神经网络最后一层的错误

Question

#10-Fold split
seed = 7
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
np.random.seed(seed)
cvscores = []

    act = 'relu'
    for train, test in kfold.split(X, Y):

        model = Sequential()

        model.add(Dense(43, input_shape=(8,)))
        model.add(Activation(act))

        model.add(Dense(500))
        model.add(Activation(act))
    #model.add(Dropout(0.4))

        model.add(Dense(1000))
        model.add(Activation(act))
    #model.add(Dropout(0.4))

        model.add(Dense(1500))
        model.add(Activation(act))
    #model.add(Dropout(0.4))


        model.add(Dense(2))
        model.add(Activation('softmax'))

        model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
        hist = model.fit(X[train], Y[train],
                    epochs=500,
                    shuffle=True,
                    batch_size=100,
                    validation_data=(X[test], Y[test]), verbose=2)
    #model.summary()

When I call model.fit it reports the following error :

ValueError: Error when checking target: expected activation_5 to have shape (None, 2) but got array with shape (3869, 1)

I am using keras with TensorFlow backend. Please ask for any further clarification if needed.

Answer 1

try this:

#10-Fold split
seed = 7
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
np.random.seed(seed)
cvscores = []

    act = 'relu'
    for train, test in kfold.split(X, Y):

        model = Sequential()

        model.add(Dense(43, input_shape=(8,)))
        model.add(Activation(act))

        model.add(Dense(500))
        model.add(Activation(act))
    #model.add(Dropout(0.4))

        model.add(Dense(1000))
        model.add(Activation(act))
    #model.add(Dropout(0.4))

        model.add(Dense(1500))
        model.add(Activation(act))
    #model.add(Dropout(0.4))


        model.add(Dense(1))
        model.add(Activation('softmax'))

        model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
        hist = model.fit(X[train], Y[train],
                    epochs=500,
                    shuffle=True,
                    batch_size=100,
                    validation_data=(X[test], Y[test]), verbose=2)
    #model.summary()

Answer 2

使用此语句时，问题已解决

y = to_categorical(Y[:])