shubham0204

private lateinit var activityMainBinding : ActivityMainBinding
private lateinit var progressDialog : ProgressDialog

override fun onCreate(savedInstanceState: Bundle?) {
    super.onCreate(savedInstanceState)
    activityMainBinding = ActivityMainBinding.inflate( layoutInflater )
    setContentView( activityMainBinding.root )

    progressDialog = ProgressDialog( this )

shubham0204

from flask import Flask , jsonify , request
from PIL import Image
import tensorflow as tf
import numpy as np
import base64
import io

# Loading the Keras model to perform inference
# Download the model from this release ->
# https://github.com/shubham0204/Age-Gender_Estimation_TF-Android/releases/tag/v1.0

shubham0204

import math
import matplotlib.pyplot as plt

def poisson( lambda_ , x ):
    return ( (lambda_)**x * math.exp(-lambda_) ) / math.factorial(x)
  
x = []
y = []
for i in range( 100 ):
    x.append( i )

shubham0204

Blogs On Medium

• On-Device Machine Learning In Android: Frameworks and Ecosystem
• Using C/C++ in Android: A Comprehensive Guide For Beginners
• Building A Cross-Platform TFIDF Text Summarizer In Rust
• Managing Deep Learning Models Easily With TOML Configurations
• glove-android: Using GloVe Word Embeddings for NLP In Android
• Chaquopy: Using Python In Android Apps
• [Using Websockets To Run Termin

shubham0204

hidden_dims = 128
token_mixing_mlp_dims = 64
channel_mixing_mlp_dims = 128
patch_size = 9
num_classes = 10
num_mixer_layers = 4
input_image_shape = ( 32 , 32 , 3 )

inputs = tf.keras.layers.Input( shape=input_image_shape )

shubham0204

hidden_dims = 128
token_mixing_mlp_dims = 64
channel_mixing_mlp_dims = 128
patch_size = 9
num_classes = 10
num_mixer_layers = 4
reshape_image_dim = 72
input_image_shape = ( 32 , 32 , 3 )

inputs = tf.keras.layers.Input( shape=input_image_shape )

shubham0204

# Mixer layer consisting of token mixing MLPs and channel mixing MLPs
# input shape -> ( batch_size , channels , num_patches )
# output shape -> ( batch_size , channels , num_patches )
def mixer( x , token_mixing_mlp_dims , channel_mixing_mlp_dims ):
    # inputs x of are of shape ( batch_size , num_patches , channels )
    # Note: "channels" is used instead of "embedding_dims"
    
    # Add token mixing MLPs
    token_mixing_out = token_mixing( x , token_mixing_mlp_dims )
    # Shape of token_mixing_out -> ( batch_size , channels , num_patches )

shubham0204

# Channel Mixing MLPs : Allow communication within channels ( features of embeddings )
def channel_mixing( x , channel_mixing_mlp_dims ):
    # x is a tensor of shape ( batch_size , num_patches , channels )
    x = tf.keras.layers.LayerNormalization( epsilon=1e-6 )( x )
    x = mlp( x , channel_mixing_mlp_dims )
    return x

shubham0204

# Token Mixing MLPs : Allow communication within patches.
def token_mixing( x , token_mixing_mlp_dims ):
    # x is a tensor of shape ( batch_size , num_patches , channels )
    x = tf.keras.layers.LayerNormalization( epsilon=1e-6 )( x )
    x = tf.keras.layers.Permute( dims=[ 2 , 1 ] )( x ) 
    # After transposition, shape of x -> ( batch_size , channels , num_patches )
    x = mlp( x , token_mixing_mlp_dims )
    return x

shubham0204

# Multilayer Perceptron with GeLU ( Gaussian Linear Units ) activation
def mlp( x , hidden_dims ):
    y = tf.keras.layers.Dense( hidden_dims )( x )
    y = tf.nn.gelu( y )
    y = tf.keras.layers.Dense( x.shape[ -1 ] )( y )
    y = tf.keras.layers.Dropout( 0.4 )( y )
    return y