python - 使用顺序模型预测（）方法的正确方法

Question

我是使用 TensorFlow 进行机器学习的新手。我在下面的代码中构建了一个模型。该模型训练和测试成功。我的数据集如下所示：

[![在此处输入图像描述][1]][1]

模型训练好后，我想手动输入一些数据进行测试，比如这样：

test_row = [57, 1,  0,  140,    192,    0,  1,  148,    0,  0.4,    1,  0,  1,  1]

但是，当我尝试使用numpy.array将该列表转换为 numpy 数组格式时

np_array = numpy.array(test_row)

按照堆栈溢出帖子之一中的说明，然后使用

result = model.predict(np_array)

预测结果我得到一个错误。我认为我使用的predict()方法不正确，但是我花了 5 个小时在这上面，找不到解决这个问题的好方法。

file_name = "heart.csv"
data=pd.read_csv(file_name) #store data to variab

feature_columns =  [] #combined features to input to the model
data["cp"] = data["cp"].apply(str)#represent data in cp as String
cp = tf.feature_column.categorical_column_with_vocabulary_list(
      'cp', ['0', '1', '2', '3'])#create one-hot vector from the string 
cp_one_hot = tf.feature_column.indicator_column(cp) #mapped to numeric value
feature_columns.append(cp_one_hot) 

#same for restecg
data["restecg"] = data["restecg"].apply(str)#represent data in cp as String
restecg = tf.feature_column.categorical_column_with_vocabulary_list(
      'restecg', ['0', '1', '2'])#create one-hot vector from the string 
restecg_one_hot = tf.feature_column.indicator_column(restecg) #mapped to numeric value
feature_columns.append(restecg_one_hot)               
thalach = tf.feature_column.numeric_column("thalach")
feature_columns.append(thalach)    

#same for restecg
data["slope"] = data["slope"].apply(str)#represent data in cp as String
slope = tf.feature_column.categorical_column_with_vocabulary_list(
      'slope', ['1', '2', '3'])#create one-hot vector from the string 
slope_one_hot = tf.feature_column.indicator_column(slope) #mapped to numeric value
feature_columns.append(slope_one_hot)

def create_data_set(self,df, size=32):
    df = df.copy()
    labels = df.pop('target')
    return tf.data.Dataset.from_tensor_slices((dict(df),labels)).shuffle(buffer_size = len(df)).batch(size)

RANDOM_SEED = 42 
train, test = train_test_split(data, test_size=0.2, random_state=RANDOM_SEED) #without random_state, every time this function run, it will generate different selection
train_set = self.create_data_set(train)
test_set = self.create_data_set(test)

#create model and train the model
model = tf.keras.models.Sequential([tf.keras.layers.DenseFeatures(feature_columns = feature_columns),
                                tf.keras.layers.Dense(units=128, activation='relu'),
                                tf.keras.layers.Dropout(rate=0.2),
                                tf.keras.layers.Dense(units=128,activation='relu'),
                                tf.keras.layers.Dense(units=1, activation = 'sigmoid')])
#compile
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])


#training model fit train data and test data to model
model.fit(train_set,validation_data = test_set, epochs = 100, use_multiprocessing=True)

更新：代码导致错误

 def start_predic(input_to_predict)
        #format of input_to_predict is a DICTIONARY of String ex {'val1':'1', 'val2':'2', 'val3':'3','val4':'4','val5':'5',
'val6':'6','val7':'7','val8':'8','val9':'9',
'val10':'10','val10':'10','val11':'11','val12':'12','val13':'13'}
        for k, v in input_to_predict.items():
            if v != None :
                 input_to_predict[k] = float(v)
    
        input_array_for_prediction =          np.array(list(input_to_predict.values()))       
   
        #pass in data  to predict the disease
        result=model.predict(input_array_for_prediction)

更新：错误回溯

Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\flask\app.py", line 2447, in wsgi_app
    response = self.full_dispatch_request()
  File "C:\ProgramData\Anaconda3\lib\site-packages\flask\app.py", line 1952, in full_dispatch_request
    rv = self.handle_user_exception(e)
  File "C:\ProgramData\Anaconda3\lib\site-packages\flask\app.py", line 1821, in handle_user_exception
    reraise(exc_type, exc_value, tb)
  File "C:\ProgramData\Anaconda3\lib\site-packages\flask\_compat.py", line 39, in reraise
    raise value
  File "C:\ProgramData\Anaconda3\lib\site-packages\flask\app.py", line 1950, in full_dispatch_request
    rv = self.dispatch_request()
  File "C:\ProgramData\Anaconda3\lib\site-packages\flask\app.py", line 1936, in dispatch_request
    return self.view_functions[rule.endpoint](**req.view_args)
  File "C:\Users\adm\DiagnosisSystem\app\routes.py", line 35, in index
    result = modules.heart_predict.start_predict(symptom_dict) #import data from UI to the model
  File "C:\Users\adm\DiagnosisSystem\diagnosis\HeartDiagnosisSystem.py", line 171, in start_predict
    result =self.model.predict(input_array_for_prediction)
  File "C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\keras\engine\training.py", line 1751, in predict
    tmp_batch_outputs = self.predict_function(iterator)
  File "C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py", line 885, in __call__
    result = self._call(*args, **kwds)
  File "C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py", line 933, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py", line 759, in _initialize
    self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
  File "C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\function.py", line 3066, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\function.py", line 3463, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\function.py", line 3298, in _create_graph_function
    func_graph_module.func_graph_from_py_func(
  File "C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\framework\func_graph.py", line 1007, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\eager\def_function.py", line 668, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\framework\func_graph.py", line 994, in wrapper
    raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:

    C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\keras\engine\training.py:1586 predict_function  *
        return step_function(self, iterator)
    C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\keras\engine\training.py:1576 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\distribute\distribute_lib.py:1286 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\distribute\distribute_lib.py:2849 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\distribute\distribute_lib.py:3632 _call_for_each_replica
        return fn(*args, **kwargs)
    C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\keras\engine\training.py:1569 run_step  **
        outputs = model.predict_step(data)
    C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\keras\engine\training.py:1537 predict_step
        return self(x, training=False)
    C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\keras\engine\base_layer.py:1037 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\keras\engine\sequential.py:383 call
        outputs = layer(inputs, **kwargs)
    C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\keras\engine\base_layer.py:1037 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    C:\Users\adm\AppData\Roaming\Python\Python38\site-packages\keras\feature_column\dense_features.py:158 call  **
        raise ValueError('We expected a dictionary here. Instead we got: ',

    ValueError: ('We expected a dictionary here. Instead we got: ', <tf.Tensor 'ExpandDims:0' shape=(None, 1) dtype=float32>)

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  [1]: https://i.stack.imgur.com/xCgYB.png

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