Keras and Tensorflow with identical weights but different test accuracy -


i have trained tensorflow model , built equivalent keras model , loaded learned weights it. however, different test accuracy in keras (e.g. 98.5% in tensorflow compared 98%). have defined accuracy in tensorflow way:

accuracy = tf.cast(tf.equal(tf.argmax(input_labels, 1), tf.argmax(output, 1)), tf.float32)

in keras have used standard metric:

model.compile(optimizer=tf.train.adamoptimizer(), loss='categorical_crossentropy', metrics=['accuracy'])

accuracy = model.evaluate(x=test_images, y=test_labels, batch_size=batch_size)

i confused discrepancy comes from.

edit: since not convinced answer, spent couple of days more on code until discovered bug caused discrepancy, , after fixing it, both frameworks produce identical results same weights expected. writing edit prevent confusion other readers, stackoverflow not allow me delete question.

one possible reason: dropout layer in model.

according the source code, internal random logic implemented tf.random_uniform, randomly generate new value on each call sess.run().


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