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- import numpy as np
- import uctc.nn as nn
- np.random.seed(42)
- def parameter_data(*shape):
- assert len(shape) == 2, (
- "Shape must have 2 dimensions, instead has {}".format(len(shape)))
- assert all(isinstance(dim, int) and dim > 0 for dim in shape), (
- "Shape must consist of positive integers, got {!r}".format(shape))
- limit = np.sqrt(3.0 / np.mean(shape))
- data = np.random.uniform(low=-limit, high=limit, size=shape).astype(np.float32)
- return data
-
- class Dataset(object):
- def __init__(self, x, y):
- assert isinstance(x, np.ndarray)
- assert isinstance(y, np.ndarray)
- assert np.issubdtype(x.dtype, np.floating)
- assert np.issubdtype(y.dtype, np.floating)
- assert x.ndim == 2
- assert y.ndim == 2
- assert x.shape[0] == y.shape[0]
- self.x = x
- self.y = y
-
- def iterate_once(self, batch_size):
- assert isinstance(batch_size, int) and batch_size > 0, (
- f"Batch size should be a positive integer, got {batch_size}")
- assert self.x.shape[0] % batch_size == 0, (
- f"Dataset size {self.x.shape[0]} is not divisible by batch size {batch_size}")
- index = 0
- while index < self.x.shape[0]:
- x = self.x[index:index + batch_size]
- y = self.y[index:index + batch_size]
- yield nn.Constant(x), nn.Constant(y)
- index += batch_size
-
- def iterate_forever(self, batch_size):
- while True:
- yield from self.iterate_once(batch_size)
-
- def get_validation_accuracy(self):
- raise NotImplementedError(
- "No validation data is available for this dataset. "
- "In this assignment, only the Digit Classification and Language "
- "Identification datasets have validation data.")
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