def main():
X_train, X_test, Y_train, Y_test = get_dataset()
from get_model import get_model, save_model
model = get_model()
model = train_model(model, X_train, X_test, Y_train, Y_test)
save_model(model)
return model
def main():
batch_size_for_capture = 300
x_train, y_train = get_dataset(batch_size_for_capture)
model = create_model()
model = train_model(model, x_train, y_train)
save_model(model)
return model
def main():
from get_dataset import get_dataset
X, X_test, Y, Y_test = get_dataset()
from get_model import get_model, save_model
model = get_model(len(Y[0]))
import numpy
model = train_model(model, X, X_test, Y, Y_test)
save_model(model)
return model
def train(data_name, model_name):
X, y = get_data.get_training_xy("./tracktrack/" + data_name + "/")
model = get_model.get_model(model_name)
try:
model.fit(X[0::3],
y[0::3],
verbose=1,
batch_size=64,
nb_epoch=300,
validation_data=(X[1::3], y[1::3]))
except KeyboardInterrupt:
pass
get_model.save_model(model, model_name)
def main():
X, X_test, Y, Y_test = get_dataset()
model = get_model()
model = train_model(model, X, X_test, Y, Y_test)
save_model(model)
return model
def main():
x, x_test, y, y_test = get_dataset()
model = get_model()
model = train_model(model, x, x_test, y, y_test)
save_model(model)
return model
plt.xlabel('Num of Epochs')
plt.ylabel('Accuracy')
plt.legend(['train', 'validation'], loc='best')
plt.subplot(1, 2, 2)
plt.plot(np.arange(1, len(history['loss']) + 1), history['loss'], 'r')
plt.plot(np.arange(1,
len(history['val_loss']) + 1), history['val_loss'], 'g')
plt.xticks(np.arange(0, epochs + 1, epochs / 10))
plt.title('Training Loss vs. Validation Loss')
plt.xlabel('Num of Epochs')
plt.ylabel('Loss')
plt.legend(['train', 'validation'], loc='best')
plt.show()
X_train, X_test, y_train, y_test = get_dataset()
model = get_model(num_classes=5)
model = train_model(model,
X_train,
X_test,
y_train,
y_test,
batch_size=32,
num_epochs=50)
save_model(model)