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This commit is contained in:
Maxim Smolskiy
2024-03-28 21:03:23 +03:00
committed by GitHub
parent efb7463cde
commit f2246ce7fd
8 changed files with 121 additions and 128 deletions
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32
neural_network/input_data.py
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@ -22,7 +22,7 @@ import os
import typing
import urllib
import numpy
import numpy as np
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
@ -39,8 +39,8 @@ DEFAULT_SOURCE_URL = "https://storage.googleapis.com/cvdf-datasets/mnist/"
def _read32(bytestream):
dt = numpy.dtype(numpy.uint32).newbyteorder(">")
return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
dt = np.dtype(np.uint32).newbyteorder(">")
return np.frombuffer(bytestream.read(4), dtype=dt)[0]
@deprecated(None, "Please use tf.data to implement this functionality.")
@ -68,7 +68,7 @@ def _extract_images(f):
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(rows * cols * num_images)
data = numpy.frombuffer(buf, dtype=numpy.uint8)
data = np.frombuffer(buf, dtype=np.uint8)
data = data.reshape(num_images, rows, cols, 1)
return data
@ -77,8 +77,8 @@ def _extract_images(f):
def _dense_to_one_hot(labels_dense, num_classes):
"""Convert class labels from scalars to one-hot vectors."""
num_labels = labels_dense.shape[0]
index_offset = numpy.arange(num_labels) * num_classes
labels_one_hot = numpy.zeros((num_labels, num_classes))
index_offset = np.arange(num_labels) * num_classes
labels_one_hot = np.zeros((num_labels, num_classes))
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot
@ -107,7 +107,7 @@ def _extract_labels(f, one_hot=False, num_classes=10):
)
num_items = _read32(bytestream)
buf = bytestream.read(num_items)
labels = numpy.frombuffer(buf, dtype=numpy.uint8)
labels = np.frombuffer(buf, dtype=np.uint8)
if one_hot:
return _dense_to_one_hot(labels, num_classes)
return labels
@ -153,7 +153,7 @@ class _DataSet:
"""
seed1, seed2 = random_seed.get_seed(seed)
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seed1 if seed is None else seed2)
np.random.seed(seed1 if seed is None else seed2)
dtype = dtypes.as_dtype(dtype).base_dtype
if dtype not in (dtypes.uint8, dtypes.float32):
raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype)
@ -175,8 +175,8 @@ class _DataSet:
)
if dtype == dtypes.float32:
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
images = images.astype(np.float32)
images = np.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
@ -210,8 +210,8 @@ class _DataSet:
start = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
perm0 = numpy.arange(self._num_examples)
numpy.random.shuffle(perm0)
perm0 = np.arange(self._num_examples)
np.random.shuffle(perm0)
self._images = self.images[perm0]
self._labels = self.labels[perm0]
# Go to the next epoch
@ -224,8 +224,8 @@ class _DataSet:
labels_rest_part = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
perm = numpy.arange(self._num_examples)
numpy.random.shuffle(perm)
perm = np.arange(self._num_examples)
np.random.shuffle(perm)
self._images = self.images[perm]
self._labels = self.labels[perm]
# Start next epoch
@ -235,8 +235,8 @@ class _DataSet:
images_new_part = self._images[start:end]
labels_new_part = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part), axis=0),
numpy.concatenate((labels_rest_part, labels_new_part), axis=0),
np.concatenate((images_rest_part, images_new_part), axis=0),
np.concatenate((labels_rest_part, labels_new_part), axis=0),
)
else:
self._index_in_epoch += batch_size