ValueError異常

Question

我試圖編譯順序（）與Keras模塊的數據集，但我得到一個值誤差：

ValueError: Error when checking model input: expected dense_input_1 to have shape (None, 33) but got array with shape (32, 36) 

我通過我的代碼多次去，但找不到任何可能的錯誤。

我有一個包含32個項目的數據集，所有這些都轉換爲浮點數。

這裏是我的神經網絡代碼：

# Build neural network 
# Sequential 
model = Sequential() 

# Neural network 
model.add(Dense(36, input_dim=34, init='uniform', activation='sigmoid')) 
model.add(Dense(32, init='uniform', activation='sigmoid')) 
model.add(Dense(32, init='uniform', activation='sigmoid')) 
model.add(Dense(32, init='uniform', activation='sigmoid')) 
model.add(Dense(33, init='uniform', activation='sigmoid')) 

# Compile model 
model.compile(loss='mean_squared_logarithmic_error', optimizer='SGD', metrics=['accuracy']) 

# Fit model 
history = model.fit(X, Y, nb_epoch=20, validation_split=0.2, batch_size=3) 

這是我收到完整的錯誤消息：

Traceback (most recent call last): 
    File "/Users/cliang/Desktop/Laurence/Python/Programs/Python/Collaborative_Projects/Cancer_screening/neural_network_alls_1.py", line 111, in <module> 
    history = model.fit(X, Y, nb_epoch=20, validation_split=0.2, batch_size=3) 
    File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/models.py", line 672, in fit 
    initial_epoch=initial_epoch) 
    File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 1116, in fit 
    batch_size=batch_size) 
    File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 1029, in _standardize_user_data 
    exception_prefix='model input') 
    File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/keras/engine/training.py", line 124, in standardize_input_data 
    str(array.shape)) 
ValueError: Error when checking model input: expected dense_input_1 to have shape (None, 34) but got array with shape (32, 36) 

Answer 1

由於錯誤說，你有你的輸入數據之間的小姐比賽形狀和第一層。您明確地定義了您的輸入尺寸是（功能數量）是34 input_dim=34，儘管您正在傳遞具有36個功能的數據。

我認爲你已經隱藏了隱藏層36的神經元數量和你的輸入數據34之間的混淆。你可以從你的數據中刪除兩列或者更改input_dim=36。