I am new to deep learning. I want to create a classifier which can predict nationality names on the bases of nationality on driving license id. To accomplish that, I created a data set of USA driving license images. Then created training data-set, labels and features of data and a classification model.
When I run my python script, the accuracy was 92%. When I tested my model with different images it gave wrong result.
Python code like this:
import numpy as np
import pandas as pd
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.callbacks import TensorBoard
import pickle
import os
import cv2
import time
DATADIR = '/home/anupam/Documents/workspace/DjangoProject/DF-Web/datafornix/USADL_DATASET'
CATEGORIES = [
'Alabma',
'Connecticut',
'California',
'Delaware',
'Georgia',
'Indiana',
'Louisiana',
'Maine',
'Massachusetts',
'MaryLand',
'NewHamshire',
'NewJersey',
'NewYork',
'NewMexico',
'Pennsylvania',
'RohodeIsland',
'Vermont',
'Virginia',
]
encoder = LabelEncoder()
city_labels = encoder.fit_transform(CATEGORIES)
# print(city_labels)
encoder = OneHotEncoder(sparse=False)
city_labels = city_labels.reshape((18, 1))
state_array = encoder.fit_transform(city_labels)
training_data = []
IMG_SIZE = 200
def create_traing_dataset():
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
class_num = CATEGORIES.index(category)
for img in os.listdir(path):
for x in state_array:
try:
img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
training_data.append([new_array, x])
except Exception as e:
pass
create_traing_dataset()
#Randomize the dataset
import random
random.shuffle(training_data)
#Create a model
X = []
y = []
for features,label in training_data:
X.append(features)
y.append(label)
X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
pickle_out = open("X.pickle","wb")
pickle.dump(X, pickle_out)
pickle_out.close()
pickle_out = open("y.pickle","wb")
pickle.dump(y, pickle_out)
pickle_out.close()
pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)
pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in)
y_data = []
for i in y:
y_data.append(i[0])
y = np.array(y_data)
print(y)
X = X/255.0
print(X.size)
dense_layers = [0]
layer_sizes = [64]
conv_layers = [3]
for dense_layer in dense_layers:
for layer_size in layer_sizes:
for conv_layer in conv_layers:
NAME = "{}-conv-{}-nodes-{}-dense-{}".format(conv_layer, layer_size, dense_layer, int(time.time()))
print(NAME)
model = Sequential()
model.add(Conv2D(layer_size, (3, 3), input_shape=X.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
for l in range(conv_layer-1):
model.add(Conv2D(layer_size, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
for _ in range(dense_layer):
model.add(Dense(layer_size))
model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
tensorboard = TensorBoard(log_dir="logs/{}".format(NAME))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'],
)
model.fit(X, y,
batch_size=32,
epochs=10,
validation_split=0.3,
callbacks=[tensorboard])
model.save('64x3-CNN.model')
And test classifier model like this:-
import cv2
import tensorflow as tf
model = tf.keras.models.load_model("64x3-CNN.model")
CATEGORIES = [
'Alabma',
'Connecticut',
'California',
'Delaware',
'Georgia',
'Indiana',
'Louisiana',
'Maine',
'Massachusetts',
'MaryLand',
'NewHamshire',
'NewJersey',
'NewYork',
'NewMexico',
'Pennsylvania',
'RohodeIsland',
'Vermont',
'Virginia',
]
def prepare(filepath):
IMG_SIZE = 200 # 50 in txt-based
img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE) # read in the image, convert to grayscale
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE)) # resize image to match model's expected sizing
return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1) # return the image with shaping that TF wants.
prediction = model.predict([prepare('/home/anupam/Documents/workspace/DjangoProject/DF-Web/datafornix/download.jpeg')]) # REMEMBER YOU'RE PASSING A LIST OF THINGS YOU WISH TO PREDICT
print(prediction)
print(CATEGORIES[int(prediction[0][0])])
I think, I wrong with features and labels of training data set. If I wrong this. How I resolve that case??
What is the approach to find best result??
Please help to clear my above query.
Thanks
