size, show=True)
model = Sequential()
model.add(Dense(neurons_number[0], input_dim=2, activation='relu'))
model.add(Dense(neurons_number[1], activation='linear'))
model.add(Dense(neurons_number[2], activation='sigmoid'))
# plot_model(model, to_file="C_Model.png", show_shapes=True, show_layer_names=True)
# 3 setting stopper
# callbacks.EarlyStopping(monitor='acc', min_delta=0, patience=5, mode='max')
callbacks = [EarlyStoppingByLossVal(monitor='val_loss', value=goal_loss, verbose=1)]
def diff_std():
(x_train, y_train), (x_test,
y_test) = dataset4.load_data_neupy(train_size=12000,
show=True)
titles = [
"\n\nspread greater than necessary", "\n\nspread optimal",
"\n\nspread less than necessary"
]
spreads = [0.01, 0.001, 0.0001]
for spread, title in zip(spreads, titles):
pnn = algorithms.PNN(std=spread, verbose=True)
pnn.train(x_train, y_train)
y_predicted = pnn.predict(x_test)
mae = (np.abs(y_test - y_predicted)).mean()
plt_x_zero = np.empty(0)
plt_y_zero = np.empty(0)
plt_x_one = np.empty(0)
plt_y_one = np.empty(0)
plt_x_two = np.empty(0)
plt_y_two = np.empty(0)
plt_x_three = np.empty(0)
plt_y_three = np.empty(0)
plt_x_four = np.empty(0)
plt_y_four = np.empty(0)
plt_x_five = np.empty(0)
plt_y_five = np.empty(0)
plt_x_six = np.empty(0)
plt_y_six = np.empty(0)
i = 0
for predict in y_predicted:
if predict == 0.0:
plt_x_zero = np.append(plt_x_zero, x_test[i][0])
plt_y_zero = np.append(plt_y_zero, x_test[i][1])
elif predict == 0.1:
plt_x_one = np.append(plt_x_one, x_test[i][0])
plt_y_one = np.append(plt_y_one, x_test[i][1])
elif predict == 0.2:
plt_x_two = np.append(plt_x_two, x_test[i][0])
plt_y_two = np.append(plt_y_two, x_test[i][1])
elif predict == 0.3:
plt_x_three = np.append(plt_x_three, x_test[i][0])
plt_y_three = np.append(plt_y_three, x_test[i][1])
elif predict == 0.4:
plt_x_four = np.append(plt_x_four, x_test[i][0])
plt_y_four = np.append(plt_y_four, x_test[i][1])
elif predict == 0.5:
plt_x_five = np.append(plt_x_five, x_test[i][0])
plt_y_five = np.append(plt_y_five, x_test[i][1])
elif predict == 0.6:
plt_x_six = np.append(plt_x_six, x_test[i][0])
plt_y_six = np.append(plt_y_six, x_test[i][1])
i += 1
plt.plot(plt_x_zero, plt_y_zero, '.')
plt.plot(plt_x_one, plt_y_one, '.')
plt.plot(plt_x_two, plt_y_two, '.')
plt.plot(plt_x_three, plt_y_three, '.')
plt.plot(plt_x_four, plt_y_four, '.')
plt.plot(plt_x_five, plt_y_five, '.')
plt.plot(plt_x_six, plt_y_six, '.')
plt.xlim(0, 1.5)
plt.ylim(0, 1)
plt.legend(('0.0 class', '0.1 class', '0.2 class', '0.3 class',
'0.4 class', '0.5 class', '0.6 class'),
loc='upper right',
shadow=True)
plt.title(title + '\n7 classification\nspread =%.4f\nmae = %.4f' %
(spread, mae))
plt.show()
plt.close()
def diff_train():
maes = [0, 0, 0, 0]
train_size = [24000, 12000, 5000, 2000]
std = [0.0004, 0.001, 0.0035, 0.01]
for j in range(0, 4):
(x_train, y_train), (x_test, y_test) = dataset4.load_data_neupy(
train_size=train_size[j], show=False)
pnn = algorithms.PNN(std=std[j], verbose=True)
pnn.train(x_train, y_train)
y_predicted = pnn.predict(x_test)
mae = (np.abs(y_test - y_predicted)).mean()
plt_x_zero = np.empty(0)
plt_y_zero = np.empty(0)
plt_x_one = np.empty(0)
plt_y_one = np.empty(0)
plt_x_two = np.empty(0)
plt_y_two = np.empty(0)
plt_x_three = np.empty(0)
plt_y_three = np.empty(0)
plt_x_four = np.empty(0)
plt_y_four = np.empty(0)
plt_x_five = np.empty(0)
plt_y_five = np.empty(0)
plt_x_six = np.empty(0)
plt_y_six = np.empty(0)
acc = 0.0
i = 0
for predict in y_predicted:
if predict == 0.0:
plt_x_zero = np.append(plt_x_zero, x_test[i][0])
plt_y_zero = np.append(plt_y_zero, x_test[i][1])
elif predict == 0.1:
plt_x_one = np.append(plt_x_one, x_test[i][0])
plt_y_one = np.append(plt_y_one, x_test[i][1])
elif predict == 0.2:
plt_x_two = np.append(plt_x_two, x_test[i][0])
plt_y_two = np.append(plt_y_two, x_test[i][1])
elif predict == 0.3:
plt_x_three = np.append(plt_x_three, x_test[i][0])
plt_y_three = np.append(plt_y_three, x_test[i][1])
elif predict == 0.4:
plt_x_four = np.append(plt_x_four, x_test[i][0])
plt_y_four = np.append(plt_y_four, x_test[i][1])
elif predict == 0.5:
plt_x_five = np.append(plt_x_five, x_test[i][0])
plt_y_five = np.append(plt_y_five, x_test[i][1])
elif predict == 0.6:
plt_x_six = np.append(plt_x_six, x_test[i][0])
plt_y_six = np.append(plt_y_six, x_test[i][1])
i += 1
plt.plot(plt_x_zero, plt_y_zero, '.')
plt.plot(plt_x_one, plt_y_one, '.')
plt.plot(plt_x_two, plt_y_two, '.')
plt.plot(plt_x_three, plt_y_three, '.')
plt.plot(plt_x_four, plt_y_four, '.')
plt.plot(plt_x_five, plt_y_five, '.')
plt.plot(plt_x_six, plt_y_six, '.')
plt.xlim(0, 1.5)
plt.ylim(0, 1)
plt.legend(('0.0 class', '0.1 class', '0.2 class', '0.3 class',
'0.4 class', '0.5 class', '0.6 class'),
loc='upper right',
shadow=True)
plt.title('7 classification\ntrain_size = %d\nstd =%.4f\nmae = %.4f' %
(train_size[j], std[j], mae))
maes[j] = mae
plt.show()
plt.close()
return train_size, std, maes
def load_data(train_size=2000, show=False):
test_size = int(train_size * 0.2)
x_train = np.empty(0)
y_train = np.empty(0)
x_test = np.empty(0)
y_test = np.empty(0)
x_train_for_plt = np.empty(0)
x_train_missed_for_plt = np.empty(0)
x_test_for_plt = np.empty(0)
x_test_missed_for_plt = np.empty(0)
for i in range(train_size + test_size):
x = np.random.random()
y = np.random.random()
if i < train_size:
x_train = np.append(x_train, (x, y))
else:
x_test = np.append(x_test, (x, y))
if dataset4.isRect(x, y, xMin=0.2, xMax=0.6, yMin=0.1, yMax=0.5) or \
dataset4.isTriangle(x, y, x1=0.5, x2=0.9, x3=0.9, y1=0.9, y2=0.5, y3=0.9):
if i < train_size:
x_train_for_plt = np.append(x_train_for_plt, (x, y))
y_train = np.append(y_train, 1)
else:
x_test_for_plt = np.append(x_test_for_plt, (x, y))
y_test = np.append(y_test, 1)
else:
if i < train_size:
x_train_missed_for_plt = np.append(x_train_missed_for_plt,
(x, y))
y_train = np.append(y_train, 0)
else:
x_test_missed_for_plt = np.append(x_test_missed_for_plt,
(x, y))
y_test = np.append(y_test, 0)
# Reshaping
x_train.shape = (train_size, 2)
x_test.shape = (test_size, 2)
x_train_for_plt.shape = (int(x_train_for_plt.size / 2), 2)
x_train_missed_for_plt.shape = (int(x_train_missed_for_plt.size / 2), 2)
x_test_for_plt.shape = (int(x_test_for_plt.size / 2), 2)
x_test_missed_for_plt.shape = (int(x_test_missed_for_plt.size / 2), 2)
# Plotting train
plt.xlim(0, 1.3)
plt.ylim(0, 1)
plt.title("train data")
plt.plot(x_train_for_plt.transpose()[0],
x_train_for_plt.transpose()[1], '.')
plt.plot(x_train_missed_for_plt.transpose()[0],
x_train_missed_for_plt.transpose()[1], '.')
plt.legend(('0 class', '1 class'), loc='upper right', shadow=True)
if show:
plt.show()
plt.close()
# Plotting test
plt.xlim(0, 1.3)
plt.ylim(0, 1)
plt.title("test data")
plt.plot(x_test_for_plt.transpose()[0], x_test_for_plt.transpose()[1], '.')
plt.plot(x_test_missed_for_plt.transpose()[0],
x_test_missed_for_plt.transpose()[1], '.')
plt.legend(('0 class', '1 class'), loc='upper right', shadow=True)
if show:
plt.show()
plt.close()
return (x_train, y_train), (x_test, y_test)