[英]MobileNet ValueError: Error when checking target: expected dense_1 to have 4 dimensions, but got array with shape (24, 2)
[英]ValueError: Error when checking target: expected dense_35 to have 4 dimensions, but got array with shape (1157, 1)
我有以下形狀的訓練和測試圖像數據。
X_test.shape , y_test.shape , X_train.shape , y_train.shape
((277, 128, 128, 3), (277, 1), (1157, 128, 128, 3), (1157, 1))
我正在訓練模特
def baseline_model():
filters = 100
model = Sequential()
model.add(Conv2D(filters, (3, 3), input_shape=(128, 128, 3), padding='same', activation='relu'))
#model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(Conv2D(filters, (3, 3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
#model.add(Flatten())
model.add(Conv2D(filters, (3, 3), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(Conv2D(filters, (3, 3), activation='relu', padding='same'))
model.add(Activation('linear'))
model.add(BatchNormalization())
model.add(Dense(512, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
lrate = 0.01
epochs = 10
decay = lrate/epochs
sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
model.compile(loss='sparse_categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
print(model.summary())
return model
但我收到以下錯誤
檢查目標時出錯:預期density_35具有4維,但數組的形狀為(1157,1)
盡管density_35需要提供4維數據,但是根據錯誤,網絡將以2維數據作為標簽矢量。
您可能忘記做的一件事是在第一個Dense
層之前添加Flatten
層:
model.add(BatchNormalization())
model.add(Flatten()) # flatten the output of previous layer before feeding it to Dense layer
model.add(Dense(512, activation='relu'))
之所以需要它,是因為Dense
層不會使輸入變平。 而是將其應用於最后一個維度 。
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