Keras不训练整个数据集 [英] Keras not training on entire dataset

问题描述

因此，我一直在遵循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)，而仅使用1875个条目进行训练.有任何可能的解决方法吗?

So I've been following Google's official tensorflow guide and trying to build a simple neural network using Keras. But when it comes to training the model, it does not use the entire dataset (with 60000 entries) and instead uses only 1875 entries for training. Any possible fix?

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>

这是我一直在研究的原始Google colab笔记本: https://colab .research.google.com/drive/1NdtzXHEpiNnelcMaJeEm6zmp34JMcN38

Here's the original google colab notebook where I've been working on this: https://colab.research.google.com/drive/1NdtzXHEpiNnelcMaJeEm6zmp34JMcN38

推荐答案

在拟合模型期间显示的数字1875不是训练样本；这是批次的数量.

The number 1875 shown during fitting the model is not the training samples; it is the number of batches.

model.fit包含可选参数batch_size，根据

model.fit includes an optional argument batch_size, which, according to the documentation:

如果未指定，batch_size将默认为32.

因此，这里发生的是-您使用默认的批处理大小32(因为您没有指定其他任何东西)，因此数据的批处理总数为

So, what happens here is - you fit with the default batch size of 32 (since you have not specified anything different), so the total number of batches for your data is

60000/32 = 1875

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