I want to train feature of size (10151, 1285) to lable (10151, 257), and I want to use way2. since in I want to use "feature_input" in the cost function. but it fails with error:
ValueError: Input 0 of layer batch_normalization_6 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 257).
I am wondering why?
Way1:
model = Sequential()
model.add(Dense(257, input_dim=1285))
model.add(BatchNormalization())
model.add(Activation('sigmoid'))
model.compile(optimizer='adam', loss='mse', metrics=['mse'])
model.fit(feature, label )
model.save("./model.hdf5")
Way2:
feature_input = Input(shape=(None, 1285))
dense = Dense(257)(feature_input)
norm = BatchNormalization()(dense)
out = Activation('sigmoid')(norm)
model = Model(feature_input, out)
model.compile(optimizer='adam', loss='mse', metrics=['mse'])
model.fit(feature, label )
model.save("./model.hdf5")
If you define the input shape as (None, 1285)
, the model recognizes the input as a 3-dimensional data. I guess the None
shape you entered was meant to describe the batch size, but when we compile the model we get a 3-dimensional input, and the batch dimension is automatically added. Therefore, you can use an input shape of (1285,)
as an alternative.
<Summary of your model>
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, None, 1285)] 0
_________________________________________________________________
dense (Dense) (None, None, 257) 330502
_________________________________________________________________
batch_normalization (BatchNo (None, None, 257) 1028
_________________________________________________________________
activation (Activation) (None, None, 257) 0
=================================================================
Total params: 331,530
Trainable params: 331,016
Non-trainable params: 514
_________________________________________________________________
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