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[英]keras and tensorflow on python - ValueError: ('Input data in `NumpyArrayIterator` should have rank 4. You passed an array with shape', (36848,))
[英]ValueError: Input to `.fit()` should have rank 4. Got array with shape in CNN?
我致力於在我的數據集中實現 CNN。
這是我的代碼得到 x 火車和 y 火車與重塑過程
Y_train = train["Label"]
X_train = train.drop(labels = ["Label"],axis = 1)
X_train.shape -> /*(230, 67500)*/
X_train = np.pad(X_train, ((0,0), (0, (67600-X_train.shape[1]))), 'constant').reshape(-1, 260, 260)
Y_train = to_categorical(Y_train, num_classes = 10)
在我完成一些程序和重塑過程之后,我將 X_train 和 Y_train 分開。 這是下面顯示的代碼。
X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=42)
print("x_train shape",X_train.shape)
print("x_test shape",X_val.shape)
print("y_train shape",Y_train.shape)
print("y_test shape",Y_val.shape)
結果定義如下。
x_train shape (207, 260, 260)
x_test shape (23, 260, 260)
y_train shape (207, 10)
y_test shape (23, 10)
然后我創建 CNN Model。
model = Sequential()
#
model.add(Conv2D(filters = 8, kernel_size = (5,5),padding = 'Same',
activation ='relu', input_shape = (260, 260)))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))
#
model.add(Conv2D(filters = 16, kernel_size = (3,3),padding = 'Same',
activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.25))
# fully connected
model.add(Flatten())
model.add(Dense(256, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation = "softmax"))
然后我使用 ImageGenerator 來使用數據增強
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # dimesion reduction
rotation_range=0.5, # randomly rotate images in the range 5 degrees
zoom_range = 0.5, # Randomly zoom image 5%
width_shift_range=0.5, # randomly shift images horizontally 5%
height_shift_range=0.5, # randomly shift images vertically 5%
horizontal_flip=False, # randomly flip images
vertical_flip=False) # randomly flip images
X_train = np.pad(X_train, ((0,0), (0, (67600-X_train.shape[1]))), 'constant').reshape(-1, 260, 260, 1)
datagen.fit(X_train)
然后它會引發如下所示的錯誤。
ValueError: operands could not be broadcast together with remapped shapes [original->remapped]: (2,2) and requested shape (3,2)
我該如何解決?
我認為問題在於ImageDataGenerator
期望圖像具有寬度、高度和顏色通道(最常見的是紅色、綠色和藍色的 3 個通道)。 由於還有一個批量大小,它期望的整體形狀是(batch size, width, height, channels)
。 您的張量是 260x260,但沒有顏色通道。 它們是灰度圖像嗎?
根據文檔:
x:樣本數據。 應該有等級 4。在灰度數據的情況下,通道軸應該有值 1
所以我認為你只需要重塑你的輸入,在最后添加一個額外的維度。
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