![]() For instance, you can access m_train by writing train_set_x_orig.shape. Remember that train_set_x_orig is a numpy-array of shape (m_train, num_px, num_px, 3). # Example of a picture index = 53 plt.imshow(train_set_x_orig) print (“y = “ + str(train_set_y) + “, it’s a ‘“ + classes)].decode(“utf-8”) + “‘ picture.”) Feel free also to change the index value and re-run to see other images. You can visualize an example by running the following code. ![]() ![]() After preprocessing, we will end up with train_set_x and test_set_x (the labels train_set_y and test_set_y don’t need any preprocessing).Įach line of your train_set_x_orig and test_set_x_orig is an array representing an image. I have added “_orig” at the end of image datasets (train and test) because i am going to preprocess them. # Loading the data (cat/non-cat) train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset() You will build a simple image-recognition algorithm that can correctly classify pictures as cat or non-cat. Thus, each image is square (height = num_px) and (width = num_px). You are given a dataset (“data.h5”) containing: - a training set of m_train images labeled as cat (y=1) or non-cat (y=0) - a test set of m_test images labeled as cat or non-cat - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB). %matplotlib inline Step 3: Loading the dataset Import numpy as np import matplotlib.pyplot as plt import h5py import scipy from PIL import Image from scipy import ndimage from lr_utils import load_dataset Step 2: I mport Required dependencies/libraries/packages : Тhen it transmits its data to all the nodes it is connected to.Ĭlick on the link below to visit colab and click on File, then New Python 3 Notebook. Each individual node performs a simple mathematical calculation. Data comes from the input layer to the output layer along with these compounds. ![]() ![]() Neurons in each layer are connected to neurons of the next layer. Neurons are located in a series of groups - layers (see figure below). Neural networks consist of individual units called neurons. This concept arose in an attempt to simulate the processes occurring in the brain by Warren McCulloch and Walter Pitts in 1943. It is a machine learning algorithm, which is built on the principle of the organization and functioning of biological neural networks. Let’s see a bit of theory about Neural networks and Logistic regression Neural Networks This cat classifier takes an image as an input and then based on regression techniques, it predicts whether the image contains a cat or not with 70% accuracy and the tools used will be Jupyter Notebook and the code is written in python. In this blog, we will be covering up the concepts of using the logistic regression along with neural networks, applying forward and backward propogation and then applying them to the practice in order to build your image recognition system i.e a cat classifier in this case. Building a Logistic regression Using Neural Networks: Cat vs Non-Cat Image Classification ![]()
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