Код: Выделить всё
import tensorflow as tf
from tensorflow import keras
from keras import layers
from keras.utils.np_utils import to_categorical
from keras import optimizers
from keras.datasets import cifar10
import matplotlib.pyplot as plt
import numpy as np
import os
def load_cifar10(num_training=10000, num_validation=1000, num_test=1000):
# Fetch the CIFAR-10 dataset from the web
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()
X_train = np.asarray(X_train, dtype=np.float32)
y_train = np.asarray(y_train, dtype=np.int32).flatten()
X_test = np.asarray(X_test, dtype=np.float32)
y_test = np.asarray(y_test, dtype=np.int32).flatten()
# Subsample the data
mask = range(num_training, num_training + num_validation)enter image description here
X_val = X_train[mask]
y_val = y_train[mask]
mask = range(num_training)
X_train = X_train[mask]
y_train = y_train[mask]
mask = range(num_test)
X_test = X_test[mask]
y_test = y_test[mask]
# Normaliza the data: subtract the mean pixel and divide by std
mean_pixel = X_train.mean(axis=(0, 1, 2), keepdims=True)
std_pixel = X_train.std(axis=(0, 1, 2), keepdims=True)
X_train = (X_train - mean_pixel) / std_pixel
X_val = (X_val - mean_pixel) / std_pixel
X_test = (X_test - mean_pixel) / std_pixel
# one-hot the labels
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_val = tf.keras.utils.to_categorical(y_val, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)
return X_train, y_train, X_val, y_val, X_test, y_test
X_train, y_train, X_val, y_val, X_test, y_test = load_cifar10()
Я и мои коллеги работаем на Pycharm 2023.3.4, Python 3.10, tensor и keras 2.9
Я также сталкиваюсь с этой проблемой с набором данных Fashion Minst
Почему такая разница и как это решить?
Подробнее здесь: https://stackoverflow.com/questions/790 ... from-keras
