本文整理匯總了Python中data.load_data方法的典型用法代碼示例。如果您正苦於以下問題：Python data.load_data方法的具體用法？Python data.load_data怎麽用？Python data.load_data使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類data的用法示例。
在下文中一共展示了data.load_data方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: main
# 需要導入模塊: import data [as 別名]
# 或者: from data import load_data [as 別名]
def main():
# format data
data = load_data("german")
# set the auditor
auditor = Auditor()
auditor.model = Weka_SVM
# call the auditor
auditor(data, output_dir="try", features_to_audit=["checking_status","duration"], dump_all=True) 示例2: main
# 需要導入模塊: import data [as 別名]
# 或者: from data import load_data [as 別名]
def main(argv=None):
train_data, validate_data, test_data, mask = load_data(data_path, BATCH_SIZE)
train(train_data, validate_data,test_data, mask) 示例3: run_lstm
# 需要導入模塊: import data [as 別名]
# 或者: from data import load_data [as 別名]
def run_lstm(model, sequence_length, prediction_steps):
data = None
global_start_time = time.time()
epochs = 1
ratio_of_data = 1 # ratio of data to use from 2+ million data points
path_to_dataset = 'data/household_power_consumption.txt'
if data is None:
print('Loading data... ')
x_train, y_train, x_test, y_test, result_mean = load_data(path_to_dataset, sequence_length,
prediction_steps, ratio_of_data)
else:
x_train, y_train, x_test, y_test = data
print('\nData Loaded. Compiling...\n')
if model is None:
model = build_model(prediction_steps)
try:
model.fit(x_train, y_train, batch_size=128, epochs=epochs, validation_split=0.05)
predicted = model.predict(x_test)
# predicted = np.reshape(predicted, (predicted.size,))
model.save('LSTM_power_consumption_model.h5') # save LSTM model
except KeyboardInterrupt: # save model if training interrupted by user
print('Duration of training (s) : ', time.time() - global_start_time)
model.save('LSTM_power_consumption_model.h5')
return model, y_test, 0
else: # previously trained mode is given
print('Loading model...')
predicted = model.predict(x_test)
plot_predictions(result_mean, prediction_steps, predicted, y_test, global_start_time)
return None 示例4: _get_buckets
# 需要導入模塊: import data [as 別名]
# 或者: from data import load_data [as 別名]
def _get_buckets():
""" Load the dataset into buckets based on their lengths.
train_buckets_scale is the inverval that'll help us
choose a random bucket later on.
"""
test_buckets = data.load_data('test_ids.enc', 'test_ids.dec')
data_buckets = data.load_data('train_ids.enc', 'train_ids.dec')
train_bucket_sizes = [len(data_buckets[b]) for b in range(len(config.BUCKETS))]
print("Number of samples in each bucket:\n", train_bucket_sizes)
train_total_size = sum(train_bucket_sizes)
# list of increasing numbers from 0 to 1 that we'll use to select a bucket.
train_buckets_scale = [sum(train_bucket_sizes[:i + 1]) / train_total_size
for i in range(len(train_bucket_sizes))]
print("Bucket scale:\n", train_buckets_scale)
return test_buckets, data_buckets, train_buckets_scale