Tensorflow/Keras:训练期间的模型准确性始终为0.5，且输入大小与首次官方教程不同 [英] Tensorflow/Keras: Model accuracy during training is always 0.5 with input size different from first official tutorial
问题描述
我是深度学习和keras/tensorflow的初学者. 我已经按照 tensorflow.org 上的第一个教程进行学习:这是时尚MNIST的基本分类.
I'm beginner with deep learning and keras/tensorflow. I have followed the first tutorial on tensorflow.org: a basic classification with fashion MNIST.
在这种情况下，输入数据是60000张28x28图像，模型是这样的:
In this case the input data are 60000, 28x28 images and the model is this:
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(10, activation=tf.nn.softmax)
])
编译:
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
训练结束时，模型具有以下准确性:
At the end of training the model has this accuracy:
10000/10000 [==============================] - 0s 21us/step
Test accuracy: 0.8769
没关系. 现在，我正在尝试使用另一组数据来复制此模型.新输入是从 kaggle 下载的数据集.
It's ok. Now I'm trying to duplicate this model with another set of datas. New input is a dataset downloaded from kaggle.
该数据集包含大小不同的狗和猫的图像，因此我创建了一个简单的脚本来获取图像，将其调整为28x28像素大小并转换为numpy数组.
The dataset has images with different sized of dogs and cats, so I have create a simple script that get the images, resize in 28x28 pixel and convert in a numpy array.
这是执行此操作的代码:
This is the code to do this:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from tensorflow.keras.models import load_model
from PIL import Image
import os
# Helper libraries
import numpy as np
# base path dataset
base_path = './dataset/'
training_path = base_path + "training_set/"
test_path = base_path + "test_set/"
# size rate of images
size = 28, 28
#
train_images = []
train_labels = []
test_images = []
test_labels = []
classes = ['dogs', 'cats']
# Scorre sulle cartelle contenute nel path e trasforma le immagini in nparray
def from_files_to_nparray(path):
images = []
labels = []
for subfolder in os.listdir(path):
if subfolder == '.DS_Store':
continue
for image_name in os.listdir(path + subfolder):
if not image_name.endswith('.jpg'):
continue
img = Image.open(path + subfolder + "/" + image_name).convert("L").resize(size) # convert to grayscale and resize
npimage = np.asarray(img)
images.append(npimage)
labels.append(classes.index(subfolder))
img.close()
# convertt to np arrays
images = np.asarray(images)
labels = np.asarray(labels)
# Normalize to [0, 1]
images = images / 255.0
return (images, labels)
(train_images, train_labels) = from_files_to_nparray(training_path)
(test_images, test_labels) = from_files_to_nparray(test_path)
最后，我有以下形状:
Train images shape : (8000, 128, 128)
Labels images shape : (8000,)
Test images shape : (2000, 128, 128)
Test images shape : (2000,)
在训练了相同的模型(但最后一个密集层格式为2个神经元)后，我得到了这个结果，应该没问题:
After training the same model (but with the last dense layer format by 2 neurons) I have this result, that should be ok:
Train images shape : (8000, 28, 28)
Labels images shape : (8000,)
Test images shape : (2000, 28, 28)
Test images shape : (2000,)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten (Flatten) (None, 784) 0
_________________________________________________________________
dense (Dense) (None, 128) 100480
_________________________________________________________________
dense_1 (Dense) (None, 2) 258
=================================================================
Total params: 100,738
Trainable params: 100,738
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/5
2018-07-27 15:25:51.283117: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
8000/8000 [==============================] - 1s 66us/step - loss: 0.6924 - acc: 0.5466
Epoch 2/5
8000/8000 [==============================] - 0s 39us/step - loss: 0.6679 - acc: 0.5822
Epoch 3/5
8000/8000 [==============================] - 0s 41us/step - loss: 0.6593 - acc: 0.6048
Epoch 4/5
8000/8000 [==============================] - 0s 39us/step - loss: 0.6545 - acc: 0.6134
Epoch 5/5
8000/8000 [==============================] - 0s 39us/step - loss: 0.6559 - acc: 0.6039
2000/2000 [==============================] - 0s 33us/step
Test accuracy: 0.592
现在，问题是，如果我尝试将输入大小从28x28更改为例如128x128，则结果是这样的:
Now, the question is, if I try to change the input size from 28x28 to, for example 128x128 the result is this:
Train images shape : (8000, 128, 128)
Labels images shape : (8000,)
Test images shape : (2000, 128, 128)
Test images shape : (2000,)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten (Flatten) (None, 16384) 0
_________________________________________________________________
dense (Dense) (None, 128) 2097280
_________________________________________________________________
dense_1 (Dense) (None, 2) 258
=================================================================
Total params: 2,097,538
Trainable params: 2,097,538
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/5
2018-07-27 15:27:41.966860: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
8000/8000 [==============================] - 4s 483us/step - loss: 8.0341 - acc: 0.4993
Epoch 2/5
8000/8000 [==============================] - 3s 362us/step - loss: 8.0590 - acc: 0.5000
Epoch 3/5
8000/8000 [==============================] - 3s 351us/step - loss: 8.0590 - acc: 0.5000
Epoch 4/5
8000/8000 [==============================] - 3s 342us/step - loss: 8.0590 - acc: 0.5000
Epoch 5/5
8000/8000 [==============================] - 3s 342us/step - loss: 8.0590 - acc: 0.5000
2000/2000 [==============================] - 0s 217us/step
Test accuracy: 0.5
为什么?尽管添加新的致密层或增加神经元数量，结果是相同的.
Why? Though adding a new dense layer or increasing the neuron numbers the result is the same.
输入大小和模型层之间有什么联系?谢谢！
What is the connection between the input size and the model layers? Thanks!
推荐答案
问题是在第二个示例中您需要训练更多的参数.在第一个示例中，您只有100k参数.您用8k图像训练它们.
The problem is that you have more parameters to train in the second example. In the first example you just have 100k Parameters. You train them with 8k images.
在第二个示例中，您具有2000k参数，并尝试使用相同数量的图像对其进行训练.这是行不通的，因为自由参数和样本数之间存在关系.没有精确的公式可以计算这种关系，但是根据经验，应该有比可训练参数更多的样本.
In the second example you have 2000k Parameters and you try to train them with the same amount of images. This does not work because there is a relation between the free parameters and the number of samples. There is no exact formula to calculate this relation, but there is a rule of thumb that you should have more samples than trainable parameters.
您可以尝试用它来训练更多的纪元并了解其工作原理，但总的来说，对于更复杂的模型，您需要更多的数据.
What you can try it to train more epochs and to look how it works but in general you need more data for more complex models.
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