tf.py

import tensorflow as tf
from tensorflow import keras
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
import os

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# print(tf.__version__)
# print(cv.__version__)

PATH = "C://Users/tharu/Downloads/tf-pics"

train_dir = os.path.join(PATH, 'train')
validation_dir = os.path.join(PATH, 'validation')

train_1_dir = os.path.join(train_dir, '1')
train_else_dir = os.path.join(train_dir, 'else')
validation_1_dir = os.path.join(validation_dir, '1')
validation_else_dir = os.path.join(validation_dir, 'else')

num_1_tr = len(os.listdir(train_1_dir))
num_else_tr = len(os.listdir(train_else_dir))

num_1_val = len(os.listdir(validation_1_dir))
num_else_val = len(os.listdir(validation_else_dir))

total_train = num_1_tr + num_else_tr
total_val = num_1_val + num_else_val

# print('total training 1 images:', num_1_tr)
# print('total training else images:', num_else_tr)
#
# print('total validation 1 images:', num_1_val)
# print('total validation else images:', num_else_val)
# print("--")
# print("Total training images:", total_train)
# print("Total validation images:", total_val)
# print("--")
# print("--\n")

batch_size = 128
epochs = 3
IMG_HEIGHT = 224
IMG_WIDTH = 224

train_image_generator = ImageDataGenerator(rescale=1./255)  # Generator for our training data
validation_image_generator = ImageDataGenerator(rescale=1./255)  # Generator for our validation data

train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
                                                           directory=train_dir,
                                                           shuffle=True,
                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                           class_mode='binary')

val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size,
                                                              directory=validation_dir,
                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                              class_mode='binary')

model = Sequential([
    Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)),
    MaxPooling2D(),
    Dropout(0.2),
    Conv2D(32, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Conv2D(64, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Dropout(0.2),
    Flatten(),
    Dense(512, activation='relu'),
    Dense(1)
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.summary()

history = model.fit_generator(
    train_data_gen,
    steps_per_epoch=total_train // batch_size,
    epochs=epochs,
    validation_data=val_data_gen,
    validation_steps=total_val // batch_size
)

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

loss=history.history['loss']
val_loss=history.history['val_loss']

epochs_range = range(epochs)

plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')

plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()