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tweak.py
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#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import os
from os.path import join
import numpy as np
import pandas as pd
import random
import glob
import matplotlib.image as mpimg
from mpl_toolkits.axes_grid1 import ImageGrid
from sklearn .preprocessing import LabelBinarizer
from sklearn.metrics import classification_report,confusion_matrix
import matplotlib.pyplot as plt
# Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.optimizers import Adam,SGD,Adagrad,Adadelta,RMSprop
from keras.utils import to_categorical
from keras.utils.vis_utils import model_to_dot,plot_model
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.layers import Dropout, Flatten,Activation
from keras.layers import Conv2D, MaxPooling2D, BatchNormalization,GlobalAveragePooling2D,Dropout,Flatten,Dense
from keras.callbacks import ModelCheckpoint,EarlyStopping,TensorBoard,CSVLogger,ReduceLROnPlateau,LearningRateScheduler
from keras.preprocessing.image import ImageDataGenerator,load_img, img_to_array
# In[ ]:
IMG_W = 150
IMG_H = 150
CHANNELS = 3
INPUT_SHAPE = (IMG_W, IMG_H, CHANNELS)
NB_CLASSES = 2
lbls = list(map(str, range(NB_CLASSES)))
# In[ ]:
DATASET_DIR = "/root/dataset/"
# In[ ]:
normal_images = []
for img_path in glob.glob(DATASET_DIR + '/normal/*'):
normal_images.append(mpimg.imread(img_path))
covid_images = []
for img_path in glob.glob(DATASET_DIR + '/covid/*'):
covid_images.append(mpimg.imread(img_path))
# In[ ]:
classifier = Sequential()
classifier.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same',activation ='relu',
input_shape = (IMG_W,IMG_H,3)))
classifier.add(MaxPooling2D(pool_size=(2,2)))
classifier.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same',activation ='relu'))
classifier.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
classifier.add(Flatten())
classifier.add(Dense(units = 512, activation = 'relu'))
classifier.add(Dropout(0.20))
classifier.add(Dense(units = 2, activation = 'softmax'))
classifier.compile(optimizer = Adam(lr=0.001),loss='categorical_crossentropy', metrics = ['accuracy'])
# In[ ]:
classifier.summary()
# In[ ]:
datagen = ImageDataGenerator(
rotation_range=90,
rescale = 1./255,
validation_split = 0.3)
train_generator = datagen.flow_from_directory(
DATASET_DIR,
target_size=(IMG_H, IMG_W),
batch_size=4,
class_mode='categorical',
subset='training')
validation_generator = datagen.flow_from_directory(
DATASET_DIR,
target_size=(IMG_H, IMG_W),
batch_size=4,
class_mode='categorical',
shuffle= False,
subset='validation')
# In[ ]:
es= EarlyStopping(monitor='val_loss', mode ='min', verbose = 1, patience = 10)
mc = ModelCheckpoint('cnn_covid_pred.h5', monitor='val_loss', save_best_only = True, mode ='min', verbose = 1)
# In[ ]:
epochs = 20
steps_per_epoch = 8
# In[ ]:
# history = classifier.fit_generator(train_generator,
# steps_per_epoch = steps_per_epoch,
# epochs=epochs,
# callbacks = [es, mc],
# workers=4,
# validation_data = validation_generator,
# validation_steps = 10)
# In[ ]:
# training_accuracy = history.history['accuracy'][-1]
# validation_accuracy = history.history['val_accuracy'][-1]
# print("training_accuracy ", training_accuracy)
# print("validation_accuracy ", validation_accuracy)
# In[ ]:
# print("training_loss", history.history['loss'][-1])
# print("validation_loss", history.history['val_loss'][-1])
# # change epoch and step_per_epoch 4 times
# In[ ]:
for i in range(4):
epochs+=5
steps_per_epoch+=1
history = classifier.fit_generator(train_generator,
steps_per_epoch = steps_per_epoch,
epochs=epochs,
callbacks = [es, mc],
workers=4,
validation_data = validation_generator,
validation_steps = 10)
training_accuracy = history.history['accuracy'][-1]
validation_accuracy = history.history['val_accuracy'][-1]
print("training_accuracy ", training_accuracy)
print("validation_accuracy ", validation_accuracy)
if training_accuracy > 0.95 :
break
# if training_accuracy < 0.95 :
# print("you dont achieve beter accuracy, now you have to add more convolution layers")
# else :
# print("you achieve better accuracy")
# # add convolution layer
# In[ ]:
if training_accuracy < 0.95 :
classifier = Sequential()
classifier.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same',activation ='relu',
input_shape = (IMG_W,IMG_H,3)))
classifier.add(MaxPooling2D(pool_size=(2,2)))
classifier.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same',activation ='relu'))
classifier.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
#here add one more convolution layer
classifier.add(Conv2D(filters =96, kernel_size = (3,3),padding = 'Same',activation ='relu'))
classifier.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
classifier.add(Flatten())
classifier.add(Dense(units = 512, activation = 'relu'))
classifier.add(Dropout(0.20))
classifier.add(Dense(units = 2, activation = 'softmax'))
classifier.compile(optimizer = Adam(lr=0.001),loss='categorical_crossentropy', metrics = ['accuracy'])
classifier.summary()
history = classifier.fit_generator(train_generator,
steps_per_epoch = steps_per_epoch,
epochs=epochs,
callbacks = [es, mc],
workers=4,
validation_data = validation_generator,
validation_steps = 10)
training_accuracy = history.history['accuracy'][-1]
validation_accuracy = history.history['val_accuracy'][-1]
print("training_accuracy ", training_accuracy)
print("validation_accuracy ", validation_accuracy)
else:
pass
# # add one more convolution layer
# In[ ]:
if training_accuracy > 0.95 :
pass
else:
classifier = Sequential()
classifier.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same',activation ='relu',
input_shape = (IMG_W,IMG_H,3)))
classifier.add(MaxPooling2D(pool_size=(2,2)))
classifier.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same',activation ='relu'))
classifier.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
#here add one more convolution layer
classifier.add(Conv2D(filters =96, kernel_size = (3,3),padding = 'Same',activation ='relu'))
classifier.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
#here add one more convolution layer
classifier.add(Conv2D(filters = 96, kernel_size = (3,3),padding = 'Same',activation ='relu'))
classifier.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))
classifier.add(Flatten())
classifier.add(Dense(units = 512, activation = 'relu'))
classifier.add(Dropout(0.20))
classifier.add(Dense(units = 2, activation = 'softmax'))
classifier.compile(optimizer = Adam(lr=0.001),loss='categorical_crossentropy', metrics = ['accuracy'])
classifier.summary()
history = classifier.fit_generator(train_generator,
steps_per_epoch = steps_per_epoch,
epochs=epochs,
callbacks = [es, mc],
workers=4,
validation_data = validation_generator,
validation_steps = 10)
training_accuracy = history.history['accuracy'][-1]
validation_accuracy = history.history['val_accuracy'][-1]
print("training_accuracy ", training_accuracy)
print("validation_accuracy ", validation_accuracy)
# In[ ]:
if training_accuracy <0.95 :
print("something went wrong plzz... contact to developer")