DataBloom

Hi I recently created a small image binary classification project and was wondering how I could deploy it to the web.

I’ve been able to convert the model to .json but I don’t know what to do next.

I’m trying to make it so that the user can upload an image or provide a image link and then the model preprocesses the image to the correct size and then uses the model to predict.

Anyone know how to do this?

I’m currently learning HTML, CSS and JavaScript (still at an introductory level) and I am looking to explore machine learning via tensorflow.js. I was wondering if the following web app idea could be built using tensorflow.js or if anyone has any recommendations for frameworks that I could learn to achieve something like this?

Idea: – similar to Akinator (guessing a celebrity based off asking questions) but only for sports players – first ask the user to input as much information as possible into a text area box – not sure if this is possible, but can tensorflow.js scan for key words that has been inputted in the text area? – then if the web app needs to ask more questions (to come to a guess), they will ask some more (and use information from the text area + from different userInput to guide the questions asked) – then output a guess! – then this info can get stored to improve guessing for the next user

For example: – user inputs “Basketball player at Los Angeles laker. Tall. Number 23” into text area – AI can then ask “was your player previously at Miami Heat.” Etc etc – AI outputs guess: Lebron James – AI stores this information for next user

Very new to computer science so I’m not sure what avenue I would take to create something like this. But any suggestions regarding how I should approach this/what I should learn would be appreciated!

When tensorflow has tf.image.* for image augmentation, why should we use ImageDataGenerator?
I feel, tf.image.* is easier, But many tutorials, ImageDataGenerator has been used.

Please someone clarify this.

Thanks

Hey, I have trained multi out model in colab the model branch one will classify an image using passion loss the other branch will segment using binary crossentropy loss and using dicecoef as accuracy , when trained on colab the model gives good result accuracy:99,dicecoef:90

however when trained on my local machine one of two usually accurcuy randomly go to zero , nothing has changed used same code , same data only difference is I used tensorflow 2.5 while on colab it was 2.6.

I appoliges for the dirty codeing.

import os

os.environ[‘TF_CPP_MIN_LOG_LEVEL’] = ‘3’ # or any {‘0’, ‘1’, ‘2’}

import tensorflow as tf

import random

import numpy as np

import matplotlib.pyplot as plt

from tqdm import tqdm

from skimage.io import imread , imshow

from skimage.transform import resize

# example of pixel normalization

from numpy import asarray

# load image

import shutil

#import cv2 as cv

​

import tensorflow.keras.backend as K

from tensorflow.keras.losses import binary_crossentropy

​

beta = 0.25

alpha = 0.25

gamma = 2

epsilon = 1e-5

smooth = 1

​

​

class Semantic_loss_functions(object):

def __init__(self):

print (“semantic loss functions initialized”)

​

def dice_coef(self, y_true, y_pred):

y_true_f = K.flatten(y_true)

y_pred_f = K.flatten(y_pred)

intersection = K.sum(y_true_f * y_pred_f)

return (2. * intersection + K.epsilon()) / (

K.sum(y_true_f) + K.sum(y_pred_f) + K.epsilon())

​

def sensitivity(self, y_true, y_pred):

true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))

possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))

return true_positives / (possible_positives + K.epsilon())

​

def specificity(self, y_true, y_pred):

true_negatives = K.sum(

K.round(K.clip((1 – y_true) * (1 – y_pred), 0, 1)))

possible_negatives = K.sum(K.round(K.clip(1 – y_true, 0, 1)))

return true_negatives / (possible_negatives + K.epsilon())

​

def convert_to_logits(self, y_pred):

y_pred = tf.clip_by_value(y_pred, tf.keras.backend.epsilon(),

1 – tf.keras.backend.epsilon())

return tf.math.log(y_pred / (1 – y_pred))

​

def weighted_cross_entropyloss(self, y_true, y_pred):

y_pred = self.convert_to_logits(y_pred)

pos_weight = beta / (1 – beta)

loss = tf.nn.weighted_cross_entropy_with_logits(logits=y_pred,

targets=y_true,

pos_weight=pos_weight)

return tf.reduce_mean(loss)

​

def focal_loss_with_logits(self, logits, targets, alpha, gamma, y_pred):

weight_a = alpha * (1 – y_pred) ** gamma * targets

weight_b = (1 – alpha) * y_pred ** gamma * (1 – targets)

​

return (tf.math.log1p(tf.exp(-tf.abs(logits))) + tf.nn.relu(

-logits)) * (weight_a + weight_b) + logits * weight_b

​

def focal_loss(self, y_true, y_pred):

y_pred = tf.clip_by_value(y_pred, tf.keras.backend.epsilon(),

1 – tf.keras.backend.epsilon())

logits = tf.math.log(y_pred / (1 – y_pred))

​

loss = self.focal_loss_with_logits(logits=logits, targets=y_true,

alpha=alpha, gamma=gamma, y_pred=y_pred)

​

return tf.reduce_mean(loss)

​

def depth_softmax(self, matrix):

sigmoid = lambda x: 1 / (1 + K.exp(-x))

sigmoided_matrix = sigmoid(matrix)

softmax_matrix = sigmoided_matrix / K.sum(sigmoided_matrix, axis=0)

return softmax_matrix

​

def generalized_dice_coefficient(self, y_true, y_pred):

smooth = 1.

y_true_f = K.flatten(y_true)

y_pred_f = K.flatten(y_pred)

intersection = K.sum(y_true_f * y_pred_f)

score = (2. * intersection + smooth) / (

K.sum(y_true_f) + K.sum(y_pred_f) + smooth)

return score

​

def dice_loss(self, y_true, y_pred):

loss = 1 – self.generalized_dice_coefficient(y_true, y_pred)

return loss

​

def bce_dice_loss(self, y_true, y_pred):

loss = binary_crossentropy(y_true, y_pred) +

self.dice_loss(y_true, y_pred)

return loss / 2.0

​

def confusion(self, y_true, y_pred):

smooth = 1

y_pred_pos = K.clip(y_pred, 0, 1)

y_pred_neg = 1 – y_pred_pos

y_pos = K.clip(y_true, 0, 1)

y_neg = 1 – y_pos

tp = K.sum(y_pos * y_pred_pos)

fp = K.sum(y_neg * y_pred_pos)

fn = K.sum(y_pos * y_pred_neg)

prec = (tp + smooth) / (tp + fp + smooth)

recall = (tp + smooth) / (tp + fn + smooth)

return prec, recall

​

def true_positive(self, y_true, y_pred):

smooth = 1

y_pred_pos = K.round(K.clip(y_pred, 0, 1))

y_pos = K.round(K.clip(y_true, 0, 1))

tp = (K.sum(y_pos * y_pred_pos) + smooth) / (K.sum(y_pos) + smooth)

return tp

​

def true_negative(self, y_true, y_pred):

smooth = 1

y_pred_pos = K.round(K.clip(y_pred, 0, 1))

y_pred_neg = 1 – y_pred_pos

y_pos = K.round(K.clip(y_true, 0, 1))

y_neg = 1 – y_pos

tn = (K.sum(y_neg * y_pred_neg) + smooth) / (K.sum(y_neg) + smooth)

return tn

​

def tversky_index(self, y_true, y_pred):

y_true_pos = K.flatten(y_true)

y_pred_pos = K.flatten(y_pred)

true_pos = K.sum(y_true_pos * y_pred_pos)

false_neg = K.sum(y_true_pos * (1 – y_pred_pos))

false_pos = K.sum((1 – y_true_pos) * y_pred_pos)

alpha = 0.7

return (true_pos + smooth) / (true_pos + alpha * false_neg + (

1 – alpha) * false_pos + smooth)

​

def tversky_loss(self, y_true, y_pred):

return 1 – self.tversky_index(y_true, y_pred)

​

def focal_tversky(self, y_true, y_pred):

pt_1 = self.tversky_index(y_true, y_pred)

gamma = 0.75

return K.pow((1 – pt_1), gamma)

​

def log_cosh_dice_loss(self, y_true, y_pred):

x = self.dice_loss(y_true, y_pred)

return tf.math.log((tf.exp(x) + tf.exp(-x)) / 2.0)

########

n_filters=50

epochs=50

batch_size=6

Img_wedth =128

Img_height = 128

Img_channels = 1

​

​

​

#####################################

import h5py

paths=os.listdir(‘C:/Users/ASUS/Desktop/imageData/’)

## save images in arrays ##

X_train = np.zeros((len(paths) , Img_height , Img_wedth , Img_channels) , dtype = np.float32)

y_train = np.zeros((len(paths) , Img_height , Img_wedth , Img_channels ) , dtype = np.float32)

y_train_label=[]

#####################################

​

for n, id_ in tqdm(enumerate(paths) , total = len(paths)) :

ttt=[0,0,0]

path = ‘C:/Users/ASUS/Desktop/imageData/’+id_

#print(‘path.. ‘,path)

#print(‘id.. ‘,id_)

#img = imread(path + ‘/image/’ +id_ + ‘.png’) [: , : ]

img1=h5py.File(path,’r’)

# print(‘path.. ‘,path)

img=img1[‘cjdata’][‘image’]

img = resize(img , (Img_height , Img_wedth , Img_channels) , mode = ‘constant’ , preserve_range=True)

#print(img.shape)

# imshow(img,cmap=”gray”)

# plt.show()

​

img = asarray(img)

img = img.astype(‘float32’)

# normalize to the range 0-1

img = tf.image.per_image_standardization(img)

# print(img[55][55])

#print(img.shape)

X_train[n] = img

# print(‘path.. ‘,path)

mask=img1[‘cjdata’][‘tumorMask’]

mask = asarray(mask)

mask = mask.astype(‘float32’)

#_,mask=cv.threshold(mask, 0.01, 1, 0 )

mask = resize(mask , (Img_height , Img_wedth , Img_channels) , mode = ‘constant’ , preserve_range=True)

#print(img.shape)

# imshow(img,cmap=”gray”)

# plt.show()

# normalize to the range 0-1

ttt[int(img1[‘cjdata’][‘label’][0][0])-1]=img1[‘cjdata’][‘label’][0][0]

y_train_label.append(ttt)

y_train[n] = mask

# print(Mask_[55][55])

# Mask_ = np.expand_dims(resize(Mask_ , (Img_height , Img_wedth) , mode = ‘constant’ ,

# preserve_range = True) , axis = -1)

# Mask = np.maximum(Mask , Mask_ )

​

# Mask = np.zeros((Img_height , Img_wedth , 1) , dtype = np.bool)

​

​

y_train_label=np.array(y_train_label)

paths_test=os.listdir(‘C:/Users/ASUS/Desktop/test’)[10:20]

​

X_test = np.zeros((len(paths_test) , Img_height , Img_wedth , Img_channels ) , dtype = np.float32)

y_test = np.zeros((len(paths_test) , Img_height , Img_wedth , Img_channels ) , dtype = np.float32)

y_test_label= []

print(‘resizing test images’)

for n, id_ in tqdm(enumerate(paths_test) , total = len(paths_test)) :

path = ‘C:/Users/ASUS/Desktop/test/’+id_

ttt1=[0,0,0]

img1=h5py.File(path,’r’)

# print(‘path.. ‘,path)

img=img1[‘cjdata’][‘image’]

img = resize(img , (Img_height , Img_wedth , Img_channels) , mode = ‘constant’ , preserve_range=True)

#print(img.shape)

# imshow(img,cmap=”gray”)

# plt.show()

img = asarray(img)

img = img.astype(‘float32’)

img = tf.image.per_image_standardization(img)

​

# normalize to the range 0-1

X_test[n]=img

mask=h5py.File(path,’r’)

# print(‘path.. ‘,path)

mask1=mask[‘cjdata’][‘tumorMask’]

mask1 = asarray(mask1)

mask1 = mask1.astype(‘float32’)

# _,mask1=cv.threshold(mask1, 0.01, 1, 0 )

​

mask1 = resize(mask1 , (Img_height , Img_wedth , Img_channels) , mode = ‘constant’ , preserve_range=True)

#print(img.shape)

# imshow(img,cmap=”gray”)

# plt.show()

# normalize to the range 0-1

ttt1[int(img1[‘cjdata’][‘label’][0][0])-1]=img1[‘cjdata’][‘label’][0][0]

y_test_label.append(ttt1)

​

y_test[n]=mask1

#print(‘imagetestid..’,n,’imagetestname’,id_)

#imshow(img)

#plt.show()

​

​

​

#image_x = random.randint(0 , len(Train_ids))

#imshow(X_train[image_x])

#plt.show()

#imshow(np.squeeze( y_train[image_x]))

#plt.show()

​

## U-net Moudel

inputs = tf.keras.Input((Img_wedth , Img_height , Img_channels ))

​

​

c1 = tf.keras.layers.Conv2D(n_filters*1 , (3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(inputs)

c1 = tf.keras.layers.Dropout(0.1 )(c1)

p1 = tf.keras.layers.MaxPooling2D((2,2 ))(c1)

print (‘Done_c1’)

​

c2 = tf.keras.layers.Conv2D(n_filters*2 , (3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(p1)

c2 = tf.keras.layers.Dropout(0.1 )(c2)

​

p2 = tf.keras.layers.MaxPooling2D((2,2 ))(c2)

print (‘Done_c2’)

​

c3 = tf.keras.layers.Conv2D(n_filters*4 , (3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(p2)

c3 = tf.keras.layers.Dropout(0.2 )(c3)

​

p3 = tf.keras.layers.MaxPooling2D((2,2 ))(c3)

print (‘Done_c3’)

​

c4 = tf.keras.layers.Conv2D(n_filters*8 , (3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(p3)

c4 = tf.keras.layers.Dropout(0.2 )(c4)

​

p4 = tf.keras.layers.MaxPooling2D(pool_size=((2,2 )))(c4)

print (‘Done_c4’)

​

c5 = tf.keras.layers.Conv2D(n_filters*16 , (3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(p4)

c5 = tf.keras.layers.Dropout(0.3 )(c5)

c5 = tf.keras.layers.Conv2D(n_filters*16, (3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(c5)

print (‘Done_c5’)

F1=tf.keras.layers.Flatten()(c5)

D1=tf.keras.layers.Dense(32,activation=’relu’)(F1)

D2=tf.keras.layers.Dense(3,activation=’softmax’,name=’clas’)(D1)

​

u6 = tf.keras.layers.Conv2DTranspose(n_filters*8 , (2,2) , strides=(2,2) , padding=’same’)(c5)

print (‘Done_c61’)

u6 = tf.keras.layers.Concatenate(axis=-1)([u6 , c4])

print (‘Done_c62’)

c6 = tf.keras.layers.Conv2D(n_filters*8 ,(3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(u6)

print (‘Done_c63’)

c6 = tf.keras.layers.Dropout(0.2 )(c6)

c6 = tf.keras.layers.Conv2D(n_filters*8, (3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(c6)

print (‘Done_c6’)

​

u7 = tf.keras.layers.Conv2DTranspose(n_filters*4 , (2,2) , strides=(2,2) , padding=’same’)(c6)

u7 = tf.keras.layers.Concatenate(axis=-1)([u7 , c3])

c7 = tf.keras.layers.Conv2D(n_filters*4 , (3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(u7)

c7 = tf.keras.layers.Dropout(0.2 )(c7)

c7 = tf.keras.layers.Conv2D(n_filters*4 , (3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(c7)

print (‘Done_c7’)

​

u8 = tf.keras.layers.Conv2DTranspose(n_filters*2 , (2,2) , strides=(2,2) , padding=’same’)(c7)

u8 = tf.keras.layers.Concatenate(axis=-1)([u8 , c2])

c8 = tf.keras.layers.Conv2D(n_filters*2 , (3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(u8)

c8 = tf.keras.layers.Dropout(0.2)(c8)

c8 = tf.keras.layers.Conv2D(n_filters*2 , (3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(c8)

print (‘Done_c8’)

​

u9 = tf.keras.layers.Conv2DTranspose(n_filters*1 , (2,2) , strides=(2,2) , padding=’same’)(c8)

print (‘Done_c91’)

u9 = tf.keras.layers.Concatenate(axis=-1)([u9 , c1])

print (‘Done_c92’)

c9 = tf.keras.layers.Conv2D(n_filters*1 , (3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(u9)

print (‘Done_c93’)

c9 = tf.keras.layers.Dropout(0.4)(c9)

print (‘Done_c94’)

c9 = tf.keras.layers.Conv2D(n_filters*1 , (3,3) , activation=’relu’

, kernel_initializer=’he_normal’ , padding=’same’)(c9)

print (‘Done_c9’)

​

outputs = tf.keras.layers.Conv2D(1 , (1,1) , activation=’sigmoid’,name=’seg’)(c9)

print (‘Done_out’)

model = tf.keras.Model(inputs=[inputs] , outputs=[outputs,D2])

print (‘Done_model1’)

​

​

​

​

​

​

def dice_coef1(y_true, y_pred, smooth=1):

print(y_true.shape,y_pred.shape)

intersection = K.sum((y_true )*( tf.round(y_pred)), axis=[1,2,3])

union = K.sum((y_true), axis=[1,2,3]) + K.sum((tf.round(y_pred)), axis=[1,2,3])

dice = K.mean((2. * intersection + smooth)/(union + smooth), axis=0)

return dice

​

​

​

​

loss={“seg”:binary_crossentropy,

“clas”:tf.keras.losses.poisson}

metricss={‘seg’:dice_coef1,

‘clas’:’Accuracy’}

opt=tf.keras.optimizers.Adam(clipvalue=1,clipnorm=1,lr=0.0001)

s = Semantic_loss_functions()

model.compile(optimizer=opt, loss=loss, metrics=metricss)

model.summary()

​

#############

# modelchecpoints

​

from tensorflow.keras import callbacks

​

​

​

results = model.fit(X_train , [y_train,y_train_label] ,shuffle=True, batch_size =batch_size, epochs=epochs )

So I have started to try my hand at tensor flow to learn how it works. While going through the steps I came across an error that I have not seen before. I can’t seem to figure out what is going on. Any help is appreciated

Traceback (most recent call last):

File “generate_tfrecord.py”, line 27, in <module>

from object_detection.utils import dataset_util, label_map_util

File “C:UsersnathaOneDriveDesktopProjectRealTimeObjectDetection-mainTensorflowscriptsobject_detectionutilslabel_map_util.py”, line 59, in <module>

label_map = label_map_util.load_labelmap(args.labels_path)

AttributeError: partially initialized module ‘object_detection.utils.label_map_util’ has no attribute ‘load_labelmap’ (most likely due to a circular import)