First version of the CNN

model_v1.py

import tensorflow as tf
from tensorflow import keras
from keras import layers

inputs = keras.Input(shape=(256, 256, 3))
x = layers.Rescaling(1./255)(inputs)
x = layers.Conv2D(filters=32, kernel_size=3, activation=keras.activations.relu)(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=64, kernel_size=3, activation=keras.activations.relu)(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=128, kernel_size=3, activation=keras.activations.relu)(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=256, kernel_size=3, activation=keras.activations.relu)(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=256, kernel_size=3, activation=keras.activations.relu)(x)
x = layers.Flatten()(x)
x = layers.Dense(516, activation=keras.activations.relu)(x)
outputs = layers.Dense(1, activation=keras.activations.sigmoid)(x)
model = keras.Model(inputs, outputs)

model.compile(optimizer=keras.optimizers.RMSprop(),
              loss=keras.losses.BinaryCrossentropy(),
              metrics=["accuracy"])