classifier.py

import numpy as np
import PIL
import tensorflow as tf
import pathlib


from tensorflow.keras import layers
from tensorflow.keras.models import Sequential

data_dir = '/home/praem90/packages/EachOneTeachOne/ImageClassification/Grapevine_Leaves_Image_Dataset';
batch_size = 8
img_height = 511
img_width = 511

train_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

val_ds = tf.keras.utils.image_dataset_from_directory(
  '/home/praem90/packages/EachOneTeachOne/ImageClassification/Training set Oneplus8',
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

class_names = train_ds.class_names
print(class_names)



val_batches = tf.data.experimental.cardinality(val_ds)
test_dataset = val_ds.take(val_batches // 5)
validation_dataset = val_ds.skip(val_batches // 5)

AUTOTUNE = tf.data.AUTOTUNE

train_dataset = train_ds.prefetch(buffer_size=AUTOTUNE)
validation_dataset = validation_dataset.prefetch(buffer_size=AUTOTUNE)
test_dataset = test_dataset.prefetch(buffer_size=AUTOTUNE)


data_augmentation = tf.keras.Sequential([
  layers.RandomFlip("horizontal_and_vertical"),
  layers.RandomRotation(0.2),
])

resize_and_rescale = tf.keras.Sequential([
  layers.Resizing(img_width, img_height),
  layers.Rescaling(1./255)
])



num_classes = len(class_names)

model = Sequential([
  layers.Rescaling(1./127.5, offset=-1, input_shape=(img_height, img_width, 3)),
  layers.RandomFlip("horizontal_and_vertical"),
  layers.RandomRotation(0.2),
  layers.Conv2D(16, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(32, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(64, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Flatten(),
  layers.Dense(128, activation='relu'),
  layers.Dense(num_classes)
])

base_learning_rate = 0.0001
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.summary()


epochs=5
history = model.fit(
  train_ds,
  validation_data=val_ds,
  epochs=epochs
)

model.save('./model');