pranjul_itondia2ndyr

#as i m working on colab ,so i uploaded the folder of images in colab in zipped format..so unzip it

!unzip image_data1.zip
  
!pip install --upgrade Keras   #upgrading keras to latest version
!pip3 list | grep -i keras     
!python -c "import keras"
  
from tensorflow.keras.models import Sequential     #importing sequntial class
import tensorflow.compat.v1 as tf                  #upgrading tensorflow version to v2
tf.disable_v2_behavior()
  
  
from tensorflow.keras.layers import Dense,Dropout,Activation, Flatten, Convolution2D, MaxPooling2D
classifier=Sequential()      #initializing of the cnn model           
 
classifier.add(Convolution2D(32,3,3,input_shape=(64,64,3),activation = "relu"))  #first convulational layer
classifier.add(MaxPooling2D(pool_size=(2,2)))
  
classifier.add(Convolution2D(32,3,3,activation='relu'))              #second convolutional layer
classifier.add(MaxPooling2D(pool_size=(2,2)))                        #pool it
  
classifier.add(Flatten())                              #flatten it toa one col array 
classifier.add(Dense(128,activation='relu'))            
classifier.add(Dense(1,activation='sigmoid'))
  
  
classifier.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
  
from keras.preprocessing.image import ImageDataGenerator
train_datagen=ImageDataGenerator(rescale=1./255,shear_range=0.2,zoom_range=0.2,horizontal_flip=True)
test_datagen=ImageDataGenerator(rescale=1./255)
training_set=train_datagen.flow_from_directory('image_data/training_set',target_size=(64,64),batch_size=32,class_mode='binary')
test_set=test_datagen.flow_from_directory('image_data/test_set',target_size=(64,64),batch_size=32,class_mode='binary')
classifier.fit_generator(training_set,steps_per_epoch=600,epochs=25,validation_data=test_set,validation_steps=94)
 #code for testing the image 
import numpy as np
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array      
from keras.preprocessing.image import array_to_img
# loading the image
test_image = load_img('random3.jpg',target_size=(64,64))
   
test_image = img_to_array(test_image)
print(test_image.dtype)
print(test_image.shape)
  
test_image=np.expand_dims(test_image,axis=0)
result=classifier.predict(test_image)
training_set.class_indices
if result[0][0] >=0.7:
   prediction='fire'
else:
   prediction='safe'
print(prediction)

#for uploading dataset from drive to colab
!pip install -U -q PyDrive
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials

auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)

downloaded = drive.CreateFile({'id':'1-jsrUfAt06Gk94EQrid1qNJq474xlPBW'}) # replace the id with id of file
downloaded.GetContentFile('data1.csv')

#first import the required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

#now read the csv file by using pandas
dataset=pd.read_csv("data1.csv",error_bad_lines=False)
x=dataset.iloc[:,:3].values
y=dataset.iloc[:,3].values

dataset.head(5)
#for the month column ,apply encoding...label and one hot encoding
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
x[:, 0] = labelencoder.fit_transform(x[:, 0])

print(x)
print(y)

from sklearn.preprocessing import OneHotEncoder
onehotencoder = OneHotEncoder(categorical_features = [0])
x = onehotencoder.fit_transform(x).toarray()

print(x)
#split the dataset in 75% and 25%
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25,random_state=0)
#apply random forest classifier
from sklearn.ensemble import RandomForestClassifier
clf=RandomForestClassifier()
clf.fit(x_train,y_train)

y_pred=classifier.predict(x_test)  #predicting the test sets

from sklearn.metrics import confusion_matrix   #confusion matrix for accuracy
cm4=confusion_matrix(y_test, y_pred)

print(cm4)

y_prob=classifier.predict_proba(x_test)   #to get the probablity of fire
print(y_prob)