Sequential design using Tensorflow 2

sequential-model.py

from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv2D, MaxPool2D, BatchNormalization, Flatten, Dropout, Dense
from tensorflow.keras.regularizers import l2
from tensorflow.keras.activations import relu, sigmoid
from tensorflow.keras.initializers import GlorotNormal

#this configuration uses backend.set_image_data_format('channels_first')

"""
This function creates a model composed by two convolutional + max pooling layers.
After, to standardize the input a batch normalization is applied.
This is put into a single one-dimensional layer using the Flatten layer.
Next, the dropout regularization technique (50%) is perfomed.
Finally, Dense layer output for binary classification
"""
def get_model_design(filters: list, input_shape: tuple) -> Sequential:
    model = Sequential([Conv2D(filters[0], (5, 5),  padding='same', kernel_regularizer=l2(0.001), activation=relu, input_shape=input_shape),
                        Conv2D(filters[1], (3, 3),  padding='same', kernel_regularizer=l2(0.001), activation=relu),
                        MaxPool2D(pool_size=(2, 2)),
                        BatchNormalization(),
                        Flatten(),
                        Dropout(0.5),
                        Dense(1, kernel_initializer=GlorotNormal(), activation=sigmoid )
                       ])
    return model

# for this example, we used 128 and 64 filters for the two first conv layers
# note the input size of 3 channel for an image size of 64x64 pixels
model = get_model_design([128, 64], (3, 64, 64))
model.summary()