Image-classification-using-python

Finding given image is cat or non cat using nn in pyhton.

Deep Neural Network for Image Classification: Application

1 - Packages Let's first import all the packages that you will need during this assignment.

numpy is the fundamental package for scientific computing with Python. matplotlib is a library to plot graphs in Python. h5py is a common package to interact with a dataset that is stored on an H5 file. PIL and scipy are used here to test your model with your own picture at the end. dnn_app_utils provides the functions implemented in the "Building your Deep Neural Network: Step by Step" assignment to this notebook. np.random.seed(1) is used to keep all the random function calls consistent. It will help us grade your work.

2 - Dataset

Problem Statement: You are given a dataset ("data.h5") containing:

• a training set of m_train images labelled as cat (1) or non-cat (0)
• a test set of m_test images labelled as cat and non-cat
• each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB).

3 - General methodology As usual you will follow the Deep Learning methodology to build the model:

1. Initialize parameters / Define hyperparameters
2. Loop for num_iterations: a. Forward propagation b. Compute cost function c. Backward propagation d. Update parameters (using parameters, and grads from backprop)
3. Use trained parameters to predict labels

4 - Two-layer neural network Use the helper functions you have implemented in the previous assignment to build a 2-layer neural network with the following structure: LINEAR -> RELU -> LINEAR -> SIGMOID. The functions you may need and their inputs are:

def initialize_parameters(n_x, n_h, n_y): ... return parameters def linear_activation_forward(A_prev, W, b, activation): ... return A, cache def compute_cost(AL, Y): ... return cost def linear_activation_backward(dA, cache, activation): ... return dA_prev, dW, db def update_parameters(parameters, grads, learning_rate): ... return parameters

5 - L-layer Neural Network Question: Use the helper functions you have implemented previously to build an $L$-layer neural network with the following structure: [LINEAR -> RELU] x (L-1) -> LINEAR -> SIGMOID. The functions you may need and their inputs are:

def initialize_parameters_deep(layer_dims): ... return parameters def L_model_forward(X, parameters): ... return AL, caches def compute_cost(AL, Y): ... return cost def L_model_backward(AL, Y, caches): ... return grads def update_parameters(parameters, grads, learning_rate): ... return parameters

6. Results Analysis

7)You can upload your images and check the output .