模型拟合未使用所有提供的数据 [英] Model fitting doesn't use all of the provided data

问题描述

我在玩Tensorflow 2.0 Keras入门教程( https:/时遇到问题. /www.tensorflow.org/tutorials/keras/classification ).

I ran into a problem when playing with the introduction Tutorial for Tensorflow 2.0 Keras (https://www.tensorflow.org/tutorials/keras/classification).

问题:

应该存在(并且有)60.000张图像以适合该模型.我通过打印出train_images和train_labels的长度来进行检查.

There should be (and there are) 60.000 Images to fit the model. I checked this by printing out the length of train_images and train_labels.

另一方面，在拟合模型时的输出让我相信，并非像1875/1875那样使用了所有数据.测试数据也一样.

The output when fitting the model on the other hand lets me believe that not all of the data was used as it says 1875/1875. Same for the Testing Data.

我停用了对此无效的GPU检测(看来).

我正在使用:

• Python 3.8.3
• Tensorflow 2.2.0

我的代码:

import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

data = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = data.load_data()

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

# preprocess the image data to have a pixel value between 0 and 1
train_images = train_images / 255.0
test_images = test_images / 255.0

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)

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

print('\nTest accuracy:', test_acc)

输出:

2020-05-17 17:48:07.147033: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-05-17 17:48:10.075816: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2020-05-17 17:48:10.098581: E tensorflow/stream_executor/cuda/cuda_driver.cc:313] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2020-05-17 17:48:10.105898: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: DESKTOP-UU9P1OG
2020-05-17 17:48:10.109837: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-UU9P1OG
2020-05-17 17:48:10.113879: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-05-17 17:48:10.127711: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x14dc97288a0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-05-17 17:48:10.132743: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
Epoch 1/10
1875/1875 [==============================] - 2s 1ms/step - loss: 0.4943 - accuracy: 0.8264
Epoch 2/10
1875/1875 [==============================] - 2s 938us/step - loss: 0.3747 - accuracy: 0.8649
Epoch 3/10
1875/1875 [==============================] - 2s 929us/step - loss: 0.3403 - accuracy: 0.8762
Epoch 4/10
1875/1875 [==============================] - 2s 914us/step - loss: 0.3146 - accuracy: 0.8844
Epoch 5/10
1875/1875 [==============================] - 2s 937us/step - loss: 0.2985 - accuracy: 0.8900
Epoch 6/10
1875/1875 [==============================] - 2s 923us/step - loss: 0.2808 - accuracy: 0.8964
Epoch 7/10
1875/1875 [==============================] - 2s 939us/step - loss: 0.2702 - accuracy: 0.8998
Epoch 8/10
1875/1875 [==============================] - 2s 911us/step - loss: 0.2585 - accuracy: 0.9032
Epoch 9/10
1875/1875 [==============================] - 2s 918us/step - loss: 0.2482 - accuracy: 0.9073
Epoch 10/10
1875/1875 [==============================] - 2s 931us/step - loss: 0.2412 - accuracy: 0.9106
313/313 - 0s - loss: 0.3484 - accuracy: 0.8729

Test accuracy: 0.8729000091552734

推荐答案

该模型的批处理大小为32，因此有60,000/32 = 1875个批处理.

The model is being trained with a batchsize of 32, hence there are 60,000/32 = 1875 batches.

尽管 tensorflow文档在batch_size=None >函数概述，有关此参数的信息说:

Despite tensorflow documentation shows batch_size=None in the fit function overview, the information about this argument says:

batch_size:整数或无.每个梯度更新的样本数. 如果未指定，batch_size将默认为32.如果您的数据采用数据集，生成器或keras.utils.Sequence实例的形式(因为它们生成批次)，则不要指定batch_size.

batch_size: Integer or None. Number of samples per gradient update. If unspecified, batch_size will default to 32. Do not specify the batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches).

这篇关于模型拟合未使用所有提供的数据的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！