Training.py

# coding: utf-8

# ## Transfer learning
# This notebook intended to try the transfer learning technique based on Google MobileNet model for defect checking dataset

# In[3]:

#import libraries
from __future__ import print_function

import numpy as np
np.random.seed(2048)

import os
import glob
import cv2
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from tqdm import *
import warnings
warnings.filterwarnings('ignore')

import keras
from keras import applications
from keras import optimizers
from keras.models import Sequential, Model 
from keras.layers import Dropout, Flatten, Dense
from keras import backend as k 
from keras.callbacks import TensorBoard
from keras.applications.mobilenet import MobileNet


# In[4]:

# Number representation of labels
HONEYCOMBING_ID = 0
FORMWORK_ID = 1


# In[5]:

# Data parameters
image_size = (224, 224)
input_shape = (224, 224, 3)
test_size = 0.25


# In[6]:

def get_im_cv2(path):
    img = cv2.imread(path)
    resized = cv2.resize(img, image_size, cv2.INTER_LINEAR)
    return resized

def load_train(path):
    x_train = []
    y_train = []
    x_test = []
    y_test = []
    
    # Load the label csv
    labels_df = pd.read_csv(path)
    
    # Split data between training and validation
    train_df, test_df = train_test_split(labels_df, test_size=test_size)
    for idx, row in tqdm(train_df.iterrows()):
        label = row.Defect
        img_path = os.path.join('..', 'input', 'train', str(row.ID) + '.jpg') 
        img = get_im_cv2(img_path)
        x_train.append(img)
        
        # There just two labels
        label_id = -1
        if (label == 'Honeycombing'):
            label_id = HONEYCOMBING_ID
        elif (label == 'Formwork'):
            label_id = FORMWORK_ID
        
        y_train.append(label_id)
        
    for idx, row in tqdm(test_df.iterrows()):
        label = row.Defect
        img_path = os.path.join('..', 'input', 'train', str(row.ID) + '.jpg') 
        img = get_im_cv2(img_path)
        x_test.append(img)
        
        # There just two labels
        label_id = -1
        if (label == 'Honeycombing'):
            label_id = HONEYCOMBING_ID
        elif (label == 'Formwork'):
            label_id = FORMWORK_ID
        
        y_test.append(label_id)    
        
    x_train = np.asarray(x_train)
    y_train = np.asarray(y_train)
    x_test = np.asarray(x_test)
    y_test = np.asarray(y_test)
    
    return x_train, y_train, x_test, y_test


# In[7]:

path = '../input/labels.csv'
x_train, y_train, x_test, y_test = load_train(path)

print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')


# In[8]:

# Training parameters
num_classes = 2
data_augmentation = False


# In[9]:

# Convert class vectors to binary class matrices.
Y_train = keras.utils.to_categorical(y_train, num_classes)
Y_test = keras.utils.to_categorical(y_test, num_classes)

X_train = x_train.astype('float32')
X_test = x_test.astype('float32')
X_train /= 255
X_test /= 255


# In[10]:

# Load mobilenet without the classification layer
model = MobileNet(weights=None, include_top=True, input_shape=input_shape, classes=2)
# Show the summary
model.summary()


# In[13]:

model.compile(loss='categorical_crossentropy',
                    optimizer = optimizers.SGD(lr=0.0001, momentum=0.9), 
                    metrics=["accuracy"])


# In[14]:

batch_size = 32
epochs = 50
verbose = 1

# Train the new model
history = model.fit(X_train, Y_train, 
                          epochs=epochs, batch_size=batch_size, 
                          validation_data=(X_test, Y_test), 
                          verbose=verbose, shuffle=True)

# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()


# In[15]:

model.save('/notebooks/keras_mobilenet_defect.h5')