def main(epochs=20):
Nin = 784
Nh_l = [100, 50]
number_of_class = 10
Nout = number_of_class
data = Data_func()
model = DNN(Nin, Nh_l, Nout)
batch_size = 100
(X_train, Y_train), (X_test, Y_test) = data
N_tr = X_train.shape[0]
loss_l = {"loss": [], "val_loss": []}
acc_l = {"accuracy": [], "val_accuracy": []}
for epoch in range(epochs):
# Train
train(model, X_train, Y_train, N_tr, batch_size)
# Validation
validation(model, X_test, Y_test)
print(f'Epoch {epoch}, '
f'Loss: {tr_loss.result():.3}, '
f'Acc: {tr_acc.result() * 100:.3}, '
f'Test Loss: {te_loss.result():.3}, '
f'Test Accuracy: {te_acc.result() * 100:.3}')
loss_l["loss"].append(tr_loss.result())
acc_l["accuracy"].append(tr_acc.result())
loss_l["val_loss"].append(te_loss.result())
acc_l["val_accuracy"].append(te_acc.result())
plot_loss(loss_l)
plot_acc(acc_l)
plt.show()
def main():
model = DNN(Nin, Nh_l, Nout)
(X_train, Y_train), (X_test, Y_test) = Data_func()
history = model.fit(X_train,
Y_train,
epochs=5,
batch_size=100,
validation_split=0.2)
performace_test = model.evaluate(X_test, Y_test, batch_size=100)
print('Test Loss and Accuracy ->', performace_test)
plot_loss(history)
plt.show()
plot_acc(history)
plt.show()
def main():
Nh_l = [100, 50]
Pd_l = [0.0, 0.0]
number_of_class = 10
Nout = number_of_class
(X_train, Y_train), (X_test, Y_test) = Data_func()
model = DNN(X_train.shape[1], Nh_l, Pd_l, Nout)
history = model.fit(X_train, Y_train, epochs=10, batch_size=100, validation_split=0.2)
performace_test = model.evaluate(X_test, Y_test, batch_size=100)
print('Test Loss and Accuracy ->', performace_test)
plot_loss(history, '(a) 손실 추이')
plt.show()
plot_acc(history, '(b) 정확도 추이')
plt.show()
def main(epochs=20, batch_size=128):
data = DATA()
autoencoder = AE(data.input_shape)
history = autoencoder.fit(data.x_train, data.x_train,
epochs=epochs,
batch_size=batch_size,
shuffle=True,
validation_split=0.2)
plot_acc(history, '(a) Accuracy')
plt.show()
plot_loss(history, '(b) Cost')
plt.show()
show_ae(autoencoder, data)
plt.show()
def main():
Nin = 784
Nh_l = [100, 50]
number_of_class = 10
Nout = number_of_class
(X_train, Y_train), (X_test, Y_test) = Data_func()
model = DNN(Nin, Nh_l, Nout)
history = model.fit(X_train, y_train, epochs=10, batch_size=100, validation_split=0.2)
performace_test = model.evaluate(X_test, y_test, batch_size=100)
print('Test Loss and Accuracy ->', performace_test)
plot_acc(history)
plt.show()
plot_loss(history)
plt.show()
def main():
x_nodes = 784
z_dim = 36
autoencoder = AE(x_nodes, z_dim)
history = autoencoder.fit(X_train, X_train,
epochs=10,
batch_size=256,
shuffle=True,
validation_data=(X_test, X_test))
plot_acc(history, '(a) Accuracy Change')
plt.show()
plot_loss(history, '(b) Loss Change')
plt.show()
show_ae(autoencoder)
plt.show()
def main():
x_nodes = 784
z_dim = 36
autoencoder = AE(x_nodes, z_dim)
history = autoencoder.fit(X_train,
X_train,
epochs=500,
batch_size=256,
shuffle=True,
validation_data=(X_test, X_test))
plot_acc(history, '(a) 학습 경과에 따른 정확도 변화 추이')
plt.show()
plot_loss(history, '(b) 학습 경과에 따른 손실값 변화 추이')
plt.show()
show_ae(autoencoder)
plt.show()
def main():
Nin = 784
Nh = 100
number_of_class = 10
Nout = number_of_class
# model = ANN_models_func(Nin, Nh, Nout)
# model = ANN_seq_func(Nin, Nh, Nout)
# model = ANN_models_class(Nin, Nh, Nout)
model = ANN_seq_class(Nin, Nh, Nout)
(X_train, Y_train), (X_test, Y_test) = Data_func()
history = model.fit(X_train, Y_train, epochs=5, batch_size=100, validation_split=0.2)
performace_test = model.evaluate(X_test, Y_test, batch_size=100)
print('Test Loss and Accuracy ->', performace_test)
plot_loss(history)
plt.show()
plot_acc(history)
plt.show()
def run(self, epochs=100, batch_size=128, verbose=1):
data = self.data
model = self.model
fig = self.fig
history = self.fit(epochs=epochs,
batch_size=batch_size,
verbose=verbose)
score = model.evaluate(data.X_test, data.Y_test, verbose=0)
print('Confusion matrix')
Y_test_pred = model.predict(data.X_test, verbose=0)
y_test_pred = np.argmax(Y_test_pred, axis=1)
print(metrics.confusion_matrix(data.y_test, y_test_pred))
print('Test score:', score[0])
print('Test accuracy:', score[1])
# Save results
suffix = sfile.unique_filename('datatime')
foldname = 'output_' + suffix
os.makedirs(foldname)
skeras.save_history_history('history_history.npy',
history.history,
fold=foldname)
model.save_weights(os.path.join(foldname, 'dl_model.h5'))
print('Output results are saved in', foldname)
if fig:
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
skeras.plot_acc(history)
plt.subplot(1, 2, 2)
skeras.plot_loss(history)
plt.show()
self.history = history
return foldname
def main():
batch_size = 128
epochs = 10
data = DATA()
model = CNN(data.input_shape, data.num_classes)
history = model.fit(data.x_train,
data.y_train,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2)
score = model.evaluate(data.x_test, data.y_test)
print()
print('Test loss:', score[0])
print('Test accuracy:', score[1])
plot_loss(history)
plt.show()
plot_acc(history)
plt.show()