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import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import mnist

# MNIST dataset parameters
num_classes = 10 # total classes (0-9 digits)
num_features = 784 # data features (img shape: 28*28)

(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Convert to float32
x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)

# Flatten images to 1-D vector of 784 features (28*28)
x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])

# Normalize images value from [0, 255] to [0, 1]
x_train, x_test = x_train / 255., x_test / 255.
< /code>

In der 15. Zeile < /strong> dieser Code, < /p>

x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])

Verwenden von MNIST -Datensatz < /p>

[code]import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import mnist

# MNIST dataset parameters
num_classes = 10 # total classes (0-9 digits)
num_features = 784 # data features (img shape: 28*28)

(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Convert to float32
x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)

# Flatten images to 1-D vector of 784 features (28*28)
x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])

# Normalize images value from [0, 255] to [0, 1]
x_train, x_test = x_train / 255., x_test / 255.
< /code>

In der 15. Zeile < /strong> dieser Code, < /p>

x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])[/code]. Ich kann nicht verstehen, was diese Umgestaltung in unserem Datensatz wirklich tut. Bitte erklären Sie mich.