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import os
import urllib.request as request
from zipfile import ZipFile
import tensorflow as tf
import time
from cnnClassfier.entity.config_entity import TrainingConfig
from pathlib import Path
class Training:
def __init__(self, config: TrainingConfig):
self.config = config
def get_base_model(self):
self.model = tf.keras.models.load_model(
self.config.updated_base_model_path
)
def train_valid_generator(self):
datagenerator_kwargs = dict(
rescale = 1./255,
validation_split=0.20
)
dataflow_kwargs = dict(
target_size=self.config.params_image_size[:-1],
batch_size=self.config.params_batch_size,
interpolation="bilinear"
)
valid_datagenerator = tf.keras.preprocessing.image.ImageDataGenerator(
**datagenerator_kwargs
)
self.valid_generator = valid_datagenerator.flow_from_directory(
directory=self.config.training_data,
subset="validation",
shuffle=False,
**dataflow_kwargs
)
if self.config.params_is_augmentation:
train_datagenerator = tf.keras.preprocessing.image.ImageDataGenerator(
rotation_range=40,
horizontal_flip=True,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
**datagenerator_kwargs
)
else:
train_datagenerator = valid_datagenerator
self.train_generator = train_datagenerator.flow_from_directory(
directory=self.config.training_data,
subset="training",
shuffle=True,
**dataflow_kwargs
)
@staticmethod
def save_model(path: Path, model: tf.keras.Model):
model.save(path)
def train(self, callback_list: list):
self.steps_per_epoch = self.train_generator.samples // self.train_generator.batch_size
self.validation_steps = self.valid_generator.samples // self.valid_generator.batch_size
self.model.fit(
self.train_generator,
epochs=self.config.params_epochs,
steps_per_epoch=self.steps_per_epoch,
validation_steps=self.validation_steps,
validation_data=self.valid_generator,
callbacks=callback_list
)
self.save_model(
path=self.config.trained_model_path,
model=self.model
)
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