resnet.py

def get_model(n_classes=1):

    base_model = ResNet50(weights='imagenet', include_top=False)

    #for layer in base_model.layers:
    #    layer.trainable = False

    x = base_model.output
    x = GlobalMaxPooling2D()(x)
    x = Dropout(0.5)(x)
    x = Dense(100, activation="relu")(x)
    x = Dropout(0.5)(x)
    if n_classes == 1:
        x = Dense(n_classes, activation="sigmoid")(x)
    else:
        x = Dense(n_classes, activation="softmax")(x)

    base_model = Model(base_model.input, x, name="base_model")
    if n_classes == 1:
        base_model.compile(loss="binary_crossentropy", metrics=['acc'], optimizer="adam")
    else:
        base_model.compile(loss="sparse_categorical_crossentropy", metrics=['acc'], optimizer="adam")

    return base_model