因此，我一直遵循Google的官方tensorflow指南，并尝试使用Keras构建一个简单的神经网络。但是，在训练模型时，它不使用整个数据集（具有60000个条目），而是仅使用1875个条目进行训练。有可能解决吗？

import tensorflow as tf
from tensorflow import keras
import numpy as np

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

train_images = train_images / 255.0
test_images = test_images / 255.0

class_names = ['T-shirt', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot']

model = keras.Sequential([
                          keras.layers.Flatten(input_shape=(28, 28)),
                          keras.layers.Dense(128, activation='relu'), 
                          keras.layers.Dense(10)
])

model.compile(optimizer='adam',
              loss= tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=10)

输出：

Epoch 1/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.3183 - accuracy: 0.8866
Epoch 2/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.3169 - accuracy: 0.8873
Epoch 3/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.3144 - accuracy: 0.8885
Epoch 4/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.3130 - accuracy: 0.8885
Epoch 5/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.3110 - accuracy: 0.8883
Epoch 6/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.3090 - accuracy: 0.8888
Epoch 7/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.3073 - accuracy: 0.8895
Epoch 8/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.3057 - accuracy: 0.8900
Epoch 9/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.3040 - accuracy: 0.8905
Epoch 10/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.3025 - accuracy: 0.8915

<tensorflow.python.keras.callbacks.History at 0x7fbe0e5aebe0>

这是我一直在为此工作的原始Google
colab笔记本：https
://colab.research.google.com/drive/1NdtzXHEpiNnelcMaJeEm6zmp34JMcN38