ImageNet_Utils | help download images by id , crop bounding box | Computer Vision library

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Dense, Flatten, BatchNormalization, Conv2D, MaxPool2D, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import matplotlib.pyplot as plt
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

os.chdir('C:/Users/dancu/PycharmProjects/firstCNN/data/ad-vs-cn')

physical_devices = tf.config.experimental.list_physical_devices('GPU')
print("Num GPUs Available: ", len(physical_devices))
tf.config.experimental.set_memory_growth(physical_devices[0], True)

train_path = "C:/Users/dancu/PycharmProjects/firstCNN\data/ad-vs-cn/train"
test_path = "C:/Users/dancu/PycharmProjects/firstCNN\data/ad-vs-cn/test"
valid_path = "C:/Users/dancu/PycharmProjects/firstCNN\data/ad-vs-cn/valid"

train_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
    .flow_from_directory(directory=train_path, target_size=(256,256), classes=['cn', 'ad'], batch_size=10, color_mode="rgb")
valid_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
    .flow_from_directory(directory=valid_path, target_size=(256,256), classes=['cn', 'ad'], batch_size=10, color_mode="rgb")
test_batches = ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
    .flow_from_directory(directory=test_path, target_size=(256,256), classes=['cn', 'ad'], batch_size=10, color_mode="rgb", shuffle=False)


# def plotImages(images_arr):
#     fig, axes = plt.subplots(1, 10, figsize=(20,20))
#     axes = axes.flatten()
#     for img, ax in zip( images_arr, axes):
#         ax.imshow(img)
#         ax.axis('off')
#     plt.tight_layout()
#     plt.show()
#
#
# imgs, labels = next(train_batches)
# plotImages(imgs)

model = Sequential([
    Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding = 'same', input_shape=(256,256,3)),
    MaxPool2D(pool_size=(2, 2), strides=2),
    Conv2D(filters=64, kernel_size=(3, 3), activation='relu', padding = 'same'),
    MaxPool2D(pool_size=(2, 2), strides=2),
    Flatten(),
    Dense(units=2, activation='softmax')
])

#print(model.summary())

model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])

model.fit(x=train_batches,
    steps_per_epoch=len(train_batches),
    validation_data=valid_batches,
    validation_steps=len(valid_batches),
    epochs=10,
    verbose=2
)