Keras conv-neural-network问题：ValueError：检查输入时出错：预期conv2d_1_input有4个维度

我是Keras的新手并尝试 Build 一个卷积神经网络 . 对于预处理，我在样本（批量大小）中拍摄了每张图片的32个补丁，大小为200 x 200（补丁大小） . 我生成了它的numpy数组并保存了它们 . 我尝试用这些批次喂养模型 .

pg = PatchGenerator(image_folder, list_path, sheet_name, patch_size, 
batch_size, split_distribution)

# Load batches and labels
X_train = []
Y_train = []
c = 0
for x in range(2):
     X_train.append(np.load(train_batch_load_path + train_batch_name + 
     str(c+1) + '.npy'))
     Y_train.append(pg.hd_train_list[c])
     c = c + 1
X_train = np.array(X_train)

print(X_train.shape)  # shape: (2, 32, 200, 200, 3)

# Model
model.add(Convolution2D(filters=16, kernel_size=3, strides=(1, 1), 
bias_initializer='zeros', padding='same', data_format='channels_last', 
activation=None, batch_size=32, input_shape=(patch_size, patch_size, 3))) 

print(model.output_shape) # shape: (32, 200, 200, 16)

model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2),  padding='same', 
data_format='channels_last'))

print(model.output_shape) #shape: (32, 100, 100, 16)

model.add(Convolution2D(filters=32, kernel_size=3, strides=(2, 2), 
bias_initializer='zeros', padding='same', data_format='channels_last', 
activation='relu'))

print(model.output_shape) # shape: (32, 50, 50, 32)

model.add(AveragePooling2D(pool_size=(2, 2), strides=(5, 5),  
padding='same', data_format='channels_last'))

print(model.output_shape) # shape: (32, 10, 10, 32)

model.add(Convolution2D(filters=16, kernel_size=5, strides=(1, 1), 
bias_initializer='zeros', padding='valid', data_format='channels_last', 
activation='relu'))

print(model.output_shape) # shape: (32, 6, 6, 16)

model.add(MaxPooling2D(pool_size=(4, 4), strides=(1, 1),  padding='valid', 
data_format='channels_last'))

print(model.output_shape) # shape: (32, 3, 3, 16)

model.add(Convolution2D(filters=1, kernel_size=3, strides=(1, 1), 
bias_initializer='zeros', padding='valid', data_format='channels_last', 
activation='relu'))

print(model.output_shape) # shape: (32, 1, 1, 1)

model.add(Flatten())

print(model.output_shape) # shape: (32, 1)

model.compile(loss='mean_squared_error',
          optimizer='adam',
          metrics=['accuracy'])

model.fit(X_train, Y_train, batch_size=batch_size, epochs=epochs, verbose=1)

当我尝试运行代码时，我收到以下错误：

ValueError：检查输入时出错：预期conv2d_1_input有4个维度，但得到的数组有形状（2,32,200,200,3）

我知道它们超过了4个维度，但是当我只使用一个具有4个维度的形状的批次时，我得到了一个错误，我的尺寸太小了 . 我认为输出形状是运行模型所需的正确形状 .

希望您能够帮助我 .