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)
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!
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.