TENSORFLOW 找不到解決方案：ValueError: logits and labels must have the same shape ((None, 1) vs (None, 2, 2))

Question

我對 CNN 完全陌生，我正在創建一個用於圖像識別的 CNN。 我試圖為我的鍛煉調整貓與狗的結構，但出現了一個錯誤，我不知道如何解決它：

這是我的代碼：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

img_width, img_height = 64, 64
img_rows, img_cols = 64, 64

# Prepare data to feed the NN
num_classes = 2

# Ask keras which format to use depending on used backend and arrange data as expected
if K.image_data_format() == 'channels_first':
    X_train = x_train.reshape(X_train.shape[0], 3, img_rows, img_cols)
    X_test = x_test.reshape(X_test.shape[0], 3, img_rows, img_cols)
    input_shape = (3, img_width, img_height)
else:
    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 3)
    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 3)
    input_shape = (img_width, img_height, 3)

# Incoming data is in uint8. Cast the input data images to be floats in range [0.0-1.0]  
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255

print('x_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

img_width, img_height = 64, 64

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

batch_size = 100
epochs = 10

model.fit(X_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(X_test, y_test))

和錯誤：

ValueError：logits 和標簽必須具有相同的形狀 ((None, 1) vs (None, 2, 2))

非常感謝您提前:)

Answer 1

您應該刪除一次性對標簽進行編碼的行。

在行中：

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

您已經對值進行了一次熱編碼，使其形狀為(batch_size, 2, 2) ，但最后一層 (Dense) 輸出單個數字，即形狀(batch_size, 1) 。 binary_crossentropy還計算 logits 形狀的損失為(batch_size, 1)和標簽為(batch_size, 1) （對於您的數據集）。

binary_crossentropy 文檔