由于 numpy 形状的张量流，keras 无法训练模型

Question

我是新手，我正在尝试用 Keras 训练我的模型。我有14节课。以下是我的训练和测试数据的形状：print('train data shape:', X_train.shape)print('one hot shape:', y_train.shape)print('one hot shape:', y_test.shape)print('Number of images in x_train', x_train.shape[0])print('Number of images in x_test', x_test.shape[0])输出：train data shape: (77623, 28, 28, 1)one hot shape: (77623, 14, 14)one hot shape: (500, 14, 14)Number of images in x_train 77623Number of images in x_test 500这是我的模型：model = Sequential()model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))model.add(Conv2D(64, (3, 3), activation='relu'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Flatten())model.add(Dense(128, activation='relu'))model.add(Dropout(0.5))model.add(Dense(14, activation='softmax'))model.compile(loss=keras.losses.categorical_crossentropy,              optimizer=keras.optimizers.Adadelta(),              metrics=['accuracy'])print(model.summary())型号概要：Layer (type)                 Output Shape              Param #   =================================================================conv2d_58 (Conv2D)           (None, 26, 26, 32)        320       _________________________________________________________________conv2d_59 (Conv2D)           (None, 24, 24, 64)        18496     _________________________________________________________________max_pooling2d_27 (MaxPooling (None, 12, 12, 64)        0         _________________________________________________________________dropout_53 (Dropout)         (None, 12, 12, 64)        0         _________________________________________________________________这是对fit方法的调用：history = model.fit(X_train, y_train,          batch_size=batch_size,          epochs=epochs,          verbose=0,          validation_data=(X_test, y_test), callbacks=[TQDMNotebookCallback()])但我收到此错误：Error when checking target: expected dense_53 to have 2 dimensions, but got array with shape (77623, 14, 14)

Answer 1

也许你必须把你的，input_shape=(28,28,1)因为你的图像是 28x28 灰度