This project classifies lothes using Deep Learning and Tensorflow 2.0.
Fashion training set consists of 70,000 images divided into 60,000 training and 10,000 testing samples. Dataset sample consists of 28x28 grayscale image, associated with a label from 10 classes.
The 10 classes are as follows:
0 => T-shirt/top 1 => Trouser 2 => Pullover 3 => Dress 4 => Coat 5 => Sandal 6 => Shirt 7 => Sneaker 8 => Bag 9 => Ankle boot
Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255.
!pip install tensorflow-gpu==2.0.0.alpha0
import tensorflow as tf
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
You will need to mount your drive using the following commands: For more information regarding mounting, please check this out here.
from google.colab import drive
drive.mount('/content/drive')
Upload the data file from Kaggle to your Google drive and then access it
The dataframes for both training and testing datasets
fashion_train_df = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Fashion Dataset/fashion-mnist_train.csv',sep=',')
fashion_test_df = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Fashion Dataset/fashion-mnist_test.csv', sep = ',')
Alternatively, you can use the same dataset made readily available by keras Using the following lines of code:
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()
Get more information about your dataset
diabetes.info()
diabetes.describe()
diabetes.head(10)
diabetes.tail(10)
See more steps in the colab.
See steps in the colab.
See steps in the colab.
See steps in the colab.
If you are not satisfied with the results, then you can increase the number of independent variables and retrain the same model. See steps in the colab.