CNN in Fashion MNIST dataset using Keras
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. It has a total of 10 classes. In this example we will be performing CNN in this dataset.
Importing Libraries
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Firstly, import the required libraries.
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We will import tensorflow and tf.keras which is TensorFlow's high-level API for building and training deep learning models. Other libraries numpy and matplotlib will be also be imported for plotting graphs and other operations.
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# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
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Loading Data
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We will store the data into fashion_mnist using keras.
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Then, we split the data into training data and testing data. The test data is used for validation.
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In class_names, we will be providing the class names. The dataset has 10 classes.
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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']
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- Now we will view the training data shape, length of training labels, training labels, testing data shape, length of test labels.
print(train_images.shape)
print(len(train_labels))
print(train_labels)
print(test_images.shape)
print(len(test_labels)) |
(60000, 28, 28) # Train image shape
60000 # Print length of training labels
array([9, 0, 0, ..., 3, 0, 5], dtype=uint8) # Print training labels
(10000, 28, 28) # Test image shape
10000 # Print length of test labels
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Let's see how 0th image of train image looks like using matplotlib library.
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plt.figure is to create a figure object.
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imshow() creates an image from a 2-dimensional numpy array.
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colorbar is for using the new axes
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Inside plt.grid we are passing false value so that it does not create any grid.
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plt.show() will display the current figure that you are working on.
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plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()
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Pre-Processing of data
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Now, we will be pre-processing the data before training it.
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We will now scale these values to a range of 0 to 1 by diving them by 255.
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Training and testing dataset must be pre-processed in same the way.
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train_images = train_images / 255.0
test_images = test_images / 255.0
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We will be displaying the first 25 images from the training dataset alongwith class name in order to verify that the data that we want to feed is ready for training.
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plt.figure is to create a figure object.
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the subplot will take the index position on a grid with nrows rows and ncols columns.
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The plt.tick() is used for getting or set the current tick locations and labels of the x-axis and y-axis. We have passed no arguments so that it returns the current values without modifying them.
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Inside plt.grid we are passing false value so that it does not create any grid.
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imshow() creates an image from a 2-dimensional numpy array.
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The plt.xlabel() command accepts a string as an argument.
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plt.show() will display the current figure that you are working on.
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plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i], cmap=plt.cm.binary)
plt.xlabel(class_names[train_labels[i]])
plt.show()
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Creating model
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Now we will configure the layers of model for compilation.
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We create sequential model having all the layers.
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Then, we will flatten the input layer which is of shape 28*28.
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A dense layer is a classic fully connected neural network layer each input node is connected to each output node.
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We have passed relu as activation function.
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The first Dense layer has 128 neurons. The second layer returns a logits array with length of 10.
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model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10,activation='softmax')
])
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Compiling the model
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Now we will compile the model.
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Loss function measures how accurate the model is during training. We have to minimize the loss function.
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Optimizer shoes how the model is updated based on the data it sees and its loss function.
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Metrics is used to monitor the training and testing steps. The following example uses accuracy, the fraction of the images that are correctly classified.
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model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
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Training the model
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Now we will train the model using model.fit().
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We have passed 5 epochs. An epoch is when an entire dataset is passed forward and backward through the neural network only once.
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You can change the number of epoch for improving the accuracy.
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model.fit(train_images, train_labels, epochs=10)
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Epoch 1/10
60000/60000 [==============================] - 6s 101us/sample - loss: 0.5007 - acc: 0.8248
Epoch 2/10
60000/60000 [==============================] - 6s 97us/sample - loss: 0.3725 - acc: 0.8659
Epoch 3/10
60000/60000 [==============================] - 6s 97us/sample - loss: 0.3336 - acc: 0.8787
Epoch 4/10
60000/60000 [==============================] - 6s 97us/sample - loss: 0.3095 - acc: 0.8864
Epoch 5/10
60000/60000 [==============================] - 6s 98us/sample - loss: 0.2917 - acc: 0.8915
Epoch 6/10
60000/60000 [==============================] - 6s 98us/sample - loss: 0.2789 - acc: 0.8960
Epoch 7/10
60000/60000 [==============================] - 6s 98us/sample - loss: 0.2669 - acc: 0.9011
Epoch 8/10
60000/60000 [==============================] - 6s 98us/sample - loss: 0.2547 - acc: 0.9068
Epoch 9/10
60000/60000 [==============================] - 6s 98us/sample - loss: 0.2451 - acc: 0.9089
Epoch 10/10
60000/60000 [==============================] - 6s 98us/sample - loss: 0.2369 - acc: 0.9117
Test Accuracy
- Now, evaluate the accuracy of the test model.
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test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)
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10000/10000 - 1s - loss: 0.3439 - acc: 0.8824
Test accuracy: 0.8824
Prediction
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Now, in output layer generally the softmax activation function is passed.
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Here, we will predict the image and also see it's test label.
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So, the model is most confident that this image is an ankle boot, or class_names[9].
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predictions = model.predict(test_images)
print("Predicting the class : ",np.argmax(predictions[0]))
print("Test Label : ",test_labels[0])
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Predicting the class : 9
Test Label : 9
Prediction and Plotting First 10 images from Test Set
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plt.figure(figsize=(10,10))
for i in range(10):
plt.subplot(5,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(test_images[i], cmap=plt.cm.binary)
img = test_images[i]
img = img.reshape(1, 28, 28, 1)
predictions = model.predict(img)
#print(np.argmax(predictions))
plt.xlabel(class_names[np.argmax(predictions)])
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