GNetTrainer - Application for training and predicting classification models.
Official Repository for GNetTrainer.
You can find the Package here- https://pypi.org/project/GNetTrainer/
Author
- Rishav Dash
- Mail me at - 9930046@gmail.com
- Github - https://github.com/Rishav-hub/
About GNetTrainer
GNetTrainer is a Deep Learning web application for training and predicting classification models which is written in Python 3. At the backend it uses Frameworks like Tensorflow 2.0 and Keras. You can train any classification model with any data using GNetTrainer without writing any line of code.
Motivation
The main aim is to make something like Keras which is a high-level neural network library that runs on top of TensorFlow.
How Image Classification looks using Keras
Import the required libraries
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout
from tensorflow.keras import optimizers
from tensorflow.keras.applications.densenet import DenseNet169, preprocess_input, decode_predictions
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import cv2Define Paths
ROOT = 'H:\\Parsonal\\Coding Practice\\dogCat'
os.chdir(ROOT)
os.getcwd()Apply Augmentation
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
TRAIN_DIR = '/content/flowers_filtered/train'
TEST_DIR = '/content/flowers_filtered/val'
training_set = train_datagen.flow_from_directory(TRAIN_DIR,
target_size = (224, 224),
batch_size = 32,
class_mode = 'categorical'
)
test_set = test_datagen.flow_from_directory(TEST_DIR,
target_size = (224, 224),
batch_size = 32,
class_mode = 'categorical'
)
Download the pretrained model
model = DenseNet169(include_top=False, weights='imagenet', input_shape=(224, 224, 3))Freeze layers
from keras.layers import BatchNormalization
for layer in model_base.layers:
if isinstance(layer, BatchNormalization):
layer.trainable = True
else:
layer.trainable = FalseAdd Custom Layers
from keras.layers import BatchNormalization
for layer in model_base.layers:
if isinstance(layer, BatchNormalization):
layer.trainable = True
else:
layer.trainable = FalseAdding Custom Layers
model = Sequential()
model.add(tf.keras.layers.experimental.preprocessing.Resizing(224,
224, interpolation="bilinear"))
model.add(model_base)
model.add(Flatten())
model.add(Dense(128, activation='elu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Dense(64, activation='elu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Dense(5, activation='softmax'))Defining Optimizers, Loss Functions and Checkpoints
OPTIMIZERS = optimizers.Adam()
checkpoints = tf.keras.callbacks.ModelCheckpoint(
'Densnet_model_best.hdf5',
monitor="val_loss",
verbose=0,
save_best_only=False)
lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(factor=0.5, patience=5)
model.compile(optimizer= OPTIMIZERS, loss='categorical_crossentropy', metrics=['acc'])Defining Path to Sabe the Model
import time
import os
def saveModel_path(model_dir="/content/drive/MyDrive/DLCVNLP/Computer_Vision/SAVED_MODELS"):
os.makedirs(model_dir, exist_ok=True)
fileName = time.strftime("DensNetModel_%Y_%m_%d_%H_%M_%S_.h5")
model_path = os.path.join(model_dir, fileName)
print(f"your model will be saved at the following location\n{model_path}")
return model_pathTensorboard Callback
log_dir = get_log_path()
tb_cb = tf.keras.callbacks.TensorBoard(log_dir=log_dir)Compiling Model
model.compile(optimizer= OPTIMIZERS, loss='categorical_crossentropy', metrics=['acc'])Fit Model and Start Training
model.fit(training_set,
steps_per_epoch= 3452 // 32,
epochs = 10,
validation_data = test_set,
validation_steps = 10,
callbacks= [checkpoints, lr_scheduler])Epoch 1/10
107/107 [==============================] - 112s 817ms/step - loss: 0.9201 - acc: 0.6760 - val_loss: 0.5511 - val_acc: 0.8313
Epoch 2/10
107/107 [==============================] - 80s 746ms/step - loss: 0.4234 - acc: 0.8681 - val_loss: 0.3199 - val_acc: 0.8906
Epoch 3/10
107/107 [==============================] - 80s 747ms/step - loss: 0.3139 - acc: 0.9056 - val_loss: 0.2199 - val_acc: 0.9281
Epoch 4/10
107/107 [==============================] - 80s 746ms/step - loss: 0.2258 - acc: 0.9330 - val_loss: 0.2631 - val_acc: 0.9062
Epoch 5/10
107/107 [==============================] - 80s 748ms/step - loss: 0.2054 - acc: 0.9336 - val_loss: 0.2168 - val_acc: 0.9219
Epoch 6/10
107/107 [==============================] - 80s 748ms/step - loss: 0.1848 - acc: 0.9424 - val_loss: 0.3004 - val_acc: 0.9094
Epoch 7/10
107/107 [==============================] - 81s 749ms/step - loss: 0.1687 - acc: 0.9515 - val_loss: 0.2496 - val_acc: 0.9312Image Classification using GNetTrainer
Create conda environment
conda create -n GNetTrainer python=3.6Activate conda environment
conda activate GNetTrainerNow all set to install the GNetTrainer Package
pip install GNetTrainerTo start the Magic. In the terminal type
gnet
Demo
Home Page
Training Page
Prediction Page
