train_model

#!/usr/bin/env python
# coding: utf-8

# In[2]:


import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.utils.np_utils import to_categorical
from keras.layers import Dense
from keras.optimizers import Adam
from keras.backend import clear_session
import numpy

# Train model function

def train_model(neurons , model , epochs , test) : 
    print("\n" , "-------->Details------>>>>> " , "\n", "Iteration  : ", test , "\n" , "   Number of Neurons : ", neurons , "\n" , "   Number of Epochs : ",  epochs)
    model.add(Dense(units = neurons , input_dim = 28*28 , activation = 'relu'))
    model.add(Dense(units=200 , input_dim = 28*28 , activation = 'relu'))
    model.add(Dense(units=60 , input_dim = 28*28 , activation = 'relu'))
    model.add(Dense(units=10 , input_dim = 28*28 , activation = 'softmax'))
    model.compile( optimizer= "Adam" , loss='categorical_crossentropy', 
    metrics=['accuracy'] )
    return model


def validate(fit_model, epochs):
    i=0
    text = fit_model.history
    accuracy = text['accuracy'][epochs-1] * 100
    accuracy = int(accuracy)
    f= open("check.txt","w+")
    f.write(str(accuracy))
    f.close()
    print(" Accuracy in {}  Iteration is {} : ".format(test,accuracy) )
    return accuracy



# Load Model 
(train_X , train_y), (test_X , test_y) = mnist.load_data("mymnist.data")
# Reshape data and change type
test_X = test_X.reshape(-1 , 28*28)
train_X = train_X.reshape(-1 ,  28*28)
test_X = test_X.astype("float32")
train_X = train_X.astype("float32")
# One hot encoding 
test_y = to_categorical(test_y)
train_y = to_categorical(train_y)


#Initials
neurons = 10
accuracy = 0
epochs = 1
test = 1
flag = 0

while int(accuracy) < 92 :
    if flag == 1 :
        model = keras.backend.clear_session()
        neurons = neurons+15
        epochs = epochs+1 
        test = test + 1
    #model=reset_weights(model)
    model = Sequential()
    model = train_model(neurons , model , epochs , test)
    print("    -------------------------Wait for while.....calculating the accuracy-------------------------- ")
    fit_model = model.fit(train_X ,  train_y , epochs = epochs , verbose =  False)
    accuracy=validate(fit_model , epochs)
    flag = 1
if fit_model.history['accuracy'][0] >= .80:
    
   
    import smtplib, ssl
    import getpass
    port = 587  # For starttls
    smtp_server = "smtp.gmail.com"
    sender_email = "radhakumari.jbp@gmail.com"
    receiver_email = "1905101@kiit.ac.in"
    password =getpass.getpass('enter pass ->')
    message = """Subject:HELLO :)
YOUR MODEL HAS BEEN SUCCESSFULLY TRAINED..!!!
Your accuracy is {}""".format(accuracy)

    context = ssl.create_default_context()
    with smtplib.SMTP(smtp_server, port) as server:
        server.ehlo()  # Can be omitted
        server.starttls(context=context)
        server.ehlo()  # Can be omitted
        server.login(sender_email, password)
        server.sendmail(sender_email, receiver_email, message)


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