def ejer3():
reg1 = regularizador.L2(0.0001)
reg2 = regularizador.L2(0.0001)
proto = clasificador.Classifier(epochs=400, batch_size=50, eta=0.003)
outputfile = 'ejer3_v2_mse.dat'
(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()
mean_train = x_train.mean()
n_clasifi = 10
X, Y = clasificador.flattening(x_train, y_train, n_clasifi, mean_train)
X_test, Y_test = clasificador.flattening(x_test, y_test, n_clasifi,
mean_train)
proto.fit(X,
Y,
X_test,
Y_test,
act_function1=act.sigmoid(),
reg1=reg1,
act_function2=act.Linear(),
reg2=reg2,
loss_function=los.MSE())
# plt.figure(1)
# plt.ylabel("Accuracy [%]")
# plt.plot(proto.acc_vect, label="Entrenamiento", c='red', alpha=0.6, ls='--')
# plt.plot(proto.pres_vect, label="Validación", c='blue', alpha=0.6)
# plt.legend(loc=0)
# plt.savefig("ejer3_acc.pdf")
# plt.figure(2)
# plt.ylabel("Pérdida")
# plt.plot(proto.loss_vect, label="Entrenamiento", c='red', alpha=0.6, ls='--')
# plt.plot(proto.loss_test, label="Validación", c='blue', alpha=0.6)
# plt.legend(loc=0)
# plt.savefig("ejer3_loss.pdf")
# plt.close()
np.save(
outputfile,
np.array([
proto.acc_vect, proto.pres_vect, proto.loss_vect, proto.loss_test
]).T)
#plt.show()
pass
def ejer5_loss(loss_fun, act_fun_last, label, nfig1, nfig2):
print(label)
reg1 = regularizador.L2(0.1)
reg2 = regularizador.L2(0.1)
proto = clasificador.Classifier(epochs=300, batch_size=50, eta=0.001)
outputfile = "ejer5_" + label + "_v3.dat"
(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()
mean_train = x_train.mean()
n_clasifi = 10
X, Y = clasificador.flattening(x_train, y_train, n_clasifi, mean_train)
X_test, Y_test = clasificador.flattening(x_test, y_test, n_clasifi,
mean_train)
proto.fit(X,
Y,
X_test,
Y_test,
act_function1=act.ReLU(0),
reg1=reg1,
loss_function=loss_fun,
act_function2=act_fun_last,
reg2=reg2)
# plt.figure(nfig1)
# plt.ylabel("Accuracy [%]")
# plt.plot(proto.acc_vect, label="Entrenamiento", c='red', alpha=0.6, ls='--')
# plt.plot(proto.pres_vect, label="Validación", c='blue', alpha=0.6)
# plt.legend(loc=0)
# plt.savefig("ejer5_acc_"+label+".pdf")
# plt.figure(nfig2)
# plt.ylabel("Pérdida")
# plt.plot(proto.loss_vect, label="Entrenamiento", c='red', alpha=0.6, ls='--')
# plt.plot(proto.loss_test, label="Validación", c='blue', alpha=0.6)
# plt.legend(loc=0)
# plt.savefig("ejer5_loss_"+label+".pdf")
# plt.show()
#plt.close()
#plt.clf()
np.savetxt(
outputfile,
np.array([
proto.acc_vect, proto.pres_vect, proto.loss_vect, proto.loss_test
]).T)
def ejer6_221(x_train, y_train):
reg1 = regularizador.L2(0.0)
reg2 = regularizador.L2(0.0)
red_densa = model.Red()
input_size=x_train.shape[1]
Layer1= layer.Dense(neuronas = input_size,
act = activation.Tanh(),
reg = reg1,
name ="Layer 1" ,
bias = True )
Layer2= layer.Dense(neuronas = 2,
act = activation.Tanh(),
reg = reg2,
name = "Layer 2",
bias = True)
red_densa.add(Layer1)
red_densa.add(Layer2)
red_densa.fit(
x_train=x_train, y_train=y_train,
batch_size=1,
epochs=300,
opt=optimizer.SGD(lr=0.1),
loss_function=loss.MSE(),
acc_function=metric.accuracy_xor)
plt.figure(1)
plt.ylabel("Accuracy [%]")
plt.plot(red_densa.acc_vect, label="221", c='red', alpha=0.6)
#plt.plot(red_densa.pres_vect, label="Validación", c='blue', alpha=0.6)
plt.legend(loc=0)
#plt.savefig("ejer6_acc.pdf")
plt.figure(2)
plt.ylabel("Pérdida")
plt.plot(red_densa.loss_vect/np.max(red_densa.loss_vect), label="221", c='red', alpha=0.6)
#plt.plot(red_densa.loss_test, label="Validación", c='blue', alpha=0.6)
plt.legend(loc=0)
#plt.savefig("ejer6_loss.pdf")
#plt.show()
np.savetxt("ejer6_221.txt", np.array([
red_densa.acc_vect,
red_densa.loss_vect]).T)
def __init__(self,
neuronas=1,
act=1,
reg=regularizador.L2(0.0),
name="No Name",
bias=False,
isCon=False):
self.neuronas = neuronas
self.act = act
self.reg = reg
self.name = name
self.bias = bias
self.isCon = isCon
def ejer6_211(x_train, y_train):
reg1 = regularizador.L1(0.0)
reg2 = regularizador.L2(0.0)
red_densa = model.Red()
input_size=x_train.shape[1]
Layer1= layer.Dense(neuronas =input_size,
act =activation.Tanh(),
reg = reg2,
name ="Layer 1" ,
bias = True )
Layer2= layer.Dense(neuronas =1,
act =activation.Tanh(),
reg =reg1,
name ="Layer 2",
bias = True)
red_densa.add(Layer1)
layer_aux= layer.ConcatInput(input_size, Layer2)
red_densa.add(layer_aux)
red_densa.fit(
x_train=x_train, y_train=y_train,
batch_size=1,
epochs=300,
opt=optimizer.SGD(lr=0.1),
loss_function=loss.MSE(),
acc_function=metric.accuracy_xor)
plt.figure(1)
plt.ylabel("Precisión [%]")
plt.plot(red_densa.acc_vect, label="211", alpha=0.6)
plt.legend(loc=0)
#plt.savefig("ejer6_acc_211.pdf")
plt.figure(2)
plt.ylabel("Pérdida Normalizada")
plt.plot(red_densa.loss_vect/np.max(red_densa.loss_vect), label="211", alpha=0.6)
plt.legend(loc=0)
#plt.savefig("ejer6_loss_211.pdf")
#plt.show()
np.savetxt("ejer6_211.txt", np.array([
red_densa.acc_vect,
red_densa.loss_vect]).T)
def ejer7_NN1(x_train, y_train, x_test, y_test, N, NN, ejemplos, i):
reg1 = regularizador.L1(0.0)
reg2 = regularizador.L2(0.0)
red_densa = model.Red()
input_size = x_train.shape[1]
Layer1 = layer.Dense(neuronas=input_size,
act=activation.Tanh(),
reg=reg2,
name="Layer 1",
bias=True)
Layer2 = layer.Dense(neuronas=NN,
act=activation.Tanh(),
reg=reg1,
name="Layer 2",
bias=True)
red_densa.add(Layer1)
red_densa.add(Layer2)
red_densa.fit(x_train=x_train,
y_train=y_train,
x_test=x_test,
y_test=y_test,
batch_size=ejemplos,
epochs=500,
opt=optimizer.SGD(lr=0.1),
loss_function=loss.MSE(),
acc_function=metric.accuracy_xor)
plt.figure(1)
plt.ylabel("Accuracy [%]")
plt.plot(red_densa.acc_vect,
c=cmap(i),
label="({},{},{})".format(N, NN, ejemplos))
plt.legend(loc=0)
plt.figure(2)
plt.ylabel("Pérdida Normalizada")
plt.plot(red_densa.loss_vect / np.max(red_densa.loss_vect),
c=cmap(i),
label="({},{},{})".format(N, NN, ejemplos))
plt.legend(loc=0)