Function check_model

tests/utils.py:150–179

compile model,train and evaluate it,then save/load weight and model file. :param model: :param model_name: :param x: :param y: :param check_model_io: :return:

(model, model_name, x, y, check_model_io=True)

def check_model(model, model_name, x, y, check_model_io=True):
    '''
    compile model,train and evaluate it,then save/load weight and model file.
    :param model:
    :param model_name:
    :param x:
    :param y:
    :param check_model_io:
    :return:
    '''
    early_stopping = EarlyStopping(monitor='val_acc', min_delta=0, verbose=1, patience=0, mode='max')
    model_checkpoint = ModelCheckpoint(filepath='model.ckpt', monitor='val_acc', verbose=1,
                                       save_best_only=True,
                                       save_weights_only=False, mode='max', period=1)

    model.compile('adam', 'binary_crossentropy',
                  metrics=['binary_crossentropy', 'acc'])
    model.fit(x, y, batch_size=100, epochs=1, validation_split=0.5, callbacks=[early_stopping, model_checkpoint])

    print(model_name + 'test, train valid pass!')
    torch.save(model.state_dict(), model_name + '_weights.h5')
    model.load_state_dict(_torch_load_compat(model_name + '_weights.h5'))
    os.remove(model_name + '_weights.h5')
    print(model_name + 'test save load weight pass!')
    if check_model_io:
        torch.save(model, model_name + '.h5')
        model = _torch_load_compat(model_name + '.h5')
        os.remove(model_name + '.h5')
        print(model_name + 'test save load model pass!')
    print(model_name + 'test pass!')


def get_device(use_cuda=True):