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tensorflowkerasdeep-learningmlp

model.prediction() fails due to mismatch of shapes

I trained a simple MLP model using new tf.keras version 2.2.4-tf. Here is how the model look like:

input_layer = Input(batch_shape=(138, 28))
first_layer = Dense(30, activation=activation, name="first_dense_layer_1")(input_layer)
first_layer = Dropout(0.1)(first_layer, training=True)
second_layer = Dense(15, activation=activation, name="second_dense_layer")(first_layer)
out = Dense(1, name='output_layer')(second_layer)
model = Model(input_layer, out)

enter image description here

I'm getting an error when I try to do prediction prediction_result = model.predict(test_data, batch_size=138). The test_data has shape of (69, 28), so it is smaller than the batch_size which is 138. Here is the error, it seems like the issue comes from first dropout layer:

tensorflow.python.framework.errors_impl.InvalidArgumentError:  Incompatible shapes: [138,30] vs. [69,30]
     [[node model/dropout/dropout/mul_1 (defined at ./mlp_new_tf.py:471) ]] [Op:__inference_distributed_function_1700]

The same solution works with no issues in older version of keras (2.2.4) and tensorflow (1.12.0). How can I fix the issue? I don't have more data for test, so I can't change the test_data set to have more data points!

Solution

  • Since you are seeing the issue at prediction time, one way of getting around this would be to pad the test data to be a multiple of your batch size. It shouldn't slow down the prediction since the number of batches doesn't change. numpy.pad should do the trick.