image_classification_tensorflow_model_for_andriod_app.py

# -*- coding: utf-8 -*-
"""Image Classification TensorFlow Model for Andriod app.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1BlXxAgAt3gEH5e4apm76REFgv06V-L5G
"""

from google.colab import drive
drive.mount('/content/gdrive')

!unzip '/content/gdrive/MyDrive/data/data.zip'

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
    rescale=1./255, # Normalize pixel values between 0 and 1
    rotation_range=10, # Rotate the image randomly between -10 and 10 degrees
    shear_range=0.2, # Apply shear transformation with a shear intensity of 0.2
    zoom_range=0.2, # Apply zoom randomly between 0.8 and 1.2
    horizontal_flip=True # Flip the image horizontally
)

val_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    'data/train', # Directory of training data
    target_size=(224, 224), # Resize the image to 224 x 224
    batch_size=200, # Number of images to process at once
    class_mode='categorical' # Categorical classification (more than 2 classes)
)

validation_generator = val_datagen.flow_from_directory(
    'data/val', # Directory of validation data
    target_size=(224, 224),
    batch_size=200,
    class_mode='categorical'
)
test_generator = test_datagen.flow_from_directory(
    'data/test', # Directory of validation data
    target_size=(224, 224),
    batch_size=200,
    class_mode='categorical'
)

import tensorflow as tf
import matplotlib.pyplot as plt

img_height, img_width = 224,224 
batch_size = 32

train_ds = tf.keras.utils.image_dataset_from_directory(
    "data/train",
    image_size = (img_height, img_width),
    batch_size = batch_size
)
val_ds = tf.keras.utils.image_dataset_from_directory(
    "data/val",
    image_size = (img_height, img_width),
    batch_size = batch_size
)
test_ds = tf.keras.utils.image_dataset_from_directory(
    "data/test",
    image_size = (img_height, img_width),
    batch_size = batch_size
)

class_names = ["apple","banana","cat","crow","daisy","dandelion","dog","kingfisher","orange","peacock","sunflower","tulip"]
plt.figure(figsize=(10,10))
for images, labels in train_ds.take(1):
  for i in range(9):
    ax = plt.subplot(3, 3, i + 1)
    plt.imshow(images[i].numpy().astype("uint8"))
    plt.title(class_names[labels[i]])
    plt.axis("off")

# model = tf.keras.Sequential([
#     tf.keras.layers.Conv2D(32,3,activation='relu',input_shape=(224,224,3)),
#     # tf.keras.layers.BatchNormalization(),
#     tf.keras.layers.MaxPooling2D(2, 2),
#     tf.keras.layers.Conv2D(64,3,activation='relu'),
#     # tf.keras.layers.BatchNormalization(),
#     tf.keras.layers.MaxPooling2D(2,2),
#     tf.keras.layers.Conv2D(64,3,activation='relu'),
#     # tf.keras.layers.BatchNormalization(),
#     tf.keras.layers.MaxPooling2D(2,2),
#     tf.keras.layers.Flatten(),
#     tf.keras.layers.Dense(128, activation='relu'),
#     tf.keras.layers.Dropout(0.5),
#     tf.keras.layers.Dense(256, activation='relu'),
#     tf.keras.layers.Dropout(0.2),
#     tf.keras.layers.Dense(256,activation='relu'),
#     tf.keras.layers.Dense(12, activation='softmax')
# ])

from keras.applications.vgg16 import VGG16
conv_base = VGG16(
    weights='imagenet',
    include_top = False,
    input_shape=(224,224,3)
)

from keras import Sequential
from keras.layers import Dense,Flatten
model = Sequential()

model.add(conv_base)
model.add(Flatten())
model.add(Dense(256,activation='relu'))
model.add(Dense(12,activation='softmax'))

conv_base.trainable = False

model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

# Train the model
history = model.fit(train_generator,
                    epochs=10,
                    validation_data=validation_generator)

# model.save('model.h5')

model.evaluate(test_generator)

plt.plot(history.history['accuracy'], label='train acc')
plt.plot(history.history['val_accuracy'], label='val acc')
plt.legend()
plt.show()


plt.plot(history.history['loss'], label='train loss')
plt.plot(history.history['val_loss'], label='val loss')
plt.legend()
plt.show()

import numpy

plt.figure(figsize=(10,10))
for images, labels in test_ds.take(1):
  classifications = model(images)
  # print(classifications)
  
  
  for i in range(9):
    ax = plt.subplot(3, 3, i + 1)
    plt.imshow(images[i].numpy().astype("uint8"))
    index = numpy.argmax(classifications[i])
    plt.title("Pred: " + class_names[index] + " | Real: " + class_names[labels[i]])

converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

with open("model.tflite", 'wb') as f:
  f.write(tflite_model)