def __init__(self, d_size, iterations): np.set_printoptions(threshold=np.nan) # Set up file handler for file logging self.logger = dev_logger.logger(__name__ + "KSPhere") self.d_size = d_size self.iterations = iterations return None
def __init__(self, size, patch_size, resolution):
np.set_printoptions(threshold=np.nan)
# Set up file handler for file logging
self.logger = dev_logger.logger(__name__ + ".Patch")
if 0 > resolution > (size / 2 - patch_size / 2):
raise Error("Resolution must be a whole number greater than 0 but less (img_size / 2 - patch_size / 2).")
self.size = size
self.resolution = resolution
self.patch_size = patch_size
self.idx = [x for x in range(int((self.size / 2) - (self.patch_size / 2))) if x % self.resolution == 0]
self.stride = len(self.idx)
def __init__(self, input_shape, n_out, batch_size, preprocessor=None ): self.layers = [] self.params = [] self.batch_size = batch_size self.input_shape = input_shape self.n_out = n_out self.output = None self.finalized = False self.preprocessor = preprocessor self.logger = dev_logger.logger(__name__ + ".NeuralNetwork")
def __init__(self, size, patch_size, resolution):
np.set_printoptions(threshold=np.nan)
# Set up file handler for file logging
self.logger = dev_logger.logger(__name__ + ".Patch")
if 0 > resolution > (size / 2 - patch_size / 2):
raise Error(
"Resolution must be a whole number greater than 0 but less (img_size / 2 - patch_size / 2)."
)
self.size = size
self.resolution = resolution
self.patch_size = patch_size
self.idx = [
x for x in range(int((self.size / 2) - (self.patch_size / 2)))
if x % self.resolution == 0
]
self.stride = len(self.idx)
def __init__(self):
np.set_printoptions(threshold=np.nan)
# Set up file handler for file logging
self.logger = dev_logger.logger(__name__ + ".ZCA_Whitening")
def __init__(self): np.set_printoptions(threshold=np.nan) # Set up file handler for file logging self.logger = dev_logger.logger(__name__ + ".ZCA_Whitening")
