當我的輸入數據的形狀是 4D 時，為什么我的 Conv2D 模型沒有得到 4 維？

Question

我正在嘗試對卷積網絡中手寫數字的 MNIST 數據庫進行分類，但一直收到此錯誤： ValueError: Error when checking input: expected conv2d_40_input to have 4 dimensions, but got array with shape (28, 28, 1)

我的任務是使用交叉抽樣，這就是數據被分成 5 組的原因。

def train_conv_subsample():

#splitting data into chunks
chunks = []
chunk_labels = []
num_chunks = 5
chunk_size = int(train_data.shape[0]/num_chunks)
for i in range(num_chunks):
    chunks.append(train_data[(i*chunk_size):(i+1)*chunk_size])
    chunk_labels.append(train_labels[(i*chunk_size):(i+1)*chunk_size])

#Create another convolutional model to train.
for i in range(num_chunks):
    current_train_data = []
    current_train_lables = []
    for j in range(num_chunks):
        if(i == j):
            validation_data = chunks[i]
            validation_labels = chunk_labels[i]
        else:
            current_train_data.extend(chunks[j])
            current_train_lables.extend(chunks[j])

    print(np.shape(current_train_data)) #Says it has a shape of (48000,28,28, 1)

    model = models.Sequential([
        layers.Conv2D(16, kernel_size=(3, 3), activation='relu', input_shape=(28,28,1)),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Flatten(),
        layers.Dense(32, activation='relu'),
        layers.Dense(10, activation='softmax')
    ])
    model.compile(optimizer='adam',
                  loss=tf.keras.losses.CategoricalCrossentropy(),
                  metrics=['accuracy'])

    #But when it goes to fit it raises the error: expected 4 dim, but got array with shape (28, 28, 1)
    model.fit(current_train_data, current_train_lables, epochs=1, validation_data=(validation_data, validation_labels))
    tf.keras.backend.clear_session()

那是我的代碼，我使用的數據集可以從 keras 數據集，datasets.mnist.load_data() 導入

謝謝你的幫助

Answer 1

我認為問題在於，對於 mnist 數據集中的圖像形狀，您需要使用 numpy 數組庫中的 reshape 將它們重塑為 4 個暗淡的數組，如下所示：

    import numpy as np 

    np.reshape(dataset,(-1,28,28,1) 

如果這不起作用嘗試在使用 OpenCV 庫重塑之前將它們轉換為灰度