model2.py

# coding: utf-8

# In[2]:


import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)


import cv2
import matplotlib.pyplot as plt 
import seaborn as sns
import os
from PIL import Image
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import load_img
from keras.utils import np_utils


# In[3]:


parasitized_data = os.listdir('./data/Parasitized/') 
print(parasitized_data[:10])
uninfected_data = os.listdir('./data/Uninfected/')
print('\n')
print(uninfected_data[:10])


# In[4]:


data = []
labels = []
for img in parasitized_data:
    try:
        img_read = plt.imread('./data/Parasitized/' + "/" + img)
        img_resize = cv2.resize(img_read, (50, 50))
        img_array = img_to_array(img_resize)
        data.append(img_array)
        labels.append(1)
    except:
        None
        
for img in uninfected_data:
    try:
        img_read = plt.imread('./data/Uninfected' + "/" + img)
        img_resize = cv2.resize(img_read, (50, 50))
        img_array = img_to_array(img_resize)
        data.append(img_array)
        labels.append(0)
    except:
        None


# In[5]:


plt.imshow(data[0])
plt.show()


# In[ ]:


image_data = np.array(data)
labels = np.array(labels)


# In[ ]:


idx = np.arange(image_data.shape[0])
np.random.shuffle(idx)
image_data = image_data[idx]
labels = labels[idx]


# In[ ]:


from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(image_data, labels, test_size = 0.2, random_state = 101)


# In[ ]:


y_train = np_utils.to_categorical(y_train, num_classes = 2)
y_test = np_utils.to_categorical(y_test, num_classes = 2)


# In[ ]:


print(f'SHAPE OF TRAINING IMAGE DATA : {x_train.shape}')
print(f'SHAPE OF TESTING IMAGE DATA : {x_test.shape}')
print(f'SHAPE OF TRAINING LABELS : {y_train.shape}')
print(f'SHAPE OF TESTING LABELS : {y_test.shape}')


# In[ ]:


import keras
from keras.layers import Dense, Conv2D
from keras.layers import Flatten
from keras.layers import MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Activation
from keras.layers import BatchNormalization
from keras.layers import Dropout
from keras.models import Sequential
from keras import backend as K

from keras import optimizers


# In[ ]:


def CNNbuild(height, width, classes, channels):
    model = Sequential()
    
    inputShape = (height, width, channels)
    chanDim = -1
    
    if K.image_data_format() == 'channels_first':
        inputShape = (channels, height, width)
    model.add(Conv2D(32, (3,3), activation = 'relu', input_shape = inputShape))
    model.add(MaxPooling2D(2,2))
    model.add(BatchNormalization(axis = chanDim))
    model.add(Dropout(0.2))

    model.add(Conv2D(32, (3,3), activation = 'relu'))
    model.add(MaxPooling2D(2,2))
    model.add(BatchNormalization(axis = chanDim))
    model.add(Dropout(0.2))

    model.add(Conv2D(32, (3,3), activation = 'relu'))
    model.add(MaxPooling2D(2,2))
    model.add(BatchNormalization(axis = chanDim))
    model.add(Dropout(0.2))

    model.add(Flatten())
    
    model.add(Dense(512, activation = 'relu'))
    model.add(BatchNormalization(axis = chanDim))
    model.add(Dropout(0.5))
    model.add(Dense(classes, activation = 'softmax'))
    
    return model


# In[ ]:


#instantiate the model
height = 50
width = 50
classes = 2
channels = 3
model = CNNbuild(height = height, width = width, classes = classes, channels = channels)
model.summary()


# In[ ]:


model.compile(loss = 'categorical_crossentropy', optimizer = 'Adam', metrics = ['accuracy'])


# In[18]:


h = model.fit(x_train, y_train, epochs = 20, batch_size = 32)


# In[19]:


plt.figure(figsize = (18,8))
plt.plot(range(20), h.history['acc'], label = 'Training Accuracy')
plt.plot(range(20), h.history['loss'], label = 'Taining Loss')
#ax1.set_xticks(np.arange(0, 31, 5))
plt.xlabel("Number of Epoch's")
plt.ylabel('Accuracy/Loss Value')
plt.title('Training Accuracy and Training Loss')
plt.legend(loc = "best")


# In[20]:


predictions = model.evaluate(x_test, y_test)
print(f'LOSS : {predictions[0]}')
print(f'ACCURACY : {predictions[1]}')