Using ResNet50 as a base model for transfer learning

ResNet50Binary.py

from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from tensorflow.keras.layers import Dense, Activation, Flatten, Dropout
from tensorflow.keras.models import Sequential, Model

HEIGHT = 300
WIDTH = 300

base_model = ResNet50(weights='imagenet', 
                      include_top=False, 
                      input_shape=(HEIGHT, WIDTH, 3))


def build_finetune_model(base_model, dropout, fc_layers, num_classes):
    for layer in base_model.layers:
        layer.trainable = False

    x = base_model.output
    x = Flatten()(x)
    for fc in fc_layers:
        # New FC layer, random init
        x = Dense(fc, activation='relu')(x) 
        x = Dropout(dropout)(x)

    # For binary classification we'll use a sigmoid layer
    predictions = Dense(1, activation='sigmoid')(x) 
    
    finetune_model = Model(inputs=base_model.input, outputs=predictions)

    return finetune_model

class_list = ["Alma", "Zohar"]
FC_LAYERS = [1024, 1024]
dropout = 0.5

finetune_model = build_finetune_model(base_model, 
                                      dropout=dropout, 
                                      fc_layers=FC_LAYERS, 
                                      num_classes=len(class_list))