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Keras ValueError：ValueError：检查目标时出错：期望dense_4有形状（None，2）但是有形状的数组（2592,1）Python3

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在尝试使用Keras 2.0.8，Python 3.6.1和Tensorflow后端训练模型时遇到问题 .

错误信息：

ValueError：检查目标时出错：期望dense_4具有形状（None，2）但是得到了具有形状的数组（2592,1）

X_train = numpy.swapaxes(X_train, 1, 3)
X_test = numpy.swapaxes(X_test, 1, 3)

print("X_train shape: ") --> size = (2592, 1, 1366, 96)
print("-----")
print("X_test shape") --> size = (648, 1, 1366, 96)
print("-----")
print(Y_train.shape) --> size = (2592,)
print("-----")
print("Y_test shape") --> size = (648,)

相关代码片段：

K.set_image_dim_ordering('th')
K.set_image_data_format('channels_first')

def create_model(weights_path=None):
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),activation='relu', padding="same", input_shape=(1, 1366, 96)))
    model.add(Conv2D(64, (3, 3), activation='relu', dim_ordering="th"))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.1))
    model.add(Dense(64, activation='relu'))
    model.add(Dropout(0.1))
    model.add(Dense(16, activation='relu'))
    model.add(Dense(2, activation='softmax'))
    if weights_path:
        model.load_weights(weights_path)
    return model

model = create_model()
model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.SGD(lr=0.01),
              metrics=['accuracy'])
history = model.fit(X_train, Y_train,
      batch_size=32,
      epochs=100,
      verbose=1,
      validation_data=(X_test, Y_test))

Line 142, where I call model.fit() is where I am getting this error

Things I have tried to fix this error 引用了这些堆栈溢出帖子：

我尝试使用以下代码重塑Y_test和Y_train numpy数组：

Y_train.reshape(2592, 2)
Y_test.reshape(648, 2)

但是，我收到以下错误：

ValueError：无法将大小为2592的数组重塑为形状（2592,2）

python-3.x machine-learning tensorflow deep-learning keras

2 回答

  • 2

    当您使用 categorical_crossentropy 丢失时，您必须使用单热编码标签 . 为此，您可以使用 keras.utils.np_utils 中的函数 to_categorical

    from keras.utils import np_utils
y_train_onehot = np_utils.to_categorical(y_train)
y_test_onehot = np_utils.to_categorical(y_test)

    然后使用单热编码标签来训练您的模型 .

    回复于
  • 4

    在我看来，你需要改变NN的最后一层：

    def create_model(weights_path=None):
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3),activation='relu', padding="same", input_shape=(1, 1366, 96)))
    model.add(Conv2D(64, (3, 3), activation='relu', dim_ordering="th"))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.1))
    model.add(Dense(64, activation='relu'))
    model.add(Dropout(0.1))
    model.add(Dense(16, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    if weights_path:
        model.load_weights(weights_path)
    return model
    回复于

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