AI – cats and dogs classifier

Recently I participated in very interesting workshop on Technical University in GDN. Workshop was lead by Tomasz Kocejko from faculty: Medical Telematics. This workshop presented possibilites of using Machine Learning models on mobile devices. More info:

https://aibay.ai/wp-content/uploads/2020/11/kursy_Inferencja_Android_DIH.pdf

This is only an example what could be achieved with Transfer Learning. The goal is:

  • creation of mobile app in language Dart using framework Flutter (new Google language for creating mobile apps), which will acquire data from camera or gallery and on this basis classify image. Images will be classified using binary perceptron, which will classify data into 2 sets: cats and dogs and when classification is uncertain unknown. This will be achieved by transfer learning from ready-to-use Mobilenet model.

For best results use computer with following configuration:

  • Win 10
  • 32GB RAM
  • 1TB SSD Disk
  • AMD Ryzen 7, 8 cores

First of all install:

  1. Android Studio
  2. Flutter
  3. Anaconda
  4. Visual Studio Code

Open Anaconda terminal and type:

conda create --name NameOfYourEnv

Then install following packages :

conda install -y _libgcc_mutex
 conda install -y  _openmp_mutex
 conda install -y  _r-mutex
 conda install -y  _tflow_select
 conda install -y  absl-py
 conda install -y  aiohttp
 conda install -y  alabaster
 conda install -y  anyio
 conda install -y  argh
 conda install -y  argon2-cffi
 conda install -y  astor
 conda install -y  astroid
 conda install -y  async-timeout
 conda install -y  async_generator
 conda install -y  atomicwrites
 conda install -y  attrs
 conda install -y  autopep8
 conda install -y  babel
 conda install -y  backcall
 conda install -y  backports
 conda install -y  backports.shutil_get_terminal_size
 conda install -y  binutils_impl_linux-64
 conda install -y  binutils_linux-64
 conda install -y  blas
 conda install -y  bleach
 conda install -y  blinker
 conda install -y  bokeh
 conda install -y  brotlipy
 conda install -y  bwidget
 conda install -y  bzip2
 conda install -y  c-ares
 conda install -y  ca-certificates
 conda install -y  cachetools
 conda install -y  cairo
 conda install -y  certifi
 conda install -y  cffi
 conda install -y  chardet
 conda install -y  click
 conda install -y  cloudpickle
 conda install -y  conda
 conda install -y  conda-package-handling
 conda install -y  cryptography
 conda install -y  curl
 conda install -y  cycler
 conda install -y  cyrus-sasl
 conda install -y  cython
 conda install -y  cytoolz
 conda install -y  dask
 conda install -y  dask-core
 conda install -y  dbus
 conda install -y  decorator
 conda install -y  defusedxml
 conda install -y  diff-match-patch
 conda install -y  distributed
 conda install -y  docutils
 conda install -y  entrypoints
 conda install -y  expat
 conda install -y  fastcache
 conda install -y  ffmpeg
 conda install -y  flake8
 conda install -y  font-ttf-dejavu-sans-mono
 conda install -y  font-ttf-inconsolata
 conda install -y  font-ttf-source-code-pro
 conda install -y  font-ttf-ubuntu
 conda install -y  fontconfig
 conda install -y  fonts-anaconda
 conda install -y  freeglut
 conda install -y  freetype
 conda install -y  fribidi
 conda install -y  future
 conda install -y  gast
 conda install -y  gcc-dbg_linux-64
 conda install -y  gcc_impl_linux-64
 conda install -y  gcc_linux-64
 conda install -y  gettext
 conda install -y  gfortran-dbg_linux-64
 conda install -y  gfortran_impl_linux-64
 conda install -y  gfortran_linux-64
 conda install -y  glib
 conda install -y  gmp
 conda install -y  gmpy2
 conda install -y  google-auth
 conda install -y  google-auth-oauthlib
 conda install -y  google-pasta
 conda install -y  graphite2
 conda install -y  grpcio
 conda install -y  gsl
 conda install -y  gst-plugins-base
 conda install -y  gst-plugins-good
 conda install -y  gstreamer
 conda install -y  gxx_impl_linux-64
 conda install -y  gxx_linux-64
 conda install -y  h5py
 conda install -y  harfbuzz
 conda install -y  hdf5
 conda install -y  heapdict
 conda install -y  html5lib
 conda install -y  icu
 conda install -y  idna
 conda install -y  imageio
 conda install -y  imagesize
 conda install -y  importlib-metadata
 conda install -y  importlib_metadata
 conda install -y  intel-openmp
 conda install -y  intervaltree
 conda install -y  ipykernel
 conda install -y  ipyparallel
 conda install -y  ipython
 conda install -y  ipython_genutils
 conda install -y  ipywidgets
 conda install -y  isort
 conda install -y  jasper
 conda install -y  jedi
 conda install -y  jeepney
 conda install -y  jinja2
 conda install -y  joblib
 conda install -y  jpeg
 conda install -y  json5
 conda install -y  jsonschema
 conda install -y  jupyter
 conda install -y  jupyter_client
 conda install -y  jupyter_console
 conda install -y  jupyter_core
 conda install -y  jupyter_server
 conda install -y jupyterlab
 conda install -y  jupyterlab_pygments
 conda install -y  jupyterlab_server
 conda install -y keras
 conda install -y keras-applications
 conda install -y keras-base
 conda install -y keras-preprocessing
 conda install -y keyring
 conda install -y kiwisolver
 conda install -y krb5
 conda install -y lazy-object-proxy
 conda install -y lcms2
 conda install -y ld_impl_linux-64
 conda install -y libcurl
 conda install -y libdb
 conda install -y libedit
 conda install -y libffi
 conda install -y libgcc-ng
 conda install -y libgfortran-ng
 conda install -y libglu
 conda install -y libgomp
 conda install -y libiconv
 conda install -y libntlm
 conda install -y libopencv
 conda install -y libopus
 conda install -y libpng
 conda install -y libprotobuf
 conda install -y libsodium
 conda install -y libspatialindex
 conda install -y libssh2
 conda install -y libstdcxx-ng
 conda install -y libtiff
 conda install -y libuuid
 conda install -y libvpx
 conda install -y libxcb
 conda install -y libxml2
 conda install -y llvmlite
 conda install -y locket
 conda install -y lz4-c
 conda install -y make
 conda install -y markdown
 conda install -y markdown2
 conda install -y markupsafe
 conda install -y matplotlib
 conda install -y matplotlib-base
 conda install -y mccabe
 conda install -y mistune
 conda install -y mkl
 conda install -y mkl-service
 conda install -y mkl_fft
 conda install -y mkl_random
 conda install -y mpc
 conda install -y mpfr
 conda install -y mpmath
 conda install -y msgpack-python
 conda install -y multidict
 conda install -y nbclassic
 conda install -y nbclient
 conda install -y nbconvert
 conda install -y nbformat
 conda install -y ncurses
 conda install -y nest-asyncio
 conda install -y networkx
 conda install -y nose
 conda install -y notebook
 conda install -y numba
 conda install -y numexpr
 conda install -y numpy
 conda install -y numpy-base
 conda install -y numpydoc
 conda install -y oauthlib
 conda install -y olefile
 conda install -y opencv
 conda install -y openjdk
 conda install -y openssl
 conda install -y opt_einsum
 conda install -y packaging
 conda install -y pandas
 conda install -y pandoc
 conda install -y pandocfilters
 conda install -y pango
 conda install -y parso
 conda install -y partd
 conda install -y path
 conda install -y path.py
 conda install -y pathtools
 conda install -y patsy
 conda install -y pcre
 conda install -y pexpect
 conda install -y pickleshare
 conda install -y pillow
 conda install -y pip
 conda install -y pixman
 conda install -y pluggy
 conda install -y portaudio
 conda install -y prometheus_client
 conda install -y prompt-toolkit
 conda install -y prompt_toolkit
 conda install -y protobuf
 conda install -y psutil
 conda install -y ptyprocess
 conda install -y py-opencv
 conda install -y pyasn1
 conda install -y pyasn1-modules
 conda install -y pyaudio
 conda install -y pycodestyle
 conda install -y pycosat
 conda install -y pycparser
 conda install -y pydocstyle
 conda install -y pyflakes
 conda install -y pygments
 conda install -y pyjwt
 conda install -y pylint
 conda install -y pyopenssl
 conda install -y pyparsing
 conda install -y pyqt
 conda install -y pyrsistent
 conda install -y pysocks
 conda install -y python=3.7.0
 conda install -y python-dateutil
 conda install -y python-jsonrpc-server
 conda install -y python-language-server
 conda install -y python_abi
 conda install -y pytz
 conda install -y pywavelets
 conda install -y pyxdg
 conda install -y pyyaml
 conda install -y pyzmq
 conda install -y qdarkstyle
 conda install -y qt
 conda install -y qtawesome
 conda install -y qtconsole
 conda install -y qtpy
 conda install -y r-abind
 conda install -y r-askpass
 conda install -y r-assertthat
 conda install -y r-backports
 conda install -y r-base
 conda install -y r-base64enc
 conda install -y r-bh
 conda install -y r-bit
 conda install -y r-bit64
 conda install -y r-bitops
 conda install -y r-blob
 conda install -y r-boot
 conda install -y r-broom
 conda install -y r-callr
 conda install -y r-car
 conda install -y r-cardata
 conda install -y r-catools
 conda install -y r-cellranger
 conda install -y r-cli
 conda install -y r-clipr
 conda install -y r-colorspace
 conda install -y r-config
 conda install -y r-corrplot
 conda install -y r-cowplot
 conda install -y r-crayon
 conda install -y r-curl
 conda install -y r-data.table
 conda install -y r-dbi
 conda install -y r-dbplyr
 conda install -y r-desc
 conda install -y r-digest
 conda install -y r-dplyr
 conda install -y r-ellipsis
 conda install -y r-evaluate
 conda install -y r-fansi
 conda install -y r-farver
 conda install -y r-forcats
 conda install -y r-foreign
 conda install -y r-forge
 conda install -y r-generics
 conda install -y r-ggplot2
 conda install -y r-ggpubr
 conda install -y r-ggrepel
 conda install -y r-ggsci
 conda install -y r-ggsignif
 conda install -y r-glue
 conda install -y r-gridextra
 conda install -y r-gtable
 conda install -y r-haven
 conda install -y r-highr
 conda install -y r-hms
 conda install -y r-htmltools
 conda install -y r-htmlwidgets
 conda install -y r-httpuv
 conda install -y r-httr
 conda install -y r-isoband
 conda install -y r-jsonlite
 conda install -y r-knitr
 conda install -y r-labeling
 conda install -y r-later
 conda install -y r-lattice
 conda install -y r-lifecycle
 conda install -y r-lme4
 conda install -y r-magrittr
 conda install -y r-maptools
 conda install -y r-markdown
 conda install -y r-mass
 conda install -y r-matrix
 conda install -y r-matrixmodels
 conda install -y r-mgcv
 conda install -y r-mime
 conda install -y r-miniui
 conda install -y r-minqa
 conda install -y r-mongolite
 conda install -y r-munsell
 conda install -y r-nlme
 conda install -y r-nloptr
 conda install -y r-nnet
 conda install -y r-odbc
 conda install -y r-openssl
 conda install -y r-openxlsx
 conda install -y r-packrat
 conda install -y r-pbkrtest
 conda install -y r-pillar
 conda install -y r-pkgbuild
 conda install -y r-pkgconfig
 conda install -y r-pkgload
 conda install -y r-pki
 conda install -y r-plyr
 conda install -y r-polynom
 conda install -y r-praise
 conda install -y r-prettyunits
 conda install -y r-processx
 conda install -y r-profvis
 conda install -y r-progress
 conda install -y r-promises
 conda install -y r-ps
 conda install -y r-purrr
 conda install -y r-quantreg
 conda install -y r-r2d3
 conda install -y r-r6
 conda install -y r-rappdirs
 conda install -y r-rcolorbrewer
 conda install -y r-rcpp
 conda install -y r-rcppeigen
 conda install -y r-rcurl
 conda install -y r-readr
 conda install -y r-readxl
 conda install -y r-rematch
 conda install -y r-reshape2
 conda install -y r-rio
 conda install -y r-rjava
 conda install -y r-rjdbc
 conda install -y r-rjsonio
 conda install -y r-rlang
 conda install -y r-rmarkdown
 conda install -y r-rprojroot
 conda install -y r-rsconnect
 conda install -y r-rstatix
 conda install -y r-rstudioapi
 conda install -y r-scales
 conda install -y r-shiny
 conda install -y r-sourcetools
 conda install -y r-sp
 conda install -y r-sparklyr
 conda install -y r-sparsem
 conda install -y r-statmod
 conda install -y r-stringi
 conda install -y r-stringr
 conda install -y r-sys
 conda install -y r-testthat
 conda install -y r-tibble
 conda install -y r-tidyr
 conda install -y r-tidyselect
 conda install -y r-tinytex
 conda install -y r-utf8
 conda install -y r-vctrs
 conda install -y r-viridislite
 conda install -y r-withr
 conda install -y r-xfun
 conda install -y r-xml2
 conda install -y r-xtable
 conda install -y r-yaml
 conda install -y r-zeallot
 conda install -y r-zip
 conda install -y readline
 conda install -y requests
 conda install -y requests-oauthlib
 conda install -y rope
 conda install -y rsa
 conda install -y rstudio
 conda install -y rtree
 conda install -y ruamel
 conda install -y ruamel.yaml
 conda install -y ruamel.yaml.clib
 conda install -y ruamel_yaml
 conda install -y scikit-image
 conda install -y scikit-learn
 conda install -y scipy
 conda install -y seaborn
 conda install -y secretstorage
 conda install -y send2trash
 conda install -y setuptools
 conda install -y simplegeneric
 conda install -y sip
 conda install -y six
 conda install -y sniffio
 conda install -y snowballstemmer
 conda install -y sortedcontainers
 conda install -y sphinx
 conda install -y sphinxcontrib-applehelp
 conda install -y sphinxcontrib-devhelp
 conda install -y sphinxcontrib-htmlhelp
 conda install -y sphinxcontrib-jsmath
 conda install -y sphinxcontrib-qthelp
 conda install -y sphinxcontrib-serializinghtml
 conda install -y spyder
 conda install -y spyder-kernels
 conda install -y sqlite
 conda install -y statsmodels
 conda install -y sympy
 conda install -y tbb
 conda install -y tblib
 conda install -y tensorboard
 conda install -y tensorboard-plugin-wit
 conda install -y tensorflow
 conda install -y tensorflow-base
 conda install -y tensorflow-estimator=2.1.0 
 conda install -y termcolor
 conda install -y terminado
 conda install -y testpath
 conda install -y tk
 conda install -y tktable
 conda install -y toml
 conda install -y toolz
 conda install -y tornado
 conda install -y tqdm
 conda install -y traitlets
 conda install -y typed-ast
 conda install -y typing-extensions
 conda install -y typing_extensions
 conda install -y ujson
 conda install -y unixodbc
 conda install -y urllib3
 conda install -y watchdog
 conda install -y wcwidth
 conda install -y webencodings
 conda install -y werkzeug
 conda install -y wfdb
 conda install -y wheel
 conda install -y widgetsnbextension
 conda install -y wrapt
 conda install -y wurlitzer
 conda install -y xlrd
 conda install -y xz
 conda install -y yaml
 conda install -y yapf
 conda install -y yarl
 conda install -y zeromq
 conda install -y zict
 conda install -y zipp
 conda install -y zlib
 conda install -y zstd

Next create GUI design using lovely prototyping designer: https://www.figma.com/

Open Visual Studio Code and install Flutter extension as follow:

Now create new Flutter app by:

press CTRL+SHIFT+P

and then chose:

In Android Studio create new AVD as follow:

Copy some files to emulator:

Start emulator.

In Visual Studio Code choose Run->Start debugging. Choose emulator as destination.

Your app should look like this:

Now every change in code will be visible on emulator! Simple as it is!

Language Dart is very similar to CSS. It assumes container construction of apps – here is sample of code:

As you can see every element of GUI can be embeded in another one. What is astonishing that in comparison to Java, coding of events like „onPress”, „onTap” is super easy. It is similar to Delphi or even CSS rather than other high-level programming languages.

What is also interesting that Dart allows you to write code simultaneously on two systems: Android and iOS.

Some more interesting info:

All documentation to Flutter and Dart can be found here: https://flutter.dev/docs

When we have our lovely app created – CatsAndDogsClassifier.

It is time to create our model in JupyterLab. Now it will be the most interesting part ;):

To open JupyterLab with chosen disk on Windows-please follow these steps:

open Anaconda terminal and type

conda activate YourEnv
cd D:\YouFolder
jupyter Lab

JupyterLab will be opened then use this notebook.

This notebook is used for classification images acc. to MobileNet model. More info about this model: https://arxiv.org/abs/1704.04861. It recognizes images related to: animals, plants, things.

#import proper libraries
 import tensorflow as tf
 import IPython
 from IPython.display import Image
 import cv2
 import numpy as np
 from tensorflow.keras.applications import imagenet_utils
 from tensorflow.keras import backend as K
 from tensorflow.keras.layers import Dense, Activation
 
#print TensorFlow version=should be 2.1
 print(tf.version)

# load MobileNet model from internet - it is model trained on 100 000 of images - animals, things, plants
 model = tf.keras.applications.mobilenet.MobileNet()

# it gives overview of used layers in this Net -more info in this book : https://www.amazon.com/Advanced-Deep-Learning-TensorFlow-Keras-ebook/dp/B0851D5YQQ
 model.summary()

# read lables
 labels = open('mobilenet/labels.txt', 'r')
 listLabels = labels.readlines()
 print(listLabels)

# load model from the file
 interpreter = tf.lite.Interpreter(model_path="mobilenet/mobilenet.tflite")

# details of used CNN network
 input_details = interpreter.get_input_details()
 output_details = interpreter.get_output_details()
 print(input_details)
 print(output_details)

 interpreter.allocate_tensors()

# read image, which should be classified from file
 filename = 'data/cat.jpg'
 img = cv2.imread(filename)
 Image(filename=filename)

# resize this image-it should have dimensions 224x224 and be in format:uint8 (net was trained on this kind of data)
 img = cv2.imread(filename)
 new_img = cv2.resize(img, (224,224))
 new_img = new_img.astype('float32')
 new_img = new_img.astype('uint8')

# put image as source data for net
 interpreter.set_tensor(input_details[0]['index'], [new_img])
 interpreter.invoke()

# retrieve the result as tensor
 this model returns several possible unnormalized answers - the most probably is returned as max value from this tensor
 output_data = interpreter.get_tensor(output_details[0]['index'])
 output_data[0]

 from matplotlib import pyplot as plt
 plt.plot(output_data[0])

# here is the max answer
 max(output_data[0])
 max_index_row = np.argmax(output_data, axis=1)
 print(max_index_row[0])
 output_data[0][0]

# here are all possible answers and their unnormalized weights
 for i in range(len(output_data[0])):
     if output_data[0][i]>10:
         print(i)
         print(output_data[0][i])
         print(listLabels[i])

# and finally label for this answer
 listLabels[max_index_row[0]]        

Now it is time for transfer learning. What is transfer learning then?

Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.[1] For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. This area of research bears some relation to the long history of psychological literature on transfer of learning, although formal ties between the two fields are limited.

Approach to Transfer Learning. … The basic premise of transfer learning is simple: take a model trained on a large dataset and transfer its knowledge to a smaller dataset. For object recognition with a CNN, we freeze the early convolutional layers of the network and only train the last few layers which make a prediction.

We will use following notebook:

import tensorflow as tf
 import tensorflow.keras
 from tensorflow.keras import Model
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense, Input, add
 from tensorflow.keras.layers import Flatten, Activation
 from tensorflow.keras.layers import Dropout
 from tensorflow.keras.layers import AveragePooling1D, AveragePooling2D, MaxPooling2D
 from tensorflow.keras.layers import Conv1D, Conv2D, BatchNormalization, LSTM
 from tensorflow.keras.layers import MaxPooling1D, Lambda, Dropout
 from tensorflow.keras.utils import to_categorical
 from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
 from tensorflow.keras.applications import inception_v3
 from tensorflow.keras.applications.inception_v3 import InceptionV3
 from tensorflow.keras.applications.inception_v3 import preprocess_input as inception_v3_preprocessor
 from tensorflow.keras.models import Sequential, Model
 from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout
 from tensorflow.keras import optimizers
 from tensorflow.keras.optimizers import Adam
 import pandas as pd
 from pathlib import Path
 import os.path
 import os
 from sklearn.metrics import r2_score
 from sklearn.model_selection import train_test_split
 import pandas as pd
 from scipy.ndimage.interpolation import shift
 from math import *
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy.fft import rfft, rfftfreq, irfft
 from scipy.signal import find_peaks
 from scipy import signal, fftpack
 from scipy.signal import savgol_filter
 from sklearn.metrics import mean_squared_error, mean_absolute_error 

#this is special model used only for transfer learning
 def create_model():
 base_model = InceptionV3(weights = 'imagenet', include_top=False, input_shape=(224, 224, 3)) x = base_model.output x = GlobalAveragePooling2D(name='avg_pool')(x) x = Dropout(0.3)(x) x = Dense(4096, activation='relu')(x) x = Dropout(0.3)(x) x = Dense(2048, activation='relu')(x) x = Dropout(0.3)(x) x = Dense(1024, activation='relu')(x) x = Dropout(0.3)(x) x = Dense(512, activation='relu')(x) x = Dropout(0.3)(x) predictions = Dense(1, activation='sigmoid')(x) model = Model(inputs=base_model.input, outputs=predictions) for layer in base_model.layers[:-10]:     layer.trainable = False model.compile(Adam(lr=0.00001), loss='binary_crossentropy', metrics=['accuracy']) 
return model

#second version of this model using different loss function
 def create_model2():
 base_model = InceptionV3(weights = 'imagenet', include_top=False, input_shape=(224, 224, 3)) x = base_model.output x = GlobalAveragePooling2D(name='avg_pool')(x) x = Dropout(0.3)(x) x = Dense(4096, activation='relu')(x) x = Dropout(0.3)(x) x = Dense(2048, activation='relu')(x) x = Dropout(0.3)(x) x = Dense(1024, activation='relu')(x) x = Dropout(0.3)(x) x = Dense(512, activation='relu')(x) x = Dropout(0.3)(x) predictions = Dense(2, activation='softmax')(x) model = Model(inputs=base_model.input, outputs=predictions) for layer in base_model.layers[:-10]:     layer.trainable = False model.compile(Adam(lr=0.00001), loss='categorical_crossentropy', metrics=['accuracy']) 
return model

# Set filepath to the image database
 images_dir = Path('PetImages/')
 
#Select only .jpg files
 im_dir = list(images_dir.glob(r'*/.jpg'))    
 imagepath = pd.Series(im_dir, name='Filepath').astype(str)

# labels read from folder structure
 labels = pd.Series(imagepath.apply(lambda x: os.path.split(os.path.split(x)[0])[1]), name='label').astype(str)

# Cat detector (if Cat is in the picture = True, else Dog = False)
 labels2 = labels == 'Cat'

# Dataframe
 images = pd.concat([imagepath, labels2], axis=1).sample(frac=1.0, random_state=1).reset_index(drop=True)
 image_df = images.sample(5000, random_state=1).reset_index(drop=True)

# Training, Validation and Test set
 train_df, test_df = train_test_split(image_df, train_size=0.7, shuffle=True, random_state=1)
 train_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255, validation_split=0.2)
 test_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)

#it is number of iterations of learning the net
 epoch_no = 5
#how many images should be provided to net at one iteration
 batch_s = 8

#extract the train images
 train_images = train_generator.flow_from_dataframe(
         dataframe=train_df,
         x_col='Filepath',
         y_col='label',
         target_size=(224, 224),
         color_mode='rgb',
         class_mode='raw',
         batch_size=batch_s,
         shuffle=True,
         seed=42,
         subset='training'
 )
 
#extract the validation images​
 val_images = train_generator.flow_from_dataframe(
         dataframe=train_df,
         x_col='Filepath',
         y_col='label',
         target_size=(224, 224),
         color_mode='rgb',
         class_mode='raw',
         batch_size=batch_s,
         shuffle=True,
         seed=42,
         subset='validation'
 )
 
#extract test images​
 test_images = test_generator.flow_from_dataframe(
         dataframe=test_df,
         x_col='Filepath',
         y_col='label',
         target_size=(224, 224),
         color_mode='rgb',
         class_mode='raw',
         batch_size=batch_s,
         shuffle=False
 )

 model = create_model()
 model.summary()

#stop learning using best results. It prevents from overfitting
 cb_checkpointer = ModelCheckpoint(filepath = 'modelinc_ssondl21.hdf5', monitor = 'val_loss', save_best_only = True, mode = 'auto')

# train the net
 history = model.fit(train_images, validation_data=val_images, epochs=epoch_no, callbacks=[cb_checkpointer])
 
#this saves trained model to file on disk
 model.load_weights('modelinc.hdf5')
 
#let's check what predictions model returns
 pred = np.squeeze(model.predict(test_images))
 y_test = test_images.labels
 y_test = y_test.astype(int)
 
from sklearn.metrics import confusion_matrix

# this matrix allows to calculate efficiency of net
 confusion_matrix(y_test, pred.round())

More info about confusion matrix: https://en.wikipedia.org/wiki/Confusion_matrix

Now when we have trained model and saved to disk we can use it further on mobile device.

One more step is needed before using saved model on mobile device-conversion of model to tflite format. To achieve this use this notebook:

import tensorflow as tf

#read model from file hdf5
 model = tf.keras.models.load_model('bestInc_skin.hdf5')
 
#convert it to tflite
 converter = tf.lite.TFLiteConverter.from_keras_model(model)
 tflite_model = converter.convert()

#save it as .tflite
 open("skin.tflite", "wb").write(tflite_model)

Jupyter notebooks are available here.

Here are the steps to use model in mobile app:

And the final result:

a) For cat – probability 98%

b) for unknown probability ~50%

c) dog – probability ~0.02% (probability that it is a cat)

1 myśl na “AI – cats and dogs classifier

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