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cyclone project spy.py
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from keras.preprocessing.image import ImageDataGenerator, load_img
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
from sklearn import preprocessing
from keras.preprocessing.image import load_img, img_to_array
import pickle
from keras.models import load_model
from keras.preprocessing import image
import numpy as np
from os import listdir
from os.path import isfile, join
img_width = 150
img_height = 150
train_data_dir = 'C://Users//KIIT//Desktop//project cyclone detection//train'
validation_data_dir = 'C://Users//KIIT//Desktop//project cyclone detection//validation'
train_samples = 120
validation_samples = 30
epochs = 5
batch_size = 20
# Check for TensorFlow or Thieno
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
import keras
model.compile(loss='binary_crossentropy',optimizer=keras.optimizers.Adam(lr=.0001),metrics=['accuracy'])
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
imgs, labels = next(train_generator)
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
history = model.fit_generator(
train_generator,
steps_per_epoch=train_samples,
epochs=epochs,
validation_data=validation_generator,
validation_steps=validation_samples)
from keras.preprocessing.image import load_img, img_to_array
#predicting a cyclonic image.....if 1 , cyclone(true)
model.predict(np.array([image.img_to_array(load_img('C://Users//KIIT//Desktop//project cyclone detection//test//1001.jpg').resize((150,150)))/255
]))
model.predict(np.array([image.img_to_array(load_img('C://Users//KIIT//Desktop//project cyclone detection//train//NON-CYCLONE//images (13).jfif').resize((150,150)))/255
]))
pickle.dump(model, open('model.pkl','wb'))
# Loading model to compare the results
model = pickle.load(open('model.pkl','rb'))