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   model1=applications.InceptionResNetV2(weights = "imagenet", include_top=False, input_shape = (img_width, img_height, 3)) 
    x = model1.output
    x=Dropout(0.5)(x)
    x = Flatten()(x)
    output = Dense(num_classes, activation="softmax")(x)
    model1 = Model(input = model.input, output = output)
    model1.compile(loss = "categorical_crossentropy", optimizer = 'adam', metrics=["accuracy"])
    reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.2, patience=3, min_lr=0.0001,mode='auto')
    checkpoint = ModelCheckpoint("vgg16.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
    reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.2, patience=3, min_lr=0.0001,mode='auto')

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