[英]Type Error When Using Python's Keras for Neural Network
我正在嘗試使用神經網絡來預測房屋的價格。 這是數據集頂部的樣子:
Price Beds SqFt Built Garage FullBaths HalfBaths LotSqFt
485000 3 2336 2004 2 2.0 1.0 2178.0
430000 4 2106 2005 2 2.0 1.0 2178.0
445000 3 1410 1999 1 2.0 0.0 3049.0
...
這是我對神經網絡進行編碼的方式(使用 Python 的 keras)。
dataset = df.values
X = dataset[:,1:8]
Y = dataset[:,0]
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
min_max_scaler = preprocessing.MinMaxScaler()
X_scale = min_max_scaler.fit_transform(X)
X_scale
X_train, X_val_and_test, Y_train, Y_val_and_test = train_test_split(X_scale, Y, test_size=0.3)
X_val, X_test, Y_val, Y_test = train_test_split(X_val_and_test, Y_val_and_test, test_size=0.5)
print(X_train.shape, X_val.shape, X_test.shape, Y_train.shape, Y_val.shape, Y_test.shape)
from keras.models import Sequential
from keras.layers import Dense
model = Sequential(
Dense(32, activation='relu', input_shape=(7,)),
Dense(1, activation='relu'))
model.compile(optimizer='sgd',
loss='mse',
metrics=['mean_squared_error'])
hist = model.fit(X_train, Y_train,
batch_size=32, epochs=100,
validation_data=(X_val, Y_val)) #Error here!
model.evaluate(X_test, Y_test)[1] #Same Error here!
運行hist =
行和model.evaluate
行時,我遇到了同樣的錯誤。 以下是錯誤信息:
TypeError Traceback (most recent call last)
<ipython-input-19-522714a352ba> in <module>
----> 1 hist = model.fit(X_train, Y_train,
2 batch_size=32, epochs=100,
3 validation_data=(X_val, Y_val))
~/opt/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
...
TypeError: in user code:
...
name_scope += '/'
TypeError: unsupported operand type(s) for +=: 'Dense' and 'str'
我不確定為什么會發生這種情況,因為當我在原始數據幀上運行df.dtypes
時,所有列都是整數或浮點數。
簡單修復! 您的模型構造中似乎缺少一個硬支架。 嘗試使用這個:
model = Sequential([
Dense(32, activation='relu', input_shape=(7,)),
Dense(1, activation='relu'),
])
希望能幫助到你! 如果您有更多問題,請告訴我!
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