model.py

from keras.applications.vgg19 import VGG19
from keras.models import Sequential,Model,load_model
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, GlobalAveragePooling2D
from keras.optimizers import SGD, Adam, Nadam

def model(img_height, img_width, num_classes, lr):

    base_model = VGG19(weights=None, include_top=False, input_shape=(img_height, img_width, 1))
    x = base_model.output
    x = Flatten()(x)
    x = Dense(4096, activation='relu')(x)
    x = Dropout(0.5)(x)
    x = Dense(4096, activation='relu')(x)

    x = Dropout(0.5)(x)
    predictions = Dense(num_classes, activation='sigmoid')(x)
    model = Model(inputs=base_model.input, outputs=predictions)
    adam = Adam(lr=lr)
    model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['binary_accuracy'])

    model.summary()

    return model