def fit(self, epochs, batch_size, params):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True #Do not assign whole gpu memory, just use it on the go
config.allow_soft_placement = True #If an operation is not defined in the default device, let it execute in another.
timestr = time.strftime("%Y%m%d-%H%M%S")
random_seed = 123
np.random.seed(random_seed)
tf.set_random_seed(random_seed)
# Data Path
CallFolder = '../../Raw_Data/'
StoreFolder = 'Final_Results/'
if not os.path.isdir(StoreFolder):
os.makedirs(StoreFolder)
StoreFolder_selfeval = 'Selfeval_Results/'
if not os.path.isdir(StoreFolder_selfeval):
os.makedirs(StoreFolder_selfeval)
StoreFolder_all_labeled = 'All_labeled_data/'
if not os.path.isdir(StoreFolder_all_labeled):
os.makedirs(StoreFolder_all_labeled)
StoreFolder_Model = 'Models/'
if os.path.exists(StoreFolder_Model) and os.path.isdir(
StoreFolder_Model):
shutil.rmtree(StoreFolder_Model)
if not os.path.isdir(StoreFolder_selfeval):
os.makedirs(StoreFolder_selfeval)
#########################################################
# Decide whether self-evaluation or final submission
Val_split = 9.5 / 10
# You want to preprocess the data?
preprocessing = True
# Hyperparameters
# epochs = 400
# batch_size = 128
learning_rate = 0.0002
# params = 800
activation = tf.nn.tanh
# At which sample starts the prediction for the test data?
sample_number = 30000
#########################################################
# LOAD AND SHUFFLE DATA!
DataTrain = np.array(
pd.read_hdf(CallFolder + "train_labeled.h5", "train"))
X_train_labeled = DataTrain[:, 1:]
features = X_train_labeled.shape[1]
y_train_labeled = DataTrain[:, 0]
classes = np.max(y_train_labeled) + 1
X_test = np.array(
pd.read_hdf(CallFolder + "train_unlabeled.h5",
"train")) # X_test = unlabeled data
print('Unpreprocessed Data')
print('X_train_labeled: ', X_train_labeled.shape, end=' || ')
print('y_train: ', y_train_labeled.shape)
print('X_test: ', X_test.shape, '\n')
(X_train_labeled, y_train_labeled) = shuffle(X_train_labeled,
y_train_labeled)
X_train = np.concatenate((X_train_labeled, X_test), axis=0)
np.save(os.path.join(StoreFolder_all_labeled, 'X_train.npy'),
X_train) # STORE BEFORE PREPROCESSING, BUT AFTER SHUFFLING!
#########################################################
if preprocessing == True:
X_train_labeled, X_test = centering(X_train_labeled, X_test)
X_train_labeled = normalize(X_train_labeled)
X_test = normalize(X_test)
samples = len(X_train_labeled)
X_valid = X_train_labeled[int(Val_split * samples):samples, :]
y_valid = y_train_labeled[int(Val_split * samples):samples]
X_train = X_train_labeled[0:int(Val_split * samples), :]
y_train = y_train_labeled[0:int(Val_split * samples)]
print('Final Data')
print('Shape of X_train:', X_train.shape)
print('Shape of y_train:', y_train.shape)
print('Shape of X_valid:', X_valid.shape)
print('Shape of y_valid:', y_valid.shape, '\n')
##################
# CREATE GRAPH
g = tf.Graph()
with g.as_default():
# build the graph
NN.build_NN(features, classes, learning_rate, params, activation)
##################
# TRAINING
print()
print('Training... ')
with tf.Session(graph=g, config=config) as sess:
[avg_loss_plot, valid_accuracy_plot,
test_accuracy_plot] = train(path=StoreFolder_Model,
sess=sess,
epochs=epochs,
random_seed=random_seed,
batch_size=batch_size,
training_set=(X_train, y_train),
validation_set=(X_valid, y_valid),
test_set=None)
del g
##################
# CREATE GRAPH
g2 = tf.Graph()
with g2.as_default():
# build the graph
NN.build_NN(features, classes, learning_rate, params, activation)
# Saver
saver = tf.train.Saver()
##################
# PREDICTION
with tf.Session(graph=g2, config=config) as sess:
epoch = np.argmax(valid_accuracy_plot) + 1
load(saver=saver, sess=sess, epoch=epoch, path=StoreFolder_Model)
y_test_pred = predict(sess, X_test)
##################
# CREATE NEW DATASET
y_train = np.concatenate((y_train_labeled, y_test_pred), axis=0)
np.save(os.path.join(StoreFolder_all_labeled, 'y_train.npy'), y_train)
X_train= DataTrain[:, 1:]
y_train = DataTrain[:, 0]
X_test = np.array(pd.read_hdf(CallFolder + "train_unlabeled.h5", "train")) # X_test = unlabeled data
(X_train, y_train) = shuffle(X_train, y_train)
y_train_onehot = keras.utils.to_categorical(y_train)
print('First 3 labels: ', y_train[:3])
print('First 3 onehot labels:\n', y_train_onehot[:3])
X_train_all = np.concatenate((X_train, X_test), axis=0)
np.save(os.path.join(StoreFolder_all_labeled, 'X_train.npy'), X_train_all) # STORE BEFORE PREPROCESSING, BUT AFTER SHUFFLING!
#########################################################
# TRAIN DATA
X_train, X_test = centering(X_train, X_test)
# build model:
model = KERAS.build(X_train, y_train_onehot, param, layers)
# train model:
trained_model, losses = KERAS.fit(model, X_train, y_train_onehot, epochs, batch_size)
# predict labels:
y_test_pred = KERAS.predict(trained_model, X_test)
timestr = time.strftime("%Y%m%d-%H%M%S")
PrintOutput(y_test_pred, sample_number, os.path.join(StoreFolder_selfeval, timestr + '_' + str(epochs) + '_' + str(param) + '_' + str(layers) + '_' + str(batch_size) + '_y_test.csv'))
y_train = np.concatenate((y_train, y_test_pred), axis=0)
np.save(os.path.join(StoreFolder_all_labeled, 'y_train.npy'), y_train)
plt.figure(1)
def fit(self, epochs, batch_size, params):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True #Do not assign whole gpu memory, just use it on the go
config.allow_soft_placement = True #If an operation is not defined in the default device, let it execute in another.
timestr = time.strftime("%Y%m%d-%H%M%S")
random_seed = 123
np.random.seed(random_seed)
tf.set_random_seed(random_seed)
# Data Path
CallFolder = '../../Raw_Data/'
StoreFolder = 'Final_Results/'
if not os.path.isdir(StoreFolder):
os.makedirs(StoreFolder)
StoreFolder_selfeval = 'Selfeval_Results/'
if not os.path.isdir(StoreFolder_selfeval):
os.makedirs(StoreFolder_selfeval)
StoreFolder_Model = 'Models/'
if os.path.exists(StoreFolder_Model) and os.path.isdir(
StoreFolder_Model):
shutil.rmtree(StoreFolder_Model)
if not os.path.isdir(StoreFolder_selfeval):
os.makedirs(StoreFolder_selfeval)
#########################################################
# Decide whether self-evaluation or final submission
final_submission = False
Test_split = 9.5 / 10
Val_split = 9.5 / 10
# You want to preprocess the data?
preprocessing = True
# Hyperparameters
# epochs = 120
# batch_size = 128
learning_rate = 0.0002
# params = 200
activation = tf.nn.relu
# At which sample starts the prediction for the test data?
sample_number = 30000
#########################################################
# LOAD AND SHUFFLE DATA!
DataTrain = np.array(
pd.read_hdf(CallFolder + "train_labeled.h5", "train"))
X_train = DataTrain[:, 1:]
features = X_train.shape[1]
y_train = DataTrain[:, 0]
classes = np.max(y_train) + 1
X_test = np.array(pd.read_hdf(CallFolder + "test.h5", "test"))
print('Unpreprocessed Data')
print('X_train_labeled: ', X_train.shape, end=' || ')
print('y_train: ', y_train.shape)
print('X_test: ', X_test.shape, '\n')
(X_train, y_train) = shuffle(X_train, y_train)
#########################################################
# FINAL DATA
if final_submission == True:
if preprocessing == True:
X_train, X_test = centering(X_train, X_test)
X_train = normalize(X_train)
X_test = normalize(X_test)
samples = len(X_train)
X_valid = X_train[int(Val_split * samples):samples, :]
y_valid = y_train[int(Val_split * samples):samples]
X_train = X_train[0:int(Val_split * samples), :]
y_train = y_train[0:int(Val_split * samples)]
print('Final Data')
print('Shape of X_train:', X_train.shape)
print('Shape of y_train:', y_train.shape)
print('Shape of X_valid:', X_valid.shape)
print('Shape of y_valid:', y_valid.shape, '\n')
##################
# CREATE GRAPH
g = tf.Graph()
with g.as_default():
# build the graph
NN.build_NN(features, classes, learning_rate, params,
activation)
##################
# TRAINING
print()
print('Training... ')
with tf.Session(graph=g, config=config) as sess:
[avg_loss_plot, valid_accuracy_plot,
test_accuracy_plot] = train(path=StoreFolder_Model,
sess=sess,
epochs=epochs,
random_seed=random_seed,
batch_size=batch_size,
training_set=(X_train, y_train),
validation_set=(X_valid, y_valid),
test_set=None)
np.save(
os.path.join(StoreFolder, timestr + '_avg_loss_plot.npy'),
avg_loss_plot)
del g
##################
# CREATE GRAPH
g2 = tf.Graph()
with g2.as_default():
# build the graph
NN.build_NN(features, classes, learning_rate, params,
activation)
# Saver
saver = tf.train.Saver()
##################
# PREDICTION
with tf.Session(graph=g2, config=config) as sess:
epoch = np.argmax(valid_accuracy_plot) + 1
load(saver=saver,
sess=sess,
epoch=epoch,
path=StoreFolder_Model)
y_test_pred = predict(sess, X_test)
PrintOutput(
y_test_pred, sample_number,
os.path.join(
StoreFolder, timestr + '_' + str(epochs) + '_' +
str(batch_size) + '_' + str(params) + '_y_test.csv'))
#################################################################################################
#################################################################################################
# SELFEVALUATION
else:
samples = len(X_train)
X_train_selfeval = X_train[0:int(Test_split * samples), :]
y_train_selfeval = y_train[0:int(Test_split * samples)]
X_test_selfeval = X_train[int(Test_split * samples):samples, :]
y_test_selfeval = y_train[int(Test_split * samples):samples]
print('Self-evaluation data')
print('Shape of X_train:', X_train_selfeval.shape)
print('Shape of y_train:', y_train_selfeval.shape)
print('Shape of X_test:', X_test_selfeval.shape)
print('Shape of y_test:', y_test_selfeval.shape)
if preprocessing == True:
X_train_selfeval, X_test_selfeval = centering(
X_train_selfeval, X_test_selfeval)
X_train_selfeval = normalize(X_train_selfeval)
X_test_selfeval = normalize(X_test_selfeval)
##################
# CREATE GRAPH TRAINING
g = tf.Graph()
with g.as_default():
# build the graph
NN.build_NN(features, classes, learning_rate, params,
activation)
##################
# TRAINING
print()
print('Training... ')
with tf.Session(graph=g, config=config) as sess:
[avg_loss_plot, valid_accuracy_plot, test_accuracy_plot
] = train(path=StoreFolder_Model,
sess=sess,
epochs=epochs,
random_seed=random_seed,
batch_size=batch_size,
training_set=(X_train_selfeval, y_train_selfeval),
validation_set=None,
test_set=(X_test_selfeval, y_test_selfeval))
np.save(
os.path.join(StoreFolder_selfeval,
timestr + '_avg_loss_plot.npy'),
avg_loss_plot)
np.save(
os.path.join(StoreFolder_selfeval,
timestr + '_test_accuracy_plot.npy'),
test_accuracy_plot)
##################
# POSTPROCESS
# plt.figure(1)
# plt.plot(range(1, len(avg_loss_plot) + 1), avg_loss_plot)
# plt.title('Training loss')
# plt.xlabel('Epoch')
# plt.ylabel('Average Training Loss')
# if final_submission == True:
# plt.savefig(os.path.join(StoreFolder, timestr + '_' + str(epochs) + '_' + str(batch_size) + '_' + str(params) + '_TrainLoss.jpg'))
# else:
# plt.savefig(os.path.join(StoreFolder_selfeval, timestr + '_' + str(epochs) + '_' + str(batch_size) + '_' + str(params) + '_TrainLoss.jpg'))
# if final_submission == False:
# plt.figure(2)
# plt.plot(range(1, len(test_accuracy_plot) + 1), test_accuracy_plot, label='Test Accuracy')
# plt.title('Test Accuracy')
# plt.xlabel('Epoch')
# plt.ylabel('Accuracy')
# plt.legend()
# plt.savefig(os.path.join(StoreFolder_selfeval, timestr + '_' + str(epochs) + '_' + str(batch_size) + '_' + str(params) + '_TestAccuracy.jpg'))
print('\nJob Done!')
return np.average(test_accuracy_plot[-10:])
y_train = DataTrain[:, 0]
classes = np.max(y_train) + 1
X_test = np.array(pd.read_hdf(CallFolder + "test.h5", "test"))
print('Unpreprocessed Data')
print('X_train_labeled: ', X_train.shape, end=' || ')
print('y_train: ', y_train.shape)
print('X_test: ', X_test.shape, '\n')
(X_train, y_train) = shuffle(X_train, y_train)
#########################################################
# FINAL DATA
if final_submission == True:
if preprocessing == True:
X_train, X_test = centering(X_train, X_test)
X_train = normalize(X_train)
X_test = normalize(X_test)
samples = len(X_train)
X_valid = X_train[int(Val_split * samples):samples, :]
y_valid = y_train[int(Val_split * samples):samples]
X_train = X_train[0:int(Val_split * samples), :]
y_train = y_train[0:int(Val_split * samples)]
print('Final Data')
print('Shape of X_train:', X_train.shape)
print('Shape of y_train:', y_train.shape)
print('Shape of X_valid:', X_valid.shape)
print('Shape of y_valid:', y_valid.shape, '\n')
##################
classes = np.max(y_train_labeled) + 1
print('Unpreprocessed Data')
print('X_train_labeled: ', X_train_labeled.shape, end=' || ')
print('y_train: ', y_train_labeled.shape)
X_test = np.array(pd.read_hdf(CallFolder + "train_unlabeled.h5", "train")) # X_test = unlabeled data
print('X_test: ', X_test.shape, '\n')
(X_train_labeled, y_train_labeled) = shuffle(X_train_labeled, y_train_labeled)
X_train = np.concatenate((X_train_labeled, X_test), axis=0)
np.save(os.path.join(StoreFolder_all_labeled, 'X_train.npy'), X_train) # STORE BEFORE PREPROCESSING, BUT AFTER SHUFFLING!
#########################################################
if preprocessing == True:
X_train_labeled, X_test = centering(X_train_labeled, X_test)
X_train_labeled = normalize(X_train_labeled)
X_test = normalize(X_test)
samples = len(X_train_labeled)
X_valid = X_train_labeled[int(Val_split * samples):samples, :]
y_valid = y_train_labeled[int(Val_split * samples):samples]
X_train = X_train_labeled[0:int(Val_split * samples), :]
y_train = y_train_labeled[0:int(Val_split * samples)]
print('Final Data')
print('Shape of X_train:', X_train.shape)
print('Shape of y_train:', y_train.shape)
print('Shape of X_valid:', X_valid.shape)
print('Shape of y_valid:', y_valid.shape, '\n')
print('Shape of X_train - labeled:', X_train.shape)