I want to create a basic RNN that can add two bytes. Here are the input and outputs, which are expected of a simple addition
X = [[0, 0], [0, 1], [1, 1], [0, 1], [1, 0], [1, 0], [1, 1], [1, 0]]
That is, X1 = 00101111
and X2 = 01110010
Y = [1, 0, 1, 0, 0, 0, 0, 1]
I created the following sequential model
model = Sequential()
model.add(GRU(output_dim = 16, input_length = 2, input_dim = 8))
model.add(Activation('relu'`))
model.add(Dense(2, activation='softmax'))
model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
model.summary()
The error I get is something along
expected
lstm_input_1
to have 3 dimensions, but got array with shape(8L, 2L)
So if I increase the dimensions by changing X to
[[[0 0]] [[1 1]] [[1 1]] [[1 0]] [[0 0]] [[1 0]] [[0 1]] [[1 0]]]
Then the error changes to
expected
lstm_input_1
to have shape(None, 8, 2)
but got array with shape(8L, 1L, 2L)
In Keras the Sequential models expect an input of shape (batch_size, sequence_length, input_dimension)
. I suspect you need to change the two last dimensions of your input array. Remember, the batch dimension is not explicitly defined.
将X更改为[[[0, 0], [0, 1], [1, 1], [0, 1], [1, 0], [1, 0], [1, 1], [1, 0]]]
使其形状为(1, 8, 2)
Keras as input requiers 3D data, as stated in error. It is samples, time steps, features. Since you have (8L, 2L) Keras takes it as 2D - [samples, features]. In order to fix it, do something like this
def reshape_dataset(train):
trainX = numpy.reshape(train, (train.shape[0], 1, train.shape[1]))
return numpy.array(trainX)
x = reshape_dataset(your_dataset)
now X
should be 8L,1,2L
which is [samples, time steps, features] - 3D
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.