zaidalyafeai

model.save('saved_model/keras.h5')

zaidalyafeai

print(model.predict_proba(X))

zaidalyafeai

model.fit(X, y, batch_size=1, nb_epoch=1000, verbose= 0)

zaidalyafeai

model = Sequential()
model.add(Dense(8, input_dim=2))
model.add(Activation('tanh'))
model.add(Dense(1))
model.add(Activation('sigmoid'))

sgd = SGD(lr=0.1)
model.compile(loss='binary_crossentropy', optimizer=sgd)

zaidalyafeai

X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([[0],[1],[1],[0]])

zaidalyafeai

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np 

zaidalyafeai

import os
import glob
import numpy as np
from tensorflow.keras import layers
from tensorflow import keras 
import tensorflow as tf

zaidalyafeai

//the minimum boudning box around the current drawing
const mbb = getMinBox()
//cacluate the dpi of the current window 
const dpi = window.devicePixelRatio
//extract the image data 
const imgData = canvas.contextContainer.getImageData(mbb.min.x * dpi, mbb.min.y * dpi,
						       (mbb.max.x - mbb.min.x) * dpi, (mbb.max.y - mbb.min.y) * dpi);

zaidalyafeai

const pred = model.predict(preprocess(imgData)).dataSync()

zaidalyafeai

model.save('keras.h5')