Artificial neural networks are one of the main tools used in machine learning. As the “neural” part of their name suggests, they are brain-inspired systems which are intended to replicate the way that we humans learn.
Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use.
They are excellent tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize.
This Machine Learning Project Classifies Clothes from the Fashion MNIST Data set using Artificial Neural Networks and Python.
Let’s start by importing the libraries we need for this task
import tensorflow as tf from tensorflow import keras import numpy as np import matplotlib.pyplot as plt
To load the data set
fashion = keras.datasets.fashion_mnist (trainImages, trainLabels), (testImages, testLabels) = fashion.load_data()
imgIndex = 0
img = trainImages[imgIndex]
print("Image Label :",trainLabels[imgIndex])
plt.imshow(img)#Output Image Label : 9 <matplotlib.image.AxesImage at 0x7f1111a06d68>
To print the shape of the training and testing data
print(trainImages.shape) print(testImages.shape)
#Output (60000, 28, 28) (10000, 28, 28)
Now let’s create a Neural Network
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)
])To Compile the Model
model.compile(optimizer = 'adam',
loss = 'sparse_categorical_crossentropy',
metrics=['accuracy'])To Train the model
model.fit(trainImages, trainLabels, epochs=5, batch_size=32)
#Output Epoch 1/5 1875/1875 [==============================] - 4s 2ms/step - loss: 3.6150 - accuracy: 0.6802 Epoch 2/5 1875/1875 [==============================] - 4s 2ms/step - loss: 0.7296 - accuracy: 0.7488 Epoch 3/5 1875/1875 [==============================] - 4s 2ms/step - loss: 0.6374 - accuracy: 0.7725 Epoch 4/5 1875/1875 [==============================] - 4s 2ms/step - loss: 0.5873 - accuracy: 0.7906 Epoch 5/5 1875/1875 [==============================] - 4s 2ms/step - loss: 0.5579 - accuracy: 0.7993 <tensorflow.python.keras.callbacks.History at 0x7f1108dc3588>
To Evaluate the Model
model.evaluate(testImages, testLabels)
#Output 313/313 [==============================] - 0s 1ms/step - loss: 0.5916 - accuracy: 0.7981 [0.5915989279747009, 0.7980999946594238]
To Make a Prediction
predictions = model.predict(testImages[0:5]) # Print the predicted labels print(predictions)
#Output [[1.74235439e-07 2.69071290e-08 6.66509115e-20 3.09463957e-07 1.11526007e-20 1.34603798e-01 8.10060641e-08 7.74199590e-02 3.87958280e-05 7.87936807e-01] [2.89689321e-02 1.06601091e-02 6.28736615e-01 2.77338717e-02 1.61624148e-01 1.49910515e-02 8.56256112e-02 1.23378839e-02 2.35275514e-02 5.79410419e-03] [6.75366528e-06 9.99993205e-01 4.27281517e-12 2.68350314e-10 8.65088672e-16 1.05001736e-14 1.33745196e-12 0.00000000e+00 1.84386378e-11 0.00000000e+00] [6.56618613e-06 9.99993443e-01 1.46741508e-11 1.80866895e-08 7.95811239e-14 1.56570215e-16 5.96713607e-12 0.00000000e+00 3.94146077e-10 0.00000000e+00] [2.19924763e-01 1.00887669e-02 1.99720263e-01 6.23517819e-02 4.97664846e-02 3.40277069e-07 4.30076748e-01 7.25772731e-09 2.80708820e-02 2.27675168e-09]]
To print the maximum labels
print(np.argmax(predictions, axis=1)) # Print the actual label values print(testLabels[0:5])
#Output [9 2 1 1 6] [9 2 1 1 6]
To Print the first 5 images
for i in range(0,5): plt.imshow(testImages[i], cmap='gray') plt.show()
