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chest_xray_prediction.py
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chest_xray_prediction.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 keras.preprocessing import image
from keras.applications.mobilenet import preprocess_input
from keras.models import Model
from keras.optimizers import Adam
import os
import pandas as pd
import numpy as np
import keras
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
# %matplotlib inline
import os
import zipfile
for file in os.listdir():
zip_ref = zipfile.ZipFile('chest-xray-pneumonia.zip', 'r')
zip_ref.extractall()
zip_ref.close()
#giving path of the dataset
print(os.listdir("chest_xray"))
print(os.listdir("chest_xray/train"))
print(os.listdir("chest_xray/test"))
#setting image width and height to give input to the CNN
img_width, img_height = 150,150
#creating testing and training data
train_data_dir = 'chest_xray/train'
validation_data_dir = 'chest_xray/val'
test_data_dir = 'chest_xray/test'
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'))
model.layers
#values of the data are defined
training_samples = 5216
validation_samples = 16
epochs = 10
batch_size = 16
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# this is the augmentation configuration we will use for training
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')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
test_generator = test_datagen.flow_from_directory(
test_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=training_samples//validation_samples,
epochs=epochs,
validation_data=validation_generator,
validation_steps=validation_samples//batch_size)
scores = model.evaluate_generator(validation_generator,steps=1)
print("\n%s: %.2f%%" % (model.metrics_names[0], scores[1]*100))
scores = model.evaluate_generator(train_generator,steps=1)
print("\n%s: %.2f%%" % (model.metrics_names[0], scores[1]*100))
scores = model.evaluate_generator(test_generator,steps=1)
print("\n%s: %.2f%%" % (model.metrics_names[0], scores[1]*100))