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I am trying to load a Keras model and make predictions with it and run into a strange error. An minimal example is the following:

from keras import models
import numpy as np

model = models.load_model('model_4hiddenLayers_16unitsPerLayer_relu_learningRate0p0001.h5')
x = np.ones(36, dtype=float)
prediction = model.predict(x )

The model expects an input shape of (36,), which should be the shape of x, which I verified:

print('x.shape={}'.format(x.shape) )

gives :

x.shape=(36,)

However when running this code I get the following error message:

ValueError: Error when checking : expected batch_normalization_1_input to have shape (36,) but got array with shape (1,)

What am I missing here? Thanks for the help.

Some additional info : I am using keras version 2.1.4 with TensorFlow as backend.

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I figured out the issue. The "predict" function expects a batch of input arrays, so it expects x to have shape (n, 36) where n is the number of examples. After adding :

x = x.reshape( (1,36) )

the code works fine

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  • $\begingroup$ Is this to be added before building the model or before model based prediction only? $\endgroup$ – AAI Jan 4 at 1:09
  • $\begingroup$ This is for the model based prediction with one sample. But for training, the array you give similarly needs to be of shape (number_of_samples, number_of_features ), like the (n,36) above. $\endgroup$ – W. Verbeke Jan 5 at 11:21

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