Deep Learning 2-Classification with Custom CNN

Learn A-Z Deep Learning in 15 Days

In this Lecture, will learn about Classification with Custom CNN

Imp:-Use Kaggle Kernel Or Google Colab to Run Code. Download the Cat Vs Dog dataset from the link below.

https://drive.google.com/open?id=1UA0NaL8bJqGBqMSeOfbE7FXRZ5z3RV2s

import cv2
import os 
import numpy as np

Unzip  the Dataset

!unzip ../input/train.zip

!unzip ../input/test1.zip

Import the Train 

IMAGE_WIDTH = 128
IMAGE_HEIGHT = 128
IMAGE_CHANNELS = 1
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)

directory = "/kaggle/working/train"
data = []
label = []

for filename in os.listdir(directory):

    image = cv2.imread(directory+r'/'+filename,0)
    
    if image is None:
        continue
    image = cv2.resize(image,IMAGE_SIZE)
    
    category = filename.split('.')[0]
    if category == 'dog':
        label.append(1)
    else:
        label.append(0)

    data.append(image/255)

List to Array Conversion

data=np.array(data)
data=data.reshape((data.shape)[0],(data.shape)[1],(data.shape)[2],1)
label=np.array(label)
print(data.shape)
print(label.shape)

Train Test Split

from sklearn.model_selection import train_test_split
x_train, x_val, y_train, y_val = train_test_split(data, label, test_size=0.3, random_state=42) 

One hot encoding

from keras.utils import np_utils
y_train = np_utils.to_categorical(y_train,num_classes=2)
y_val = np_utils.to_categorical(y_val,num_classes=2)

Custom CNN Architecture

### 1st Network
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, Activation, BatchNormalization
​
model = Sequential()
​
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
​
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
​
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
​
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) 
​
model.summary()
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

OR

### 2nd Network
from keras.models import Sequential, Model
from keras.layers import Dense, Flatten, Activation, Dropout, Conv2D, MaxPooling2D, AveragePooling2D, Input, BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers 
​
​
img_input  = Input(shape=(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS))
​
x = Conv2D(32, (3, 3),activation='relu',padding='same')(img_input)
x = BatchNormalization()(x)
x = MaxPooling2D((2, 2), strides=(2, 2))(x)
x = Dropout(0.25)(x)
​
x = Conv2D(64, (3, 3),activation='relu',padding='same')(img_input)
x = BatchNormalization()(x)
x = MaxPooling2D((2, 2), strides=(2, 2))(x)
x = Dropout(0.25)(x)
​
x = Conv2D(128, (3, 3),activation='relu',padding='same')(img_input)
x = BatchNormalization()(x)
x = MaxPooling2D((2, 2), strides=(2, 2))(x)
x = Dropout(0.25)(x)
​
x = Flatten()(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.5)(x)
x = Dense(2, activation='softmax', name='predictions')(x)
​
model = Model(img_input , x)
model.summary()
model.compile(loss='categorical_crossentropy',optimizer=optimizers.adam(lr=1e-8),metrics=['acc'])

Callbacks

from keras.callbacks import ModelCheckpoint, EarlyStopping
​
filepath = "./cp-{epoch:02d}.h5"
​
checkpoint = ModelCheckpoint(filepath,
                             monitor="val_loss",
                             mode="min",
                             save_best_only = True,
                             verbose=1)
​
​
earlystop = EarlyStopping(monitor = 'val_loss', 
                          min_delta = 0, 
                          patience =4,
                          verbose = 1,
                          restore_best_weights = True)
​
# put our callbacks into a callback list
callbacks = [earlystop, checkpoint]

Start the Training

model.fit(x_train,y_train,validation_data=(x_val,y_val),epochs=50,batch_size=64, callbacks = callbacks)

Visualize Graph

### Graph Epoch vs acc AND Epoch vs Loss

import matplotlib.pyplot as plt
​
acc = model.history.history['acc']
val_acc = model.history.history['val_acc']
loss = model.history.history['loss']
val_loss = model.history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'blue', label='Training acc')
plt.plot(epochs, val_acc, 'red', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'blue', label='Training loss')
plt.plot(epochs, val_loss, 'red', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()

Import test dataset

IMAGE_WIDTH = 128
IMAGE_HEIGHT = 128
IMAGE_CHANNELS = 1
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
​
directory = "/kaggle/working/test1"
data = []
​
​
for filename in os.listdir(directory):
​
    image = cv2.imread(directory+r'/'+filename,0)
    if image is None:
        continue
    image = cv2.resize(image,IMAGE_SIZE)
    
    data.append(image/255)

List to Array Conversion

data = np.array(data)
test_image = np.array(data.reshape((data.shape)[0],(data.shape)[1],(data.shape)[2],1))

Make the Prediction

predictions = model.predict(test_image)
results = np.argmax(predictions, axis = 1)

Convert the label into the category

key={0:'cat',1:'dog'}

label_prediction=[key[r] for r in results]

Display the Result

import matplotlib.image as img
import matplotlib.pyplot as plt
​
nb_rows = 3
nb_cols = 3
fig, axs = plt.subplots(nb_rows, nb_cols, figsize=(6, 6), dpi =100)
​
n = 0
for i in range(0, nb_rows):
    for j in range(0, nb_cols):
        axs[i,j].set_title(label_prediction[n])
        axs[i,j].imshow(data[n],cmap = "gray")
        n += 1  
        
plt.tight_layout()
plt.show()

Create the Dataframe

import pandas as pd
​
df=pd.DataFrame(data={'imagename':os.listdir(directory), 'predicted_labels': label_prediction})
df.head()

Save the dataframe in csv format

df.to_csv('submission_new_model.csv', index=False, header=True)

In the next blog, we will start  Deep Learning Classification with Data Augmentation.

https://sngurukuls247.blogspot.com/2020/05/deep-learning-3-classification-with_9.html

                                                                                                                                                    

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