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I'm trying to visualize the output of each convolutional layer in keras, following this link: MNIST Visualisation. I have modified some layers to remove errors, but now I'm stuck with the Dense Layer Error.

np.set_printoptions(precision=5, suppress=True)
np.random.seed(1337) # for reproducibility

nb_classes = 10

# the data, shuffled and split between tran and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data("mnist.pkl")

X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
X_train = X_train.astype("float32")
X_test = X_test.astype("float32")
X_train /= 255
X_test /= 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 = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

i = 4600
pl.imshow(X_train[i, 0], interpolation='nearest', cmap=cm.binary)
print("label : ", Y_train[i,:])

model = Sequential()

model.add(Convolution2D(32, 3, 3, border_mode='same',input_shape = (1,28,28))) #changed border_mode from full -> valid
convout1 = Activation('relu')
model.add(convout1)
model.add(Convolution2D(32, 32, 3))

convout2 = Activation('relu')
model.add(convout2)
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(32*196, 128)) #ERROR HERE

Any comment or suggestion highly appreciated. Thank you.

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1 回答 1

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如果你检查一个Dense层的文档，你会注意到它接受的第一个参数是输出的形状，第二个是init描述层权重的启动方式。在您的情况下，您提供了int第二个位置参数，这导致了错误。您应该将代码更改为（假设您想要 128 维向量形式的输出）：

model.add(Dense(128))
于 2017-01-30T21:47:57.010 回答