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

Question

Im completely new to CNN and Im creating a CNN for image recognition. Im trying to adapt the Cats vs dogs structure for my exercise but an error is popping up and I don't know how to solve it:

Here is my code:

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))

And the error:

ValueError: logits and labels must have the same shape ((None, 1) vs (None, 2, 2))

Thank you very much in advance:)

Answer 1

You should remove the lines where you one-hot encoded the labels.

In the line:

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

you have one-hot encoded the values making their shapes to (batch_size, 2, 2) , but the last layer (Dense) outputs a single number ie of shape (batch_size, 1) . Also binary_crossentropy calculates loss for shapes of logits as (batch_size, 1) and labels as (batch_size, 1) (for your dataset).

binary_crossentropy documentation