import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

plt.rcParams['figure.figsize'] = (16, 10)
plt.rc('font', size=15)


## Fashion MNIST

Yann LeCun introduced Convolutional Neural Network (CNN for short) through his paper, namely LeNet-5, and shows its effectiveness in hand-written digits. The dataset used his paper is called "Modified National Institute of Standards and Technology"(or MNIST for short), and it is widely used for validating the neural network performance.

Each image has 28x28 shapes, and is grayscaled (meaning that each pixel value has a range from 0 to 255). But as you notice from original image, features for each digits are almost clear, so most of neural network in now can easily learn its dataset. And also the task cannot represent the complicated task. So there are many trials to formalize its baseline dataset. One of these is Fashion-MNIST, presented by Zalando research. Its dataset also has 28x28 pixels, and has 10 labels to classify. So main properties are same as Original MNIST, but it is hard to classify it.

In this post, we will use Fashion MNIST dataset classification with tensorflow 2.x. For the prerequisite for implementation, please check the previous posts.

### Data Preprocessing

Actually, tensorflow-keras includes several baseline datasets, including FashionMNIST. It contains 60000 training datasets, 10000 test datasets for validation, and 10 labels. Also, each dataset has grayscale. At first, we can load the dataset into variables. Let's see what it looks like.

(X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
print(X_train[0])
print(y_train[0])
print(X_train.shape)
print(y_train.shape)

[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0
0   1   4   0   0   0   0   1   1   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62
54   0   0   0   1   3   4   0   0   3]
[  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134
144 123  23   0   0   0   0  12  10   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178
107 156 161 109  64  23  77 130  72  15]
[  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216
216 163 127 121 122 146 141  88 172  66]
[  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229
223 223 215 213 164 127 123 196 229   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228
235 227 224 222 224 221 223 245 173   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198
180 212 210 211 213 223 220 243 202   0]
[  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192
169 227 208 218 224 212 226 197 209  52]
[  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203
198 221 215 213 222 220 245 119 167  56]
[  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240
232 213 218 223 234 217 217 209  92   0]
[  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219
222 221 216 223 229 215 218 255  77   0]
[  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208
211 218 224 223 219 215 224 244 159   0]
[  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230
224 234 176 188 250 248 233 238 215   0]
[  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223
255 255 221 234 221 211 220 232 246   0]
[  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221
188 154 191 210 204 209 222 228 225   0]
[ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117
168 219 221 215 217 223 223 224 229  29]
[ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245
239 223 218 212 209 222 220 221 230  67]
[ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216
199 206 186 181 177 172 181 205 206 115]
[  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191
195 191 198 192 176 156 167 177 210  92]
[  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209
210 210 211 188 188 194 192 216 170   0]
[  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179
182 182 181 176 166 168  99  58   0   0]
[  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]
[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0   0   0   0   0   0   0   0   0   0]]
9
(60000, 28, 28)
(60000,)


As you can see, each pixel value has a range from 0 to 255. and image has 2d array. So it requires to normalize it and reshape it with 1D array for training neural network (since we cover the MLP, we need to reshape it with 1D array. If we use CNN, we don't need to convert it).

X_train = X_train / 255.
X_train = X_train.reshape([-1, 28*28])
X_train = X_train.astype(np.float32)
y_train = y_train.astype(np.int32)

X_test = X_test / 255.
X_test = X_test.reshape([-1, 28*28])
X_test = X_test.astype(np.float32)
y_test = y_test.astype(np.int32)


### Input Pipeline

As you can see from previous post, it requires to convert raw dataset into tensorflow input pipeline. While building input pipeline, we can chain the method with shuffle, prefetch, and repeat. Note that, the purpose of test dataset is to measure the performance. So we don't need to shuffle it.

train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train))\
.shuffle(buffer_size=len(X_train))\
.batch(batch_size=128)\
.prefetch(buffer_size=128)\
.repeat()

# Test dataset
test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test))\
.batch(batch_size=128)\
.prefetch(buffer_size=128)\
.repeat()


### Sample data visualization

Actually, it is important to check the dataset manually. And it is require to visualize sample data. In this section, we'll visualize each label of image in 5x5 matrix.

labels_map = {0: 'T-Shirt', 1: 'Trouser', 2: 'Pullover', 3: 'Dress', 4: 'Coat',
5: 'Sandal', 6: 'Shirt', 7: 'Sneaker', 8: 'Bag', 9: 'Ankle Boot'}
columns = 5
rows = 5
fig = plt.figure(figsize=(8, 8))

for i in range(1, columns * rows+1):
data_idx = np.random.randint(len(X_train))
img = X_train[data_idx].reshape([28, 28])
label = labels_map[y_train[data_idx]]

plt.title(label)
plt.imshow(img, cmap='gray')
plt.axis('off')
plt.tight_layout()
plt.show()


### Building Neural network.

In this section, we'll build the Multi Layer Perceptron (MLP for short) with 2 Dense Layers. MLP, also called Artificial Neural Network, consists of several fully-connected layers. We can add activation function(sigmoid, ReLU or Softmax) for each layer. We can also apply advanced techniques like weight initialization, Dropout or Batch Normalization. Here, we will build 2 Dense layers in Sequential model.

model = tf.keras.Sequential(name='nn')



We need to figure some points.

• In the input layer, we implemented Dense layer with 256 nodes, and it accepts 28x28-shaped(or 764) input. Since the shape of image is 28x28, and 1D array converted from 2d array will enter here. So we need to define input_shape here. Note that, input_shape argument have to be a tuple type.
• We added Batch Normalization. Batch Normalization can reduce the effect of Internal Covariate Shift. And it would maintain the information distribution to be normal distribution.
• Here, we added ReLU activation function. We can also add this as an argument of layers.
• Since this task is a sort of multi-class classification, Softmax activation function is added at the end of the output layer.

We can get summary of this model. From the summary, we can check how many layers implement this model, and how many parameters in this model, etc.

model.summary()

Model: "nn"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense_4 (Dense)              (None, 256)               200960
_________________________________________________________________
batch_normalization_2 (Batch (None, 256)               1024
_________________________________________________________________
re_lu_2 (ReLU)               (None, 256)               0
_________________________________________________________________
dense_5 (Dense)              (None, 10)                2570
=================================================================
Total params: 204,554
Trainable params: 204,042
Non-trainable params: 512
_________________________________________________________________


### Model compile

we're almost at the end. Here, we need to compile the model to train. Before compiling, it is required to define loss function and optimizer. As you can see in the documentation, there are lots of loss functions predefined. In this task, we need to classify the label, so our loss function may be categorical crossentropy. But keep in mind that, if your label is sort of one-hot encoding, you need to use categorical_crossentropy. Since our label is just integer, meaning its label index, our loss function may be SparseCategoricalCrossentropy.

And mainly-used optimizer is Adam with 0.01 learning rate.

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])


### Model fit

At last, we can train the model with model.fit. Here, we can also define batch size, and epochs, and steps.

model.fit(train_ds, batch_size=128, steps_per_epoch=len(X_train)/128, epochs=10)

Epoch 1/10
469/468 [==============================] - 1s 1ms/step - loss: 0.4541 - accuracy: 0.8366
Epoch 2/10
469/468 [==============================] - 1s 1ms/step - loss: 0.3444 - accuracy: 0.8747
Epoch 3/10
469/468 [==============================] - 1s 1ms/step - loss: 0.3058 - accuracy: 0.8874
Epoch 4/10
469/468 [==============================] - 1s 1ms/step - loss: 0.2822 - accuracy: 0.8959
Epoch 5/10
469/468 [==============================] - 1s 1ms/step - loss: 0.2680 - accuracy: 0.8996
Epoch 6/10
469/468 [==============================] - 1s 1ms/step - loss: 0.2474 - accuracy: 0.9092
Epoch 7/10
469/468 [==============================] - 1s 1ms/step - loss: 0.2385 - accuracy: 0.9116
Epoch 8/10
469/468 [==============================] - 1s 1ms/step - loss: 0.2219 - accuracy: 0.9186
Epoch 9/10
469/468 [==============================] - 1s 1ms/step - loss: 0.2104 - accuracy: 0.9222
Epoch 10/10
469/468 [==============================] - 1s 1ms/step - loss: 0.2002 - accuracy: 0.9249

<tensorflow.python.keras.callbacks.History at 0x7f91d8dad5d0>

### Evaluation

We have the model with trained weight. So we can evaluate our model performance with test data. Since test dataset is unseen data from model, so its accuracy and loss may be lower than training one.

loss, acc = model.evaluate(test_ds, steps=len(X_test)/128)
print('test loss is {}'.format(loss))
print('test accuracy is {}'.format(acc))

79/78 [==============================] - 0s 987us/step - loss: 0.4087 - accuracy: 0.8724
test loss is 0.408719003200531
test accuracy is 0.8723999857902527


Also, we can visualize its performance. It'll also visualize true or false of label classification.

test_batch_size = 25
batch_index = np.random.choice(len(X_test), size=test_batch_size, replace=False)

batch_xs = X_test[batch_index]
batch_ys = y_test[batch_index]
y_pred_ = model(batch_xs, training=False)

fig = plt.figure(figsize=(10, 10))
for i, (px, py, y_pred) in enumerate(zip(batch_xs, batch_ys, y_pred_)):
p = fig.add_subplot(5, 5, i+1)
if np.argmax(y_pred) == py:
p.set_title("{}".format(labels_map[py]), color='blue')
else:
p.set_title("{}/{}".format(labels_map[np.argmax(y_pred)],
labels_map[py]), color='red')
p.imshow(px.reshape(28, 28))
p.axis('off')
plt.tight_layout()


At last, we implement the Multi layer perceptron for image classification. There are some incorrect prediction. But we can improve your model with hyperparameter tuning (the number of epoch, the number of layers, input nodes, learning rate, etc..)

## Summary

In this post, we implemented the neural network for Fashion-MNIST. Through this process, we preprocess the dataset and generate the input pipeline. Then add the layers in sequential model. After that, we defined loss function and optimizers for training.

Thanks to the tensorflow-keras, we can easily train the model and evalute its performance.