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lstmtensorflow2.0dimensions

model.predict yield yhat of bad dimension

I reuse an old code who was perfectly functionnal 3 mount ago to train a new LSTM model on new set of data.

my data have these shapes:

X_train =  [21500, 5, 4]
y_train = [21500, 1]

trainning model seem to work well, it finish with a single

tensor_input = Input(shape=(x_train.shape[1], x_train.shape[2]), name='main_inputs')

xy = LSTM(6, activation='softsign',
          kernel_initializer=initializers.glorot_uniform(),
          return_sequences=False, stateful=False,
          name='Hlayer1')(tensor_input)

xy = Dropout(rate=.2)(xy)

Dense(y_train_shape[1], activation='linear')

but when I try to predict with

yhat = model.predict(x_test)
x_test = [1350,1]

I get

yhat = [1350, 5, 1]

I build my code this way since 5 years but first time I get this!??!

Solution

  • Never find the trouble, just reinstall Tensorflow and now it work!?