Building and Deploying Models of Computer Vision

Authors

• @Akash Kumar Jha

Deployment

1. Data Extraction
2. Analyze the Data
3. Data Preprocessing.
4. Building CNN Architecture
5. Train the Model
6. Model Evaluation on the Test Set
7. Adding Dropout into the Network
8. Final loss and accuracy

Installation

To install the libraries used in this project. Follow the below steps:

%tensorflow_version 2.x
import tensorflow
tensorflow.__version__

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt # plotting library
%matplotlib inline
from keras.models import Sequential
import tensorflow as tf
from tensorflow.keras.optimizers import Adam # - Works ,RMSprop
from tensorflow.keras.utils import to_categorical, plot_model
from keras import  backend as K
from tensorflow.keras.datasets import mnist
from keras.layers import Dense
from keras.layers.convolutional import Conv2D, MaxPooling2D
# from keras.models import Sequential
from keras.layers import Activation, Flatten, Dropout
from tensorflow.keras.layers import BatchNormalization
from keras.layers.advanced_activations import LeakyReLU
from keras.datasets import fashion_mnist
import random
random.seed(0)

# Ignore the warnings
import warnings
warnings.filterwarnings("ignore")

Running Flask Api

To run tests, run the following command

  python app.py

🚀 About Me

Data Scientist Enthusiast | Petroleum Engineer Graduate | Solving Problems Using Data

Hi, I'm Akash! 👋

🔗 Links

Tech Stack

Other Me

👩‍💻 I’m interested in Petroleum Engineering

🧠 I’m currently learning Data Scientist | Data Analytics | Business Analytics

👯‍♀️ I’m looking to collaborate on Ideas & Data

🛠 Skills

1. Data Scientist
2. Data Analyst
3. Business Analyst
4. Machine Learning

Future Plans

⚡️ Looking forward to help drive innovations into your company as a Data Scientist

⚡️ Looking forward to offer more than I take and leave the place better than i found