Prerequisites: Logistic Regression
Getting Started With Keras:
Deep learning is one of the major subfields of machine learning framework. It is supported by various libraries such as Theano, TensorFlow, Caffe, Mxnet etc., Keras is one of the most powerful and easy to use python library, which is built on top of popular deep learning libraries like TensorFlow, Theano, etc., for creating deep learning models.
Keras offers a collection of datasets that can be used to train and test the model. The Fashion MNIST dataset is a part of the available datasets present in the tf.keras datasets API. This dataset contains 70 thousand images of fashion objects that spread across 10 categories such as shoe, bag, T-shirts etc. which are scaled to 28 by 28 Grayscale pixels.
Approach:
So, the approach would be to first load the MNIST Objects Dataset and then we will use the Matplotlib to see examples so as to get a better idea about the dataset. Then finally we will classify them using Keras API by building a Neural Network. Later we will test our trained model on the test set to check the accuracy of our model.
Implementation:
Code: Loading the Data
Python3
mnist = tf.keras.datasets.fashion_mnist(training_images, training_labels), (test_images, test_labels) = mnist.load_data() |
Calling load_data on this object will give us two sets of two lists, these will be the training and testing values for the graphics that contain the dataset items and their labels.
Code: Understanding The Data
Python3
import numpy as npnp.set_printoptions(linewidth = 200)import matplotlib.pyplot as pltplt.imshow(training_images[42])# printing training labels and training imagesprint(training_labels[42])print(training_images[42]) |
Output:
9 [[ 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 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 82 187 26 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 179 240 237 255 240 139 83 64 43 60 54 0 1] [ 0 0 0 0 0 0 0 0 0 1 0 0 1 0 58 239 222 234 238 246 252 254 255 248 255 187 0 0] [ 0 0 0 0 0 0 0 0 0 0 2 3 0 0 194 239 226 237 235 232 230 234 234 233 249 171 0 0] [ 0 0 0 0 0 0 0 0 0 1 1 0 0 10 255 226 242 239 238 239 240 239 242 238 248 192 0 0] [ 0 0 0 0 0 0 0 0 0 0 0 0 0 172 245 229 240 241 240 241 243 243 241 227 250 209 0 0] [ 0 0 0 0 0 0 0 0 0 6 5 0 62 255 230 236 239 241 242 241 242 242 238 238 242 253 0 0] [ 0 0 0 0 0 0 0 0 0 3 0 0 255 235 228 244 241 241 244 243 243 244 243 239 235 255 22 0] [ 0 0 0 0 0 0 0 0 0 0 0 246 228 220 245 243 237 241 242 242 242 243 239 237 235 253 106 0] [ 0 0 3 4 4 2 1 0 0 18 243 228 231 241 243 237 238 242 241 240 240 240 235 237 236 246 234 0] [ 1 0 0 0 0 0 0 0 22 255 238 227 238 239 237 241 241 237 236 238 239 239 239 239 239 237 255 0] [ 0 0 0 0 0 25 83 168 255 225 225 235 228 230 227 225 227 231 232 237 240 236 238 239 239 235 251 62] [ 0 165 225 220 224 255 255 233 229 223 227 228 231 232 235 237 233 230 228 230 233 232 235 233 234 235 255 58] [ 52 251 221 226 227 225 225 225 226 226 225 227 231 229 232 239 245 250 251 252 254 254 252 254 252 235 255 0] [ 31 208 230 233 233 237 236 236 241 235 241 247 251 254 242 236 233 227 219 202 193 189 186 181 171 165 190 42] [ 77 199 172 188 199 202 218 219 220 229 234 222 213 209 207 210 203 184 152 171 165 162 162 167 168 157 192 78] [ 0 45 101 140 159 174 182 186 185 188 195 197 188 175 133 70 19 0 0 209 231 218 222 224 227 217 229 93] [ 0 0 0 0 0 0 2 24 37 45 32 18 11 0 0 0 0 0 0 72 51 53 37 34 29 31 5 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 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]]
Normalisation:
Notice that all of the values in the number are between 0 and 255. If we are training a neural network, for various reasons it’s easier if we treat all values as between 0 and 1, a process called ‘normalizing’…and fortunately in Python it’s easy to normalize a list like this without looping. We can do it like this:
Code:
Python3
training_images = training_images / 255.0test_images = test_images / 255.0 |
Code: Implementing Keras Model
Python3
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(), tf.keras.layers.Dense(128, activation = tf.nn.relu), tf.keras.layers.Dense(10, activation = tf.nn.softmax)]) |
- Sequential: That defines a SEQUENCE of layers in the neural network.
- Flatten: It justs takes the image and convert it to a 1 Dimensional set.
- Dense: Adds a layer of neurons.
- Each layer of neurons need an activation function to tell them what to do. There’s lots of options, but just use these for now.
- Relu: Effectively means “If X > 0 return X, else return 0″ — so what it does it it only passes values 0 or greater to the next layer in the network.
- Softmax: takes a set of values, and effectively picks the biggest one, so, for example, if the output of the last layer looks like [0.1, 0.1, 0.05, 0.1, 9.5, 0.1, 0.05, 0.05, 0.05], it saves you from fishing through it looking for the biggest value, and turns it into [0, 0, 0, 0, 1, 0, 0, 0, 0]. The goal is to save a lot of coding
Step 5: Compile The Model
The next thing to do, now the model is defined, is to actually build it. You do this by compiling it with an optimizer and loss function as before and then you train it by calling model.fit asking it to fit your training data to your training labels i.e. have it figure out the relationship between the training data and its actual labels, so in future, if you have data that looks like the training data, then it can make a prediction for what that data would look like.
Code:
Python3
model.compile(optimizer = tf.optimizers.Adam(), loss = 'sparse_categorical_crossentropy', metrics =['accuracy'])model.fit(training_images, training_labels, epochs = 5) |
Output:
Instructions for updating: Colocations handled automatically by placer. Epoch 1/5 60000/60000 [==============================] - 8s 130us/sample - loss: 0.4714 - acc: 0.8322 Epoch 2/5 60000/60000 [==============================] - 8s 137us/sample - loss: 0.3598 - acc: 0.8683 Epoch 3/5 60000/60000 [==============================] - 9s 142us/sample - loss: 0.3201 - acc: 0.8824 Epoch 4/5 60000/60000 [==============================] - 8s 131us/sample - loss: 0.2949 - acc: 0.8917 Epoch 5/5 60000/60000 [==============================] - 8s 140us/sample - loss: 0.2767 - acc: 0.9098
Once it’s done training we should see an accuracy value at the end of the final epoch. It might look something like 0.9098. This tells us that your neural network is about 91% accurate in classifying the training data. i.e. it figured out a pattern match between the image and the labels that worked 91% of the time. Not great, but not bad considering it was only trained for 5 epochs and done quite quickly.
Step 6: Model Evaluation
But how would it work with unseen data? That’s why we have the test images. We can call model.evaluate, and pass in the two sets, and it will report back the loss for each.
Code:
Python3
model.evaluate(test_images, test_labels) |
Output:
10000/10000 [==============================] - 1s 60us/sample - loss: 0.2908 - acc: 0.8956
Finally, we have trained our model and got an accuracy of 90% on the unseen dataset. That’s pretty good.
Advantages of Using KERAS:
We have seen that our calculations have just reduced to 7-8 lines rather than a hundred lines of code. That’s awesome. Overall, this helps us save our time and energy and also reduces the chances of error in our code.
