MobileNetV2 is a popular deep learning model that was introduced by Google in 2018 as an improvement over its predecessor, MobileNetV1. It is designed specifically for mobile and embedded devices with limited computational resources.
The primary goal of MobileNetV2 is to provide a lightweight and efficient architecture while still achieving competitive accuracy in various computer vision tasks.
# import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import random
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import shutil
import tensorflow as tf
from tensorflow import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.layers.core import Dense
# define the base working directory
BASE_DIR = os.getcwd()
# define data directory
DATA_DIR = os.path.join(BASE_DIR, 'data')
# define paths to the other directories
TRAIN_DIR = os.path.join(DATA_DIR, 'train')
VALIDATION_DIR = os.path.join(DATA_DIR, 'validation')
TEST_DIR = os.path.join(DATA_DIR, 'test')
# A function to get a random image from each class using the data folder, and show them
def show_nRandom_images(data_dir= DATA_DIR):
# for each class, grab one random image
for class_ in os.listdir(DATA_DIR):
# Get a list of all image files in the folder
image_files = [f for f in os.listdir(os.path.join(DATA_DIR,class_)) if os.path.isfile(os.path.join(DATA_DIR,class_ ,f))]
# Select a random image from the list and Display it
img = mpimg.imread(os.path.join(os.path.join(DATA_DIR,class_ ,random.choice(image_files))))
plt.imshow(img)
plt.title('sample from class {}'.format(class_))
# plt.axis('off')
plt.show()
# execute the function
show_nRandom_images()
#### See number of samples within each class
def get_metadata():
metadata_df = pd.DataFrame(columns=['class','n_sample'] , index=[])
for class_ in range(10):
# Get a list of all image files in the folder
image_files = [f for f in os.listdir(os.path.join(DATA_DIR,str(class_))) if os.path.isfile(os.path.join(DATA_DIR,str(class_) ,f))]
metadata_df.loc[metadata_df.shape[0],['class','n_sample']] = [str(class_),len(image_files)]
return metadata_df
# see results
get_metadata()
| class | n_sample | |
|---|---|---|
| 0 | 0 | 205 |
| 1 | 1 | 206 |
| 2 | 2 | 206 |
| 3 | 3 | 206 |
| 4 | 4 | 207 |
| 5 | 5 | 207 |
| 6 | 6 | 207 |
| 7 | 7 | 206 |
| 8 | 8 | 208 |
| 9 | 9 | 204 |
# more details about each class samples
get_metadata().plot.bar()
<AxesSubplot:>
The data is balanced as expected
data/
# verify that this structure is not here yet
if(os.path.isdir(os.path.join(DATA_DIR,'train')) == False):
# create three directories (train, val, test)
os.mkdir(os.path.join(DATA_DIR,'train'))
os.mkdir(os.path.join(DATA_DIR,'validation'))
os.mkdir(os.path.join(DATA_DIR,'test'))
# Organize data to follow a machine learning dataset structure
# loop through the original data folder
for class_ in range(10):
# Get a list of all image files in the folder
image_files = [f for f in os.listdir(os.path.join(DATA_DIR,str(class_))) if os.path.isfile(os.path.join(DATA_DIR,str(class_) ,f))]
# for validation
# create corresponding class_ folder
os.mkdir(os.path.join(VALIDATION_DIR,str(class_)))
# take 30 random samples
validation_set = random.sample(image_files, 30)
# move them to the validation directory
for validation_img in validation_set:
shutil.move(os.path.join(DATA_DIR,str(class_),validation_img), os.path.join(VALIDATION_DIR,str(class_)))
# for test
# Refresh the list of all image files in the folder
image_files = [f for f in os.listdir(os.path.join(DATA_DIR,str(class_))) if os.path.isfile(os.path.join(DATA_DIR,str(class_) ,f))]
# create corresponding class_ folder
os.mkdir(os.path.join(TEST_DIR,str(class_)))
# take 30 random samples
test_set = random.sample(image_files, 5)
# move them to the test directory
for test_img in test_set:
shutil.move(os.path.join(DATA_DIR,str(class_),test_img), os.path.join(TEST_DIR,str(class_)))
# for training
# takes the remaining samples
for class_ in range(10):
# Get a list of all image files in the folder
train_set = [f for f in os.listdir(os.path.join(DATA_DIR,str(class_))) if os.path.isfile(os.path.join(DATA_DIR,str(class_) ,f))]
# create corresponding class_ folder
os.mkdir(os.path.join(TRAIN_DIR,str(class_)))
# move them to the validation directory
for train_img in train_set:
shutil.move(os.path.join(DATA_DIR,str(class_),train_img), os.path.join(TRAIN_DIR,str(class_)))
else:
print('The structure you are trying to create is already here!')
# a function to do the job
def get_metadataOfFolder(folder_name, folder_path):
metadata_df = pd.DataFrame(columns=[folder_name+'_classes','{}_samples'.format(folder_name)] , index=[])
for class_ in os.listdir(folder_path):
# Get a list of all image files in the folder
image_files = [f for f in os.listdir(os.path.join(folder_path,class_)) if os.path.isfile(os.path.join(folder_path, class_ ,f))]
metadata_df.loc[metadata_df.shape[0],[folder_name+'_classes','{}_samples'.format(folder_name)]] = [str(class_),len(image_files)]
return metadata_df
# for training data
get_metadataOfFolder('train',TRAIN_DIR)
| train_classes | train_samples | |
|---|---|---|
| 0 | 0 | 170 |
| 1 | 1 | 171 |
| 2 | 2 | 171 |
| 3 | 3 | 171 |
| 4 | 4 | 172 |
| 5 | 5 | 172 |
| 6 | 6 | 172 |
| 7 | 7 | 171 |
| 8 | 8 | 173 |
| 9 | 9 | 169 |
# for testing data
get_metadataOfFolder('test',TEST_DIR)
| test_classes | test_samples | |
|---|---|---|
| 0 | 0 | 5 |
| 1 | 1 | 5 |
| 2 | 2 | 5 |
| 3 | 3 | 5 |
| 4 | 4 | 5 |
| 5 | 5 | 5 |
| 6 | 6 | 5 |
| 7 | 7 | 5 |
| 8 | 8 | 5 |
| 9 | 9 | 5 |
# for validation data
get_metadataOfFolder('validation',VALIDATION_DIR)
| validation_classes | validation_samples | |
|---|---|---|
| 0 | 0 | 30 |
| 1 | 1 | 30 |
| 2 | 2 | 30 |
| 3 | 3 | 30 |
| 4 | 4 | 30 |
| 5 | 5 | 30 |
| 6 | 6 | 30 |
| 7 | 7 | 30 |
| 8 | 8 | 30 |
| 9 | 9 | 30 |
train_chunks = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet_v2.preprocess_input).flow_from_directory(
directory=TRAIN_DIR, target_size=(224,224), batch_size=10)
valid_chunks = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet_v2.preprocess_input).flow_from_directory(
directory=VALIDATION_DIR, target_size=(224,224), batch_size=10)
test_chunks = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet_v2.preprocess_input).flow_from_directory(
directory=TEST_DIR, target_size=(224,224), batch_size=10, shuffle=False)
Found 1712 images belonging to 10 classes. Found 300 images belonging to 10 classes. Found 50 images belonging to 10 classes.
# Initialize the MobileNetV2 object
mobilenetv2 = tf.keras.applications.MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
By setting include_top=False, you exclude these dense layers, effectively removing the final classification/regression functionality of the pre-trained model. This allows you to add your own custom top layers suited to your specific task, such as a different number of output classes or a different type of prediction.
# Discover model architecture
mobilenetv2.summary()
Model: "mobilenetv2_1.00_224"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_17 (InputLayer) [(None, 224, 224, 3 0 []
)]
Conv1 (Conv2D) (None, 112, 112, 32 864 ['input_17[0][0]']
)
bn_Conv1 (BatchNormalization) (None, 112, 112, 32 128 ['Conv1[0][0]']
)
Conv1_relu (ReLU) (None, 112, 112, 32 0 ['bn_Conv1[0][0]']
)
expanded_conv_depthwise (Depth (None, 112, 112, 32 288 ['Conv1_relu[0][0]']
wiseConv2D) )
expanded_conv_depthwise_BN (Ba (None, 112, 112, 32 128 ['expanded_conv_depthwise[0][0]']
tchNormalization) )
expanded_conv_depthwise_relu ( (None, 112, 112, 32 0 ['expanded_conv_depthwise_BN[0][0
ReLU) ) ]']
expanded_conv_project (Conv2D) (None, 112, 112, 16 512 ['expanded_conv_depthwise_relu[0]
) [0]']
expanded_conv_project_BN (Batc (None, 112, 112, 16 64 ['expanded_conv_project[0][0]']
hNormalization) )
block_1_expand (Conv2D) (None, 112, 112, 96 1536 ['expanded_conv_project_BN[0][0]'
) ]
block_1_expand_BN (BatchNormal (None, 112, 112, 96 384 ['block_1_expand[0][0]']
ization) )
block_1_expand_relu (ReLU) (None, 112, 112, 96 0 ['block_1_expand_BN[0][0]']
)
block_1_pad (ZeroPadding2D) (None, 113, 113, 96 0 ['block_1_expand_relu[0][0]']
)
block_1_depthwise (DepthwiseCo (None, 56, 56, 96) 864 ['block_1_pad[0][0]']
nv2D)
block_1_depthwise_BN (BatchNor (None, 56, 56, 96) 384 ['block_1_depthwise[0][0]']
malization)
block_1_depthwise_relu (ReLU) (None, 56, 56, 96) 0 ['block_1_depthwise_BN[0][0]']
block_1_project (Conv2D) (None, 56, 56, 24) 2304 ['block_1_depthwise_relu[0][0]']
block_1_project_BN (BatchNorma (None, 56, 56, 24) 96 ['block_1_project[0][0]']
lization)
block_2_expand (Conv2D) (None, 56, 56, 144) 3456 ['block_1_project_BN[0][0]']
block_2_expand_BN (BatchNormal (None, 56, 56, 144) 576 ['block_2_expand[0][0]']
ization)
block_2_expand_relu (ReLU) (None, 56, 56, 144) 0 ['block_2_expand_BN[0][0]']
block_2_depthwise (DepthwiseCo (None, 56, 56, 144) 1296 ['block_2_expand_relu[0][0]']
nv2D)
block_2_depthwise_BN (BatchNor (None, 56, 56, 144) 576 ['block_2_depthwise[0][0]']
malization)
block_2_depthwise_relu (ReLU) (None, 56, 56, 144) 0 ['block_2_depthwise_BN[0][0]']
block_2_project (Conv2D) (None, 56, 56, 24) 3456 ['block_2_depthwise_relu[0][0]']
block_2_project_BN (BatchNorma (None, 56, 56, 24) 96 ['block_2_project[0][0]']
lization)
block_2_add (Add) (None, 56, 56, 24) 0 ['block_1_project_BN[0][0]',
'block_2_project_BN[0][0]']
block_3_expand (Conv2D) (None, 56, 56, 144) 3456 ['block_2_add[0][0]']
block_3_expand_BN (BatchNormal (None, 56, 56, 144) 576 ['block_3_expand[0][0]']
ization)
block_3_expand_relu (ReLU) (None, 56, 56, 144) 0 ['block_3_expand_BN[0][0]']
block_3_pad (ZeroPadding2D) (None, 57, 57, 144) 0 ['block_3_expand_relu[0][0]']
block_3_depthwise (DepthwiseCo (None, 28, 28, 144) 1296 ['block_3_pad[0][0]']
nv2D)
block_3_depthwise_BN (BatchNor (None, 28, 28, 144) 576 ['block_3_depthwise[0][0]']
malization)
block_3_depthwise_relu (ReLU) (None, 28, 28, 144) 0 ['block_3_depthwise_BN[0][0]']
block_3_project (Conv2D) (None, 28, 28, 32) 4608 ['block_3_depthwise_relu[0][0]']
block_3_project_BN (BatchNorma (None, 28, 28, 32) 128 ['block_3_project[0][0]']
lization)
block_4_expand (Conv2D) (None, 28, 28, 192) 6144 ['block_3_project_BN[0][0]']
block_4_expand_BN (BatchNormal (None, 28, 28, 192) 768 ['block_4_expand[0][0]']
ization)
block_4_expand_relu (ReLU) (None, 28, 28, 192) 0 ['block_4_expand_BN[0][0]']
block_4_depthwise (DepthwiseCo (None, 28, 28, 192) 1728 ['block_4_expand_relu[0][0]']
nv2D)
block_4_depthwise_BN (BatchNor (None, 28, 28, 192) 768 ['block_4_depthwise[0][0]']
malization)
block_4_depthwise_relu (ReLU) (None, 28, 28, 192) 0 ['block_4_depthwise_BN[0][0]']
block_4_project (Conv2D) (None, 28, 28, 32) 6144 ['block_4_depthwise_relu[0][0]']
block_4_project_BN (BatchNorma (None, 28, 28, 32) 128 ['block_4_project[0][0]']
lization)
block_4_add (Add) (None, 28, 28, 32) 0 ['block_3_project_BN[0][0]',
'block_4_project_BN[0][0]']
block_5_expand (Conv2D) (None, 28, 28, 192) 6144 ['block_4_add[0][0]']
block_5_expand_BN (BatchNormal (None, 28, 28, 192) 768 ['block_5_expand[0][0]']
ization)
block_5_expand_relu (ReLU) (None, 28, 28, 192) 0 ['block_5_expand_BN[0][0]']
block_5_depthwise (DepthwiseCo (None, 28, 28, 192) 1728 ['block_5_expand_relu[0][0]']
nv2D)
block_5_depthwise_BN (BatchNor (None, 28, 28, 192) 768 ['block_5_depthwise[0][0]']
malization)
block_5_depthwise_relu (ReLU) (None, 28, 28, 192) 0 ['block_5_depthwise_BN[0][0]']
block_5_project (Conv2D) (None, 28, 28, 32) 6144 ['block_5_depthwise_relu[0][0]']
block_5_project_BN (BatchNorma (None, 28, 28, 32) 128 ['block_5_project[0][0]']
lization)
block_5_add (Add) (None, 28, 28, 32) 0 ['block_4_add[0][0]',
'block_5_project_BN[0][0]']
block_6_expand (Conv2D) (None, 28, 28, 192) 6144 ['block_5_add[0][0]']
block_6_expand_BN (BatchNormal (None, 28, 28, 192) 768 ['block_6_expand[0][0]']
ization)
block_6_expand_relu (ReLU) (None, 28, 28, 192) 0 ['block_6_expand_BN[0][0]']
block_6_pad (ZeroPadding2D) (None, 29, 29, 192) 0 ['block_6_expand_relu[0][0]']
block_6_depthwise (DepthwiseCo (None, 14, 14, 192) 1728 ['block_6_pad[0][0]']
nv2D)
block_6_depthwise_BN (BatchNor (None, 14, 14, 192) 768 ['block_6_depthwise[0][0]']
malization)
block_6_depthwise_relu (ReLU) (None, 14, 14, 192) 0 ['block_6_depthwise_BN[0][0]']
block_6_project (Conv2D) (None, 14, 14, 64) 12288 ['block_6_depthwise_relu[0][0]']
block_6_project_BN (BatchNorma (None, 14, 14, 64) 256 ['block_6_project[0][0]']
lization)
block_7_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_6_project_BN[0][0]']
block_7_expand_BN (BatchNormal (None, 14, 14, 384) 1536 ['block_7_expand[0][0]']
ization)
block_7_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_7_expand_BN[0][0]']
block_7_depthwise (DepthwiseCo (None, 14, 14, 384) 3456 ['block_7_expand_relu[0][0]']
nv2D)
block_7_depthwise_BN (BatchNor (None, 14, 14, 384) 1536 ['block_7_depthwise[0][0]']
malization)
block_7_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_7_depthwise_BN[0][0]']
block_7_project (Conv2D) (None, 14, 14, 64) 24576 ['block_7_depthwise_relu[0][0]']
block_7_project_BN (BatchNorma (None, 14, 14, 64) 256 ['block_7_project[0][0]']
lization)
block_7_add (Add) (None, 14, 14, 64) 0 ['block_6_project_BN[0][0]',
'block_7_project_BN[0][0]']
block_8_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_7_add[0][0]']
block_8_expand_BN (BatchNormal (None, 14, 14, 384) 1536 ['block_8_expand[0][0]']
ization)
block_8_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_8_expand_BN[0][0]']
block_8_depthwise (DepthwiseCo (None, 14, 14, 384) 3456 ['block_8_expand_relu[0][0]']
nv2D)
block_8_depthwise_BN (BatchNor (None, 14, 14, 384) 1536 ['block_8_depthwise[0][0]']
malization)
block_8_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_8_depthwise_BN[0][0]']
block_8_project (Conv2D) (None, 14, 14, 64) 24576 ['block_8_depthwise_relu[0][0]']
block_8_project_BN (BatchNorma (None, 14, 14, 64) 256 ['block_8_project[0][0]']
lization)
block_8_add (Add) (None, 14, 14, 64) 0 ['block_7_add[0][0]',
'block_8_project_BN[0][0]']
block_9_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_8_add[0][0]']
block_9_expand_BN (BatchNormal (None, 14, 14, 384) 1536 ['block_9_expand[0][0]']
ization)
block_9_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_9_expand_BN[0][0]']
block_9_depthwise (DepthwiseCo (None, 14, 14, 384) 3456 ['block_9_expand_relu[0][0]']
nv2D)
block_9_depthwise_BN (BatchNor (None, 14, 14, 384) 1536 ['block_9_depthwise[0][0]']
malization)
block_9_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_9_depthwise_BN[0][0]']
block_9_project (Conv2D) (None, 14, 14, 64) 24576 ['block_9_depthwise_relu[0][0]']
block_9_project_BN (BatchNorma (None, 14, 14, 64) 256 ['block_9_project[0][0]']
lization)
block_9_add (Add) (None, 14, 14, 64) 0 ['block_8_add[0][0]',
'block_9_project_BN[0][0]']
block_10_expand (Conv2D) (None, 14, 14, 384) 24576 ['block_9_add[0][0]']
block_10_expand_BN (BatchNorma (None, 14, 14, 384) 1536 ['block_10_expand[0][0]']
lization)
block_10_expand_relu (ReLU) (None, 14, 14, 384) 0 ['block_10_expand_BN[0][0]']
block_10_depthwise (DepthwiseC (None, 14, 14, 384) 3456 ['block_10_expand_relu[0][0]']
onv2D)
block_10_depthwise_BN (BatchNo (None, 14, 14, 384) 1536 ['block_10_depthwise[0][0]']
rmalization)
block_10_depthwise_relu (ReLU) (None, 14, 14, 384) 0 ['block_10_depthwise_BN[0][0]']
block_10_project (Conv2D) (None, 14, 14, 96) 36864 ['block_10_depthwise_relu[0][0]']
block_10_project_BN (BatchNorm (None, 14, 14, 96) 384 ['block_10_project[0][0]']
alization)
block_11_expand (Conv2D) (None, 14, 14, 576) 55296 ['block_10_project_BN[0][0]']
block_11_expand_BN (BatchNorma (None, 14, 14, 576) 2304 ['block_11_expand[0][0]']
lization)
block_11_expand_relu (ReLU) (None, 14, 14, 576) 0 ['block_11_expand_BN[0][0]']
block_11_depthwise (DepthwiseC (None, 14, 14, 576) 5184 ['block_11_expand_relu[0][0]']
onv2D)
block_11_depthwise_BN (BatchNo (None, 14, 14, 576) 2304 ['block_11_depthwise[0][0]']
rmalization)
block_11_depthwise_relu (ReLU) (None, 14, 14, 576) 0 ['block_11_depthwise_BN[0][0]']
block_11_project (Conv2D) (None, 14, 14, 96) 55296 ['block_11_depthwise_relu[0][0]']
block_11_project_BN (BatchNorm (None, 14, 14, 96) 384 ['block_11_project[0][0]']
alization)
block_11_add (Add) (None, 14, 14, 96) 0 ['block_10_project_BN[0][0]',
'block_11_project_BN[0][0]']
block_12_expand (Conv2D) (None, 14, 14, 576) 55296 ['block_11_add[0][0]']
block_12_expand_BN (BatchNorma (None, 14, 14, 576) 2304 ['block_12_expand[0][0]']
lization)
block_12_expand_relu (ReLU) (None, 14, 14, 576) 0 ['block_12_expand_BN[0][0]']
block_12_depthwise (DepthwiseC (None, 14, 14, 576) 5184 ['block_12_expand_relu[0][0]']
onv2D)
block_12_depthwise_BN (BatchNo (None, 14, 14, 576) 2304 ['block_12_depthwise[0][0]']
rmalization)
block_12_depthwise_relu (ReLU) (None, 14, 14, 576) 0 ['block_12_depthwise_BN[0][0]']
block_12_project (Conv2D) (None, 14, 14, 96) 55296 ['block_12_depthwise_relu[0][0]']
block_12_project_BN (BatchNorm (None, 14, 14, 96) 384 ['block_12_project[0][0]']
alization)
block_12_add (Add) (None, 14, 14, 96) 0 ['block_11_add[0][0]',
'block_12_project_BN[0][0]']
block_13_expand (Conv2D) (None, 14, 14, 576) 55296 ['block_12_add[0][0]']
block_13_expand_BN (BatchNorma (None, 14, 14, 576) 2304 ['block_13_expand[0][0]']
lization)
block_13_expand_relu (ReLU) (None, 14, 14, 576) 0 ['block_13_expand_BN[0][0]']
block_13_pad (ZeroPadding2D) (None, 15, 15, 576) 0 ['block_13_expand_relu[0][0]']
block_13_depthwise (DepthwiseC (None, 7, 7, 576) 5184 ['block_13_pad[0][0]']
onv2D)
block_13_depthwise_BN (BatchNo (None, 7, 7, 576) 2304 ['block_13_depthwise[0][0]']
rmalization)
block_13_depthwise_relu (ReLU) (None, 7, 7, 576) 0 ['block_13_depthwise_BN[0][0]']
block_13_project (Conv2D) (None, 7, 7, 160) 92160 ['block_13_depthwise_relu[0][0]']
block_13_project_BN (BatchNorm (None, 7, 7, 160) 640 ['block_13_project[0][0]']
alization)
block_14_expand (Conv2D) (None, 7, 7, 960) 153600 ['block_13_project_BN[0][0]']
block_14_expand_BN (BatchNorma (None, 7, 7, 960) 3840 ['block_14_expand[0][0]']
lization)
block_14_expand_relu (ReLU) (None, 7, 7, 960) 0 ['block_14_expand_BN[0][0]']
block_14_depthwise (DepthwiseC (None, 7, 7, 960) 8640 ['block_14_expand_relu[0][0]']
onv2D)
block_14_depthwise_BN (BatchNo (None, 7, 7, 960) 3840 ['block_14_depthwise[0][0]']
rmalization)
block_14_depthwise_relu (ReLU) (None, 7, 7, 960) 0 ['block_14_depthwise_BN[0][0]']
block_14_project (Conv2D) (None, 7, 7, 160) 153600 ['block_14_depthwise_relu[0][0]']
block_14_project_BN (BatchNorm (None, 7, 7, 160) 640 ['block_14_project[0][0]']
alization)
block_14_add (Add) (None, 7, 7, 160) 0 ['block_13_project_BN[0][0]',
'block_14_project_BN[0][0]']
block_15_expand (Conv2D) (None, 7, 7, 960) 153600 ['block_14_add[0][0]']
block_15_expand_BN (BatchNorma (None, 7, 7, 960) 3840 ['block_15_expand[0][0]']
lization)
block_15_expand_relu (ReLU) (None, 7, 7, 960) 0 ['block_15_expand_BN[0][0]']
block_15_depthwise (DepthwiseC (None, 7, 7, 960) 8640 ['block_15_expand_relu[0][0]']
onv2D)
block_15_depthwise_BN (BatchNo (None, 7, 7, 960) 3840 ['block_15_depthwise[0][0]']
rmalization)
block_15_depthwise_relu (ReLU) (None, 7, 7, 960) 0 ['block_15_depthwise_BN[0][0]']
block_15_project (Conv2D) (None, 7, 7, 160) 153600 ['block_15_depthwise_relu[0][0]']
block_15_project_BN (BatchNorm (None, 7, 7, 160) 640 ['block_15_project[0][0]']
alization)
block_15_add (Add) (None, 7, 7, 160) 0 ['block_14_add[0][0]',
'block_15_project_BN[0][0]']
block_16_expand (Conv2D) (None, 7, 7, 960) 153600 ['block_15_add[0][0]']
block_16_expand_BN (BatchNorma (None, 7, 7, 960) 3840 ['block_16_expand[0][0]']
lization)
block_16_expand_relu (ReLU) (None, 7, 7, 960) 0 ['block_16_expand_BN[0][0]']
block_16_depthwise (DepthwiseC (None, 7, 7, 960) 8640 ['block_16_expand_relu[0][0]']
onv2D)
block_16_depthwise_BN (BatchNo (None, 7, 7, 960) 3840 ['block_16_depthwise[0][0]']
rmalization)
block_16_depthwise_relu (ReLU) (None, 7, 7, 960) 0 ['block_16_depthwise_BN[0][0]']
block_16_project (Conv2D) (None, 7, 7, 320) 307200 ['block_16_depthwise_relu[0][0]']
block_16_project_BN (BatchNorm (None, 7, 7, 320) 1280 ['block_16_project[0][0]']
alization)
Conv_1 (Conv2D) (None, 7, 7, 1280) 409600 ['block_16_project_BN[0][0]']
Conv_1_bn (BatchNormalization) (None, 7, 7, 1280) 5120 ['Conv_1[0][0]']
out_relu (ReLU) (None, 7, 7, 1280) 0 ['Conv_1_bn[0][0]']
==================================================================================================
Total params: 2,257,984
Trainable params: 2,223,872
Non-trainable params: 34,112
__________________________________________________________________________________________________
#Freeze the base model layers
mobilenetv2.trainable = False
# Create the input tensor
inputs = tf.keras.Input(shape=(224, 224, 3))
# Apply the pre-trained base model to the inputs
x = mobilenetv2(inputs, training=False)
# Add a global average pooling layer
x = tf.keras.layers.GlobalAveragePooling2D()(x)
# Add the output layer with the desired number of classes
outputs = Dense(units=10, activation='softmax')(x)
# Create the final model
model_ = tf.keras.Model(inputs, outputs)
# get the new model summary
model_.summary()
Model: "model_7"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_16 (InputLayer) [(None, 224, 224, 3)] 0
mobilenetv2_1.00_224 (Funct (None, 7, 7, 1280) 2257984
ional)
global_average_pooling2d_11 (None, 1280) 0
(GlobalAveragePooling2D)
dense_15 (Dense) (None, 10) 12810
=================================================================
Total params: 2,270,794
Trainable params: 12,810
Non-trainable params: 2,257,984
_________________________________________________________________
# using a learning rate of 0.0001 and Adam optimizer
model_.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])
# Running the model using 10 epochs and saving the history
history = model_.fit(x=train_chunks,
steps_per_epoch=len(train_chunks), # determines how many batches are processed in one epoch,
validation_data=valid_chunks,
validation_steps=len(valid_chunks),
epochs=30,
verbose=2
)
Epoch 1/20 172/172 - 78s - loss: 0.4630 - accuracy: 0.8984 - val_loss: 0.4560 - val_accuracy: 0.9067 - 78s/epoch - 455ms/step Epoch 2/20 172/172 - 78s - loss: 0.4327 - accuracy: 0.9042 - val_loss: 0.4278 - val_accuracy: 0.9067 - 78s/epoch - 454ms/step Epoch 3/20 172/172 - 78s - loss: 0.4065 - accuracy: 0.9136 - val_loss: 0.4069 - val_accuracy: 0.9167 - 78s/epoch - 455ms/step Epoch 4/20 172/172 - 78s - loss: 0.3862 - accuracy: 0.9206 - val_loss: 0.3906 - val_accuracy: 0.9100 - 78s/epoch - 456ms/step Epoch 5/20 172/172 - 79s - loss: 0.3658 - accuracy: 0.9188 - val_loss: 0.3765 - val_accuracy: 0.9133 - 79s/epoch - 458ms/step Epoch 6/20 172/172 - 79s - loss: 0.3479 - accuracy: 0.9299 - val_loss: 0.3660 - val_accuracy: 0.9233 - 79s/epoch - 458ms/step Epoch 7/20 172/172 - 78s - loss: 0.3312 - accuracy: 0.9317 - val_loss: 0.3473 - val_accuracy: 0.9300 - 78s/epoch - 453ms/step Epoch 8/20 172/172 - 322s - loss: 0.3153 - accuracy: 0.9369 - val_loss: 0.3459 - val_accuracy: 0.9233 - 322s/epoch - 2s/step Epoch 9/20 172/172 - 82s - loss: 0.3036 - accuracy: 0.9363 - val_loss: 0.3288 - val_accuracy: 0.9267 - 82s/epoch - 478ms/step Epoch 10/20 172/172 - 79s - loss: 0.2901 - accuracy: 0.9422 - val_loss: 0.3143 - val_accuracy: 0.9333 - 79s/epoch - 461ms/step Epoch 11/20 172/172 - 78s - loss: 0.2784 - accuracy: 0.9457 - val_loss: 0.3015 - val_accuracy: 0.9400 - 78s/epoch - 454ms/step Epoch 12/20 172/172 - 78s - loss: 0.2673 - accuracy: 0.9509 - val_loss: 0.3003 - val_accuracy: 0.9367 - 78s/epoch - 454ms/step Epoch 13/20 172/172 - 79s - loss: 0.2583 - accuracy: 0.9486 - val_loss: 0.2924 - val_accuracy: 0.9367 - 79s/epoch - 459ms/step Epoch 14/20 172/172 - 79s - loss: 0.2477 - accuracy: 0.9544 - val_loss: 0.2830 - val_accuracy: 0.9333 - 79s/epoch - 457ms/step Epoch 15/20 172/172 - 79s - loss: 0.2394 - accuracy: 0.9539 - val_loss: 0.2807 - val_accuracy: 0.9333 - 79s/epoch - 459ms/step Epoch 16/20 172/172 - 79s - loss: 0.2311 - accuracy: 0.9579 - val_loss: 0.2697 - val_accuracy: 0.9433 - 79s/epoch - 457ms/step Epoch 17/20 172/172 - 79s - loss: 0.2237 - accuracy: 0.9550 - val_loss: 0.2663 - val_accuracy: 0.9433 - 79s/epoch - 458ms/step Epoch 18/20 172/172 - 78s - loss: 0.2162 - accuracy: 0.9603 - val_loss: 0.2645 - val_accuracy: 0.9333 - 78s/epoch - 451ms/step Epoch 19/20 172/172 - 79s - loss: 0.2087 - accuracy: 0.9620 - val_loss: 0.2624 - val_accuracy: 0.9333 - 79s/epoch - 459ms/step Epoch 20/20 172/172 - 79s - loss: 0.2025 - accuracy: 0.9638 - val_loss: 0.2556 - val_accuracy: 0.9433 - 79s/epoch - 457ms/step
# real classes
test_labels = test_chunks.classes
# inference
predictions = model_.predict(x=test_chunks, steps=len(test_chunks), verbose=0)
# confusion matrix for more details
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_true=test_labels, y_pred=predictions.argmax(axis=1))
# plot the confusion matrix
import seaborn as sns
sns.heatmap(cm, annot=True)
<AxesSubplot:>
### Save the model to your local disk
model_.save('SignDigitsModel.h5')
By: Ismail Ouahbi
06/07/2023
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