I am a PhD student working on a machine learning project with binary classification and RESNET architecture in TensorFlow. I believe I have done everything correctly but I am looking for some validation that the code is correct as I have no one to check my work. I am working in Google Colab with two programs: 1 to split the dataset and 1 to run the model. The first code is splitdataset.ipynb and the second code is classifyimages.ipynb. I know their are easier and other ways to do this but this is how I implemented it. Some of the more important things to check over would be how I implemented the brightness augmentations and how I split the dataset but ideally a whole code validation would be nice.

splitdataset.ipynb

import cv2import osimport shutilimport random%cd /content/drive/MyDrive/static_CTC_classification!rm -r data_set!mkdir data_set!mkdir data_set/training!mkdir data_set/validation!mkdir data_set/testing!mkdir data_set/training/DU145!mkdir data_set/training/PC3!mkdir data_set/validation/DU145!mkdir data_set/validation/PC3!mkdir data_set/testing/DU145!mkdir data_set/testing/PC3du145 = []pc3 = []for image in os.listdir("full_ds/DU145"):  du145.append("full_ds/DU145/" + image)for image in os.listdir("full_ds/PC3"):  pc3.append("full_ds/PC3/" + image)images = du145 + pc3random.shuffle(images)num_images = len(images)train_num = int(0.8 * num_images)val_num = int(0.1 * num_images)print("train_num: ",train_num)print("val_num: ",val_num)train = images[0:train_num]val = images[train_num:train_num+val_num]test = images[train_num+val_num:]for image in train:  if(image[8] == "D"):    im = image[14:]    shutil.copyfile(image, "split_ds/training/DU145/"+im)  if(image[8] == "P"):    im = image[12:]    shutil.copyfile(image, "split_ds/training/PC3/"+im)for image in val:  if(image[8] == "D"):    im = image[14:]    shutil.copyfile(image, "split_ds/validation/DU145/"+im)  if(image[8] == "P"):    im = image[12:]    shutil.copyfile(image, "split_ds/validation/PC3/"+im)for image in test:  if(image[8] == "D"):    im = image[14:]    shutil.copyfile(image, "split_ds/testing/DU145/"+im)  if(image[8] == "P"):    im = image[12:]    shutil.copyfile(image, "split_ds/testing/PC3/"+im)

classifyimages.ipynb

import tensorflow as tffrom tensorflow.keras.callbacks import ModelCheckpointimport tensorflow_datasets as tfdsimport numpy as npimport matplotlib.pyplot as pltimage_size = (224,224)batch_size = 32train_ds = tf.keras.preprocessing.image_dataset_from_directory(    "/content/drive/MyDrive/static_CTC_classification/split_ds/training",    seed=1337,    color_mode='rgb',    image_size=image_size,    batch_size=batch_size)val_ds = tf.keras.preprocessing.image_dataset_from_directory(    "/content/drive/MyDrive/static_CTC_classification/split_ds/validation",    seed=1337,    color_mode='rgb',    image_size=image_size,    batch_size=batch_size)test_ds = tf.keras.preprocessing.image_dataset_from_directory(    "/content/drive/MyDrive/static_CTC_classification/split_ds/testing",    seed=1337,    color_mode='rgb',    image_size=image_size,    batch_size=batch_size)# from website: https://towardsdatascience.com/writing-a-custom-data-augmentation-layer-in-keras-2b53e048a98class RandomColorDistortion(tf.keras.layers.Layer):  def __init__(self, contrast_range=[0.5, 1.5],                 brightness_delta=[-0.2, 0.2], **kwargs):      super(RandomColorDistortion, self).__init__(**kwargs)      self.contrast_range = contrast_range      self.brightness_delta = brightness_delta  def call(self, images, training=None):          if not training:              return images                    contrast = np.random.uniform(              self.contrast_range[0], self.contrast_range[1])          brightness = np.random.uniform(              self.brightness_delta[0], self.brightness_delta[1])                    images = tf.image.adjust_contrast(images, contrast)          images = tf.image.adjust_brightness(images, brightness)          images = tf.clip_by_value(images, 0, 1)          return imagesaugment_and_normalize = tf.keras.Sequential([  RandomColorDistortion(contrast_range=[0.5,1.5], brightness_delta=[-0.15, 0.15]),                         tf.keras.layers.RandomFlip("horizontal"),  tf.keras.layers.RandomRotation(0.1),  tf.keras.layers.experimental.preprocessing.Rescaling(1.0 / 255)], name="augment_and_normalize")def make_model(input_shape, num_classes):    input = tf.keras.Input(shape=input_shape)    # Entry block    x = augment_and_normalize(input)    feature_extractor = tf.keras.applications.resnet.ResNet50(input_shape=(224, 224, 3),                                               include_top=False,                                               weights='imagenet')(x)    x = tf.keras.layers.GlobalAveragePooling2D()(feature_extractor)    x = tf.keras.layers.Flatten()(x)    x = tf.keras.layers.Dense(1024, activation="relu")(x)    x = tf.keras.layers.Dropout(0.5)(x)    x = tf.keras.layers.Dense(512, activation="relu")(x)    x = tf.keras.layers.Dropout(0.5)(x)    output = tf.keras.layers.Dense(1, activation="sigmoid", name="classification")(x)    return tf.keras.Model(input, output)model = make_model(input_shape=image_size + (3,), num_classes=2)tf.keras.utils.plot_model(model)epochs = 250model.compile(    optimizer=tf.keras.optimizers.Adam(),    loss="binary_crossentropy",    metrics=["accuracy"],)history = model.fit(x=train_ds, epochs=epochs, validation_data=val_ds,    callbacks = [ModelCheckpoint(filepath="weights.{epoch:02d}.ckpt", monitor='val_accuracy',    verbose=0, save_best_only=True,save_weights_only=True, mode='auto', save_freq='epoch',options=None)])!cp /content/weights.05.ckpt.data-00000-of-00001 /content/drive/MyDrive/static_CTC_classification/!cp /content/weights.05.ckpt.index /content/drive/MyDrive/static_CTC_classification/# Then after saving these weights to my google drive I can load them later with the following commandmodel.load_weights("/content/drive/MyDrive/static_CTC_classification/weights.05.ckpt")

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