def fetch_digits(data_target=True):
file_path = maybe_download('../../ztlearn/datasets/digits/', URL)
with gzip.open(file_path, 'rb') as digits_path:
digits_data = np.loadtxt(digits_path, delimiter=',')
data, target = digits_data[:, :-1], digits_data[:, -1].astype(np.int)
if data_target:
return DataSet(data, target)
else:
return train_test_split(data, target, test_size=0.33, random_seed=5)
def fetch_pima_indians(data_target=True):
file_path = maybe_download('../../ztlearn/datasets/pima/', URL)
describe = [
'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',
'DiabetesPedigreeFunction', 'Age', 'Insulin', 'BMI', 'Outcome (0 or 1)'
]
dataframe = pd.read_csv(file_path, names=describe)
data, target = dataframe.values[:, 0:8], dataframe.values[:, 8]
if data_target:
return DataSet(data, target, describe)
else:
return train_test_split(data, target, test_size=0.2, random_seed=2)
def fetch_boston(data_target=True):
file_path = maybe_download('../../ztlearn/datasets/boston/', URL)
describe = [
'CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',
'PTRATIO', 'B', 'LSTAT', 'MEDV'
]
dataframe = pd.read_csv(file_path, delim_whitespace=True, names=describe)
data, target = dataframe.values[:, 0:13], dataframe.values[:, 13]
if data_target:
return DataSet(data, target, describe)
else:
return train_test_split(data, target, test_size=0.2, random_seed=2)
def fetch_iris(data_target = True):
file_path = maybe_download('../../ztlearn/datasets/iris/', URL)
describe = [
'sepal-length (cm)',
'sepal-width (cm)',
'petal-length (cm)',
'petal-width (cm)',
'petal_type'
]
dataframe = pd.read_csv(file_path, names = describe)
# convert petal type column to categorical data i.e {0:'Iris-setosa', 1:'Iris-versicolor', 2:'Iris-virginica'}
dataframe.petal_type = pd.Categorical(dataframe.petal_type)
dataframe['petal_type'] = dataframe.petal_type.cat.codes
data, target = dataframe.values[:,0:4], dataframe.values[:,4].astype('int')
if data_target:
return DataSet(data, target, describe)
else:
return train_test_split(data, target, test_size = 0.2, random_seed = 2)
def fetch_steel_plates_faults(data_target=True, custom_path=os.getcwd()):
file_path = maybe_download(custom_path + '/../../ztlearn/datasets/steel/',
URL)
file_path_2 = maybe_download(
custom_path + '/../../ztlearn/datasets/steel/', URL_2)
describe = [
'Pastry', 'Z_Scratch', 'K_Scatch', 'Stains', 'Dirtiness', 'Bumps',
'Other_Faults'
]
InputDataHeader = pd.read_csv(file_path_2, header=None)
InputData = pd.read_csv(file_path, header=None, sep="\t")
InputData.set_axis(InputDataHeader.values.flatten(), axis=1, inplace=True)
dataframe = InputData.copy()
dataframe.drop(describe, axis=1, inplace=True)
targetframe = InputData[describe].copy()
data, target = dataframe.values, targetframe.values
if data_target:
return DataSet(data, target, describe)
else:
return train_test_split(data, target, test_size=0.2, random_seed=2)
from ztlearn.dl.layers import Embedding
from ztlearn.dl.models import Sequential
from ztlearn.utils import train_test_split
from ztlearn.dl.optimizers import register_opt
opt = register_opt(optimizer_name = 'sgd_momentum', momentum = 0.01, learning_rate = 0.001)
model = Sequential(init_method = 'he_normal')
model.add(Embedding(10, 2, activation = 'selu', input_shape = (1, 10)))
model.compile(loss = 'categorical_crossentropy', optimizer = opt)
train_data = np.random.randint(10, size=(5, 1, 10))
train_label = np.random.randint(14, size=(5, 1, 10))
train_data, test_data, train_label, test_label = train_test_split(train_data,
train_label,
test_size = 0.1)
fit_stats = model.fit(train_data, train_label, batch_size = 4, epochs = 50)
"""
works
data = np.arange(0,100,1).reshape(10,1,10)
labels = np.arange(1,101,1).reshape(10,1,10)
model.add(Embedding(100, 5, activation = 'selu', input_shape = (1, 10)))
model.add(RNN(10, activation="tanh", bptt_truncate = 3, input_shape = (10, 10)))
"""
