weather_model.py

base_model = keras.applications.DenseNet121(weights='imagenet', input_shape=input_shape, include_top=False)    # False, do not include the classification layer of the model
base_model.trainable = False

inputs = tf.keras.Input(shape=input_shape)

x = base_model(inputs, training=False)
x = keras.layers.GlobalAveragePooling2D()(x)
outputs = keras.layers.Dense(1, activation = 'sigmoid')(x)    # Add own classififcation layer

model = keras.Model(inputs, outputs)

cb = [callbacks.EarlyStopping(monitor = 'val_loss', patience = 5, restore_best_weights = True)]

model.compile(loss='binary_crossentropy', optimizer=optimizers.Adam(0.01), metrics=['accuracy'])

history = model.fit(train_ds, validation_data =  test_ds, epochs=32, callbacks = cb)