fashion MNIST识别（Tensorflow + Keras + NN）

淩亂°似流年 2023-08-17 15:34 165阅读 0赞

# Fashion MNIST #

https://www.kaggle.com/zalando-research/fashionmnist

> Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.
> 
> The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. "If it doesn't work on MNIST, it won't work at all", they said. "Well, if it does work on MNIST, it may still fail on others."
> 
> Zalando seeks to replace the original MNIST dataset

# Code #

https://github.com/fanqingsong/code-snippet/blob/master/machine\_learning/FMNIST/code.py

# TensorFlow and tf.keras
    import tensorflow as tf
    from tensorflow import keras
    
    # Helper libraries
    import numpy as np
    
    print(tf.__version__)
    
    
    fashion_mnist = keras.datasets.fashion_mnist
    
    (train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
    
    
    class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
                   'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
    
    
    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=tf.nn.relu),
        keras.layers.Dense(10, activation=tf.nn.softmax)
    ])
    
    
    model.compile(optimizer=tf.train.AdamOptimizer(),
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    
    
    model.fit(train_images, train_labels, epochs=5)
    
    
    test_loss, test_acc = model.evaluate(test_images, test_labels)
    
    print('Test accuracy:', test_acc)
    
    
    predictions = model.predict(test_images)
    
    
    print(test_labels[0])
    
    print(np.argmax(predictions[0]))

# run #

> root@DESKTOP-OGSLB14:~/mine/code-snippet/machine\_learning/FMNIST\#  
> root@DESKTOP-OGSLB14:~/mine/code-snippet/machine\_learning/FMNIST\# python code.py  
> 1.14.0  
> WARNING: Logging before flag parsing goes to stderr.  
> W0816 23:26:49.741352 140630311962432 deprecation.py:506\] From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/init\_ops.py:1251: calling \_\_init\_\_ (from tensorflow.python.ops.init\_ops) with dtype is deprecated and will be removed in a future version.  
> Instructions for updating:  
> Call initializer instance with the dtype argument instead of passing it to the constructor  
> W0816 23:26:49.977197 140630311962432 deprecation\_wrapper.py:119\] From code.py:33: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.  
>   
> 2019-08-16 23:26:50.289949: I tensorflow/core/platform/cpu\_feature\_guard.cc:142\] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA  
> 2019-08-16 23:26:50.684455: I tensorflow/core/platform/profile\_utils/cpu\_utils.cc:94\] CPU Frequency: 1992000000 Hz  
> 2019-08-16 23:26:50.686887: I tensorflow/compiler/xla/service/service.cc:168\] XLA service 0x7fffe64d99e0 executing computations on platform Host. Devices:  
> 2019-08-16 23:26:50.686967: I tensorflow/compiler/xla/service/service.cc:175\]   StreamExecutor device (0): <undefined>, <undefined>  
> 2019-08-16 23:26:50.958569: W tensorflow/compiler/jit/mark\_for\_compilation\_pass.cc:1412\] (One-time warning): Not using XLA:CPU for cluster because envvar TF\_XLA\_FLAGS=--tf\_xla\_cpu\_global\_jit was not set.  If you want XLA:CPU, either set that envvar, or use experimental\_jit\_scope to enable XLA:CPU.  To confirm that XLA is active, pass --vmodule=xla\_compilation\_cache=1 (as a proper command-line flag, not via TF\_XLA\_FLAGS) or set the envvar XLA\_FLAGS=--xla\_hlo\_profile.  
> Epoch 1/5  
> 60000/60000 \[==============================\] - 3s 50us/sample - loss: 0.4992 - acc: 0.8240  
> Epoch 2/5  
> 60000/60000 \[==============================\] - 2s 40us/sample - loss: 0.3758 - acc: 0.8650  
> Epoch 3/5  
> 60000/60000 \[==============================\] - 3s 42us/sample - loss: 0.3382 - acc: 0.8770  
> Epoch 4/5  
> 60000/60000 \[==============================\] - 2s 41us/sample - loss: 0.3135 - acc: 0.8854  
> Epoch 5/5  
> 60000/60000 \[==============================\] - 3s 42us/sample - loss: 0.2953 - acc: 0.8922  
> 10000/10000 \[==============================\] - 0s 25us/sample - loss: 0.3533 - acc: 0.8715  
> ('Test accuracy:', 0.8715)  
> 9  
> 9  
> root@DESKTOP-OGSLB14:~/mine/code-snippet/machine\_learning/FMNIST\#  
>   
>

# Reference #

https://github.com/MachineIntellect/DeepLearner/blob/master/basic\_classification.ipynb

https://tensorflow.google.cn/beta/guide/data

转载于:https://www.cnblogs.com/lightsong/p/11366934.html