tensorflow.py

model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.40)) #0.4 removed as it is bad of tflite # 0.25
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.25)) #0.25 removed as it is bad of tflite # 0.5
    model.add(Dense(num_classes, activation='sigmoid')) #sigmoid
    model.compile(loss=keras.losses.binary_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

    filepath="weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5"
    checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max', period=5)

    callbacks_list = [checkpoint]

    model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          callbacks=callbacks_list,
          validation_data=(x_test, y_test))

    score = model.evaluate(x_test, y_test, verbose=5)