def run(self, epochs=400):
d = self.data
X_train, X_test, y_train, y_test = d.X_train, d.X_test, d.y_train, d.y_test
X, y = d.X, d.y
m = self.model
h = m.fit(X_train,
y_train,
epochs=epochs,
validation_data=[X_test, y_test],
verbose=0)
skeras.plot_loss(h)
plt.title('History of training')
plt.show()
yp = m.predict(X_test)
print('Loss:', m.evaluate(X_test, y_test))
plt.plot(yp, label='Origial')
plt.plot(y_test, label='Prediction')
plt.legend(loc=0)
plt.title('Validation Results')
plt.show()
yp = m.predict(X)
plt.plot(yp, label='Origial')
plt.plot(y, label='Prediction')
plt.legend(loc=0)
plt.title('All Results')
plt.show()
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(in_ch=1, epochs=10, batch_size=512, fig=True):
data = DATA(in_ch=in_ch)
print(data.input_shape, data.x_train_in.shape)
unet = UNET(data.input_shape, data.n_ch)
history = unet.fit(data.x_train_in, data.x_train_out,
epochs=epochs,
batch_size=batch_size,
shuffle=True,
validation_split=0.2)
if fig:
plot_loss(history)
show_images(data, unet)
def main():
Nin = 13
Nh = 5
Nout = 1
model = ANN(Nin, Nh, Nout)
(X_train, y_train), (X_test, y_test) = Data_func()
history = model.fit(X_train, y_train, epochs=100, batch_size=100, validation_split=0.2, verbose=2)
performace_test = model.evaluate(X_test, y_test, batch_size=100)
print('\nTest Loss -> {:.2f}'.format(performace_test))
plot_loss(history)
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 run(self, epochs=400):
d = self.data
X_train, X_test, y_train, y_test = d.X_train, d.X_test, d.y_train, d.y_test
X, y = d.X, d.y
m = self.model
h = m.fit(X_train,
y_train,
epochs=epochs,
validation_data=[X_test, y_test],
verbose=0)
skeras.plot_loss(h)
plt.title('History of training')
plt.show()
yp = m.predict(X_test)
print('Loss:', m.evaluate(X_test, y_test))
plt.plot(yp, label='Origial')
plt.plot(y_test, label='Prediction')
plt.legend(loc=0)
plt.title('Validation Results')
plt.show()
yp = m.predict(X_test).reshape(-1)
print('Loss:', m.evaluate(X_test, y_test))
print(yp.shape, y_test.shape)
df = pd.DataFrame()
df['Sample'] = list(range(len(y_test))) * 2
df['Normalized #Passengers'] = np.concatenate([y_test, yp], axis=0)
df['Type'] = ['Original'] * len(y_test) + ['Prediction'] * len(yp)
plt.figure(figsize=(7, 5))
sns.barplot(x="Sample",
y="Normalized #Passengers",
hue="Type",
data=df)
plt.ylabel('Normalized #Passengers')
plt.show()
yp = m.predict(X)
plt.plot(yp, label='Origial')
plt.plot(y, label='Prediction')
plt.legend(loc=0)
plt.title('All Results')
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()
#print('y_train_shape:{}'.format(y_train.shape))
#print('X_test_shape:{}'.format(X_test.shape))
#print('y_test_shape:{}'.format(y_test.shape))
#print(X_train[0], y_train[0])
model = lstm_obj.build_model([5, window, 1])
h = model.fit(X_train,
y_train,
batch_size=512,
epochs=30,
validation_split=0.1,
verbose=1)
fig, ax = plt.subplots(nrows=1, ncols=1)
skeras.plot_loss(h)
plt.title('History of training')
#plt.show()
#skeras.save_history_history(symbol_name, h, fold='/home/deep-core2/바탕화면/Stock1')
#fig.savefig('/home/deep-core2/바탕화면/Stock1/{}_training_history.png'.format(symbol_name))
diff = []
ratio = []
predicted_test_norm = model.predict(
X_test
) # test set의 X(normalized)값에 대해서 예측한 y(normalized)값을 p라고 명함.
print(predicted_test_norm.shape)
# for each data index in test data
for u in range(len(y_test)): # y_test의 날짜 만큼 예측 값을 기록함.
# pr = prediction day u
pr = predicted_test_norm[u][0]
def load_data():
(X_train, y_train), (X_test, y_test) = datasets.boston_housing.load_data()
scaler = preprocessing.MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
return (X_train, y_train), (X_test, y_test)
if __name__ == "__main__":
Nin = 13
Nh = 5
Nout = 1
model = ANN(Nin, Nh, Nout)
(X_train, y_train), (X_test, y_test) = load_data()
history = model.fit(X_train,
y_train,
epochs=100,
batch_size=100,
validation_split=0.2,
verbose=2)
performace_test = model.evaluate(X_test, y_test, batch_size=100)
print("\nTest Loss -> {:.2f}".format(performace_test))
plt.figure(figsize=(5, 5), dpi=100)
plot_loss(history)
plt.show()
