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. We intend 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.
Here's an example of how the data looks (each class takes three-rows):
You can clone this GitHub repository; the dataset appears under data/fashion. This repo also contains some scripts for benchmark and visualization.
git clone git@github.com:zalandoresearch/fashion-mnist.gitEach training and test example is assigned to one of the following labels:
| Label | Description |
|---|---|
| 0 | T-shirt/top |
| 1 | Trouser |
| 2 | Pullover |
| 3 | Dress |
| 4 | Coat |
| 5 | Sandal |
| 6 | Shirt |
| 7 | Sneaker |
| 8 | Bag |
| 9 | Ankle boot |
Make sure you have downloaded the data and placed it in data/fashion. Otherwise, Tensorflow will download and use the original MNIST.
from tensorflow.examples.tutorials.mnist import input_data
data = input_data.read_data_sets('data/fashion')
data.train.next_batch(BATCH_SIZE)Note, Tensorflow supports passing in a source url to the read_data_sets. You may use:
data = input_data.read_data_sets('data/fashion', source_url='http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/')from keras.models import Sequential
from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D
model = Sequential()
model.add(Conv2D(28,kernel_size=(3,3),input_shape=input_shape)) #should be the first layer 28 represents the pixels
#kernel size for scnning the image is (3,3) pixels
#input shape is the input of the pixel size and the shader
model.add(MaxPooling2D(pool_size=(2,2)))#pools the image size into a smaller matrix max pooling take the max size in the
#frame we have set and it will be pooled in a (2,2) matrix
model.add(Flatten()) #flattens the data into one dimentional layer i.e array so it can be connected into many layers
model.add(Dense(128,activation=tf.nn.relu)) #connects the layers together relu = rectified linear unit 128 is the output
#tf = tensorflow . nn = neural networks . relu
model.add(Dropout(0.1)) #removes some connections of neural network because it will remember exactly the same of how a
#image looks like hence it will be a problem detecting the similar images
#0.1 is the percentage of neural networks you want to cut
model.add(Dense(10,activation = tf.nn.softmax)) #softmax takes care of the max probabilistic outcomemodel.compile(optimizer='adam', #minimizes the loss
loss = 'sparse_categorical_crossentropy', #calculates the loss
metrics=['accuracy'])#calculates accuracy
model.fit(x=x_train,y=y_train,epochs=6) #data is sent into model and epochs = 6 are the iterations performed on the model to
#make it corrent