檢查輸入時出錯：預期dense_1_input 有2 維，但得到了形狀為(25000, 700, 50) 的數組

Question

trainData.shape = (25000, 700, 50) ，形狀如下：

[[[ 0.7095   0.863    0.712   ...  0.02715 -1.305    0.5195 ]
  [-0.66     1.715   -1.934   ...  0.5684   0.754    0.2593 ]
  [-0.3533   2.256   -1.292   ... -0.2708   0.6714  -1.128  ]
  ...
  [ 0.       0.       0.      ...  0.       0.       0.     ]
  [ 0.       0.       0.      ...  0.       0.       0.     ]
  [ 0.       0.       0.      ...  0.       0.       0.     ]]
  ...

trainLabel.shape = (25000,) , , 形狀如下：

[1. 1. 1. ... 0. 0. 0.]

使用它們來訓練 MLP 模型，我應該如何重塑 trainData 和 trainLabel ？ 詳細代碼如下：

def MySimpleMLP(feature=700, vec_size=50):
    auc_roc = LSTM.as_keras_metric(tf.compat.v1.metrics.auc)

    model = Sequential()

    model.add(Dense(32, activation='relu', input_shape=(feature * vec_size,)))
    model.add(Dropout(0.2))
    model.add(Dense(32, activation='relu'))
    model.add(Dropout(0.2))
    model.add(Dense(1, activation='softmax'))

    model.compile(loss="binary_crossentropy", optimizer="adam", metrics=[auc_roc])
    return model

 ......       

 model.fit(trainData, trainLabel, validation_split=0.2, epochs=10, batch_size=64, verbose=2)

請幫忙。

Answer 1

嘗試添加一個Flatten層，如下所示：

def MySimpleMLP(feature=700, vec_size=50):
    auc_roc = LSTM.as_keras_metric(tf.compat.v1.metrics.auc)

    model = Sequential()

    model.add(Dense(32, activation='relu', input_shape=(feature * vec_size,)))
    model.add(Dropout(0.2))
    model.add(Dense(32, activation='relu'))
    model.add(Flatten())
    model.add(Dropout(0.2))
    model.add(Dense(1, activation='softmax'))

    model.compile(loss="binary_crossentropy", optimizer="adam", metrics=[auc_roc])
    return model

 ......       

 model.fit(trainData, trainLabel, validation_split=0.2, epochs=10, batch_size=64, verbose=2)

Flatten將 (num_of_samples, 64, 32, 32) 數組轉換為 (num_of_samples, 64 32 32) 數組，即它使數組為 2D，這正是您所需要的。