Question

我对CNN完全陌生，我创建了用于图像识别的CNN。我正在尝试根据我的锻炼适应猫对狗的结构，但是弹出了一个错误，而且我不知道如何解决它：

这是我的代码：

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))

错误：

ValueError：登录件和标签必须具有相同的形状（（无，1）与（无，2，2））

非常感谢您：）

Answer 1

您应该删除对标签一键编码的行。

在此行：

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

您已经对值进行了一次热编码，使其形状变为(batch_size, 2, 2)，但是最后一层（密集）输出一个数字，即形状为(batch_size, 1)。此外，binary_crossentropy将Logit形状的损失计算为(batch_size, 1)，标签的损失计算为(batch_size, 1)（对于数据集）。

binary_crossentropy documentation

TENSORFLOW找不到以下解决方案：ValueError：logits和标签的形状必须相同（（None，1）与（None，2，2））

1 个答案: